An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

PubMed

Fitzpatrick, J M; Roberts, D W; Patlewicz, G

2018-06-01

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

Predicting local field potentials with recurrent neural networks.

PubMed

Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

2016-08-01

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

Potential predictability of a Colombian river flow

NASA Astrophysics Data System (ADS)

Córdoba-Machado, Samir; Palomino-Lemus, Reiner; Quishpe-Vásquez, César; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In this study the predictability of an important Colombian river (Cauca) has been analysed based on the use of climatic variables as potential predictors. Cauca River is considered one of the most important rivers of Colombia because its basin supports important productive activities related with the agriculture, such as the production of coffee or sugar. Potential relationships between the Cauca River seasonal streamflow anomalies and different climatic variables such as sea surface temperature (SST), precipitation (Pt), temperature over land (Tm) and soil water (Sw) have been analysed for the period 1949-2009. For this end, moving correlation analysis of 30 years have been carried out for lags from one to four seasons for the global SST, and from one to two seasons for South America Pt, Tm and Sw. Also, the stability of the significant correlations have been also studied, identifying the regions used as potential predictors of streamflow. Finally, in order to establish a prediction scheme based on the previous stable correlations, a Principal Component Analysis (PCA) applied on the potential predictor regions has been carried out in order to obtain a representative time series for each predictor field. Significant and stable correlations between the seasonal streamflow and the tropical Pacific SST (El Niño region) are found for lags from one to four (one-year) season. Additionally, some regions in the Indian and Atlantic Oceans also show significant and stable correlations at different lags, highlighting the importance that exerts the Atlantic SST on the hydrology of Colombia. Also significant and stable correlations are found with the Pt, Tm and Sw for some regions over South America, at lags of one and two seasons. The prediction of Cauca seasonal streamflow based on this scheme shows an acceptable skill and represents a relative improvement compared with the predictability obtained using the teleconnection indices associated with El Niño. Keywords

Increasing potential predictability of Indian Summer monsoon active and break spells

NASA Astrophysics Data System (ADS)

Mani, N. J.; Goswami, B.

2009-12-01

An understanding of the limit on potential predictability is crucial for developing appropriate tools for extended range prediction of active/break spells of Indian summer monsoon (ISM). The global low frequency changes in climate modulate the annual cycle of the ISM and can influence the intrinsic predictability limit of the ISM intraseasonal oscillations (ISOs). Using 104 year (1901-2004) long daily rainfall data, the change in potential predictability of active and break spells are estimated by an empirical method. Using an ISO index based on 10-90 day filtered precipitation, Goswami and Xavier (2003)showed that the monsoon breaks are intrinsically more predictable (20-25 days) than the active conditions (10-15 days. In the present study, employing the same method in 15 year sliding windows, we found that the potential predictability of both active and break spells have undergone a rapid increase during the recent three decades. The potential predictability of active spells has shown an increase from 1 week to 2 weeks while that for break spells increased from 2 weeks to 3 weeks. This result is interesting and intriguing in the backdrop of recent finding that the potential predictability of monsoon weather has decreased substantially over the same period compared to earlier decades due to increased potential instability of the atmosphere. The possible role of internal dynamics and external forcing in producing this change has been explored. The variance among peak active/break conditions shows a steady decrease over the years, indicating a lesser event to event variability in the magnitude of ISO peak phases in recent years. The ISO predictability may be closely linked to the error energy cascading from the synoptic scales and the interaction between these scales. Computation of nonlinear kinetic energy exchange between synoptic and ISO scales in frequency domain, also support the notion of ineffectual influence of synoptic scale errors on the ISO scale

Potential predictability of Northern America surface temperature in AGCMs and CGCMs

NASA Astrophysics Data System (ADS)

Tang, Youmin; Chen, Dake; Yan, Xiaoqin

2015-07-01

In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier prediction by coupled models (T1), and a two-tier prediction by atmospheric models only (T2). Furthermore, the potential predictability was optimally decomposed into different modes for both T1 and T2, by extracting the most predictable structures. Emphasis was placed on the comparison of the predictability between T1 and T2. It was found that the potential predictability of the NA SAT is seasonal and spatially dependent in both T1 and T2. Higher predictability occurs in spring and winter and over the southeastern US and northwestern Canada. There is no significant difference of potential predictability between T1 and T2 for most areas of NA, although T1 has higher potential predictability than T2 in the southeastern US. Both T1 and T2 display similar most predictable components (PrCs) for the NA SAT, characterized by the inter-annual variability mode and the long-term trend mode. The first one is inherent to the tropical Pacific sea surface temperature forcing, such as the El Nino-Southern Oscillation, whereas the second one is closely associated with global warming. In general, the PrC modes can better characterize the predictability in T1 than in T2, in particular for the inter-annual variability mode in the fall. The prediction skill against observations is better measured by the PrC analysis than by principal component analysis for all seasons, indicating the stronger capability of PrCA in extracting prediction targets.

An entropy and viscosity corrected potential method for rotor performance prediction

NASA Technical Reports Server (NTRS)

Bridgeman, John O.; Strawn, Roger C.; Caradonna, Francis X.

1988-01-01

An unsteady Full-Potential Rotor code (FPR) has been enhanced with modifications directed at improving its drag prediction capability. The shock generated entropy has been included to provide solutions comparable to the Euler equations. A weakly interacted integral boundary layer has also been coupled to FPR in order to estimate skin-friction drag. Pressure distributions, shock positions, and drag comparisons are made with various data sets derived from two-dimensional airfoil, hovering, and advancing high speed rotor tests. In all these comparisons, the effect of the nonisentropic modification improves (i.e., weakens) the shock strength and wave drag. In addition, the boundary layer method yields reasonable estimates of skin-friction drag. Airfoil drag and hover torque data comparisons are excellent, as are predicted shock strength and positions for a high speed advancing rotor.

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

NASA Technical Reports Server (NTRS)

Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

2010-01-01

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Plant water potential improves prediction of empirical stomatal models.

PubMed

Anderegg, William R L; Wolf, Adam; Arango-Velez, Adriana; Choat, Brendan; Chmura, Daniel J; Jansen, Steven; Kolb, Thomas; Li, Shan; Meinzer, Frederick; Pita, Pilar; Resco de Dios, Víctor; Sperry, John S; Wolfe, Brett T; Pacala, Stephen

2017-01-01

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

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.

Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

PubMed

Meylan, William; Boethling, Robert; Aronson, Dallas; Howard, Philip; Tunkel, Jay

2007-09-01

Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

Regional Arctic sea-ice prediction: potential versus operational seasonal forecast skill

NASA Astrophysics Data System (ADS)

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

2018-06-01

Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea-ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system's OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981-2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea-ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

PubMed

Trisciuzzi, Daniela; Alberga, Domenico; Mansouri, Kamel; Judson, Richard; Novellino, Ettore; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio

2017-11-27

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

Modeling Seizure Self-Prediction: An E-Diary Study

PubMed Central

Haut, Sheryl R.; Hall, Charles B.; Borkowski, Thomas; Tennen, Howard; Lipton, Richard B.

2013-01-01

Purpose A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, “How likely are you to experience a seizure [time frame]”? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy. PMID:24111898

The Potential for Predicting Precipitation on Seasonal-to-Interannual Timescales

NASA Technical Reports Server (NTRS)

Koster, R. D.

1999-01-01

The ability to predict precipitation several months in advance would have a significant impact on water resource management. This talk provides an overview of a project aimed at developing this prediction capability. NASA's Seasonal-to-Interannual Prediction Project (NSIPP) will generate seasonal-to-interannual sea surface temperature predictions through detailed ocean circulation modeling and will then translate these SST forecasts into forecasts of continental precipitation through the application of an atmospheric general circulation model and a "SVAT"-type land surface model. As part of the process, ocean variables (e.g., height) and land variables (e.g., soil moisture) will be updated regularly via data assimilation. The overview will include a discussion of the variability inherent in such a modeling system and will provide some quantitative estimates of the absolute upper limits of seasonal-to-interannual precipitation predictability.

Potential Seasonal Predictability of Water Cycle in Observations and Reanalysis

NASA Astrophysics Data System (ADS)

Feng, X.; Houser, P.

2012-12-01

Identification of predictability of water cycle variability is crucial for climate prediction, water resources availability, ecosystem management and hazard mitigation. An analysis that can assess the potential skill in seasonal prediction was proposed by the authors, named as analysis of covariance (ANOCOVA). This method tests whether interannual variability of seasonal means exceeds that due to weather noise under the null hypothesis that seasonal means are identical every year. It has the advantage of taking into account autocorrelation structure in the daily time series but also accounting for the uncertainty of the estimated parameters in the significance test. During the past several years, multiple reanalysis datasets have become available for studying climate variability and understanding climate system. We are motivated to compare the potential predictability of water cycle variation from different reanalysis datasets against observations using the newly proposed ANOCOVA method. The selected eight reanalyses include the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) 40-year Reanalysis Project (NNRP), the National Centers for Environmental Prediction-Department of Energy (NCEP/DOE) Reanalysis Project (NDRP), the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year Reanalysis, The Japan Meteorological Agency 25-year Reanalysis Project (JRA25), the ECMWF) Interim Reanalysis (ERAINT), the NCEP Climate Forecast System Reanalysis (CFSR), the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the National Oceanic and Atmospheric Administration-Cooperative Institute for Research in Environmental Sciences (NOAA/CIRES) 20th Century Reanalysis Version 2 (20CR). For key water cycle components, precipitation and evaporation, all reanalyses consistently show high fraction of predictable variance in the tropics, low

Academic performance, career potential, creativity, and job performance: can one construct predict them all?

PubMed

Kuncel, Nathan R; Hezlett, Sarah A; Ones, Deniz S

2004-01-01

This meta-analysis addresses the question of whether 1 general cognitive ability measure developed for predicting academic performance is valid for predicting performance in both educational and work domains. The validity of the Miller Analogies Test (MAT; W. S. Miller, 1960) for predicting 18 academic and work-related criteria was examined. MAT correlations with other cognitive tests (e.g., Raven's Matrices [J. C. Raven, 1965]; Graduate Record Examinations) also were meta-analyzed. The results indicate that the abilities measured by the MAT are shared with other cognitive ability instruments and that these abilities are generalizably valid predictors of academic and vocational criteria, as well as evaluations of career potential and creativity. These findings contradict the notion that intelligence at work is wholly different from intelligence at school, extending the voluminous literature that supports the broad importance of general cognitive ability (g).

Mechanism-Based Classification of PAH Mixtures to Predict Carcinogenic Potential.

PubMed

Tilton, Susan C; Siddens, Lisbeth K; Krueger, Sharon K; Larkin, Andrew J; Löhr, Christiane V; Williams, David E; Baird, William M; Waters, Katrina M

2015-07-01

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC > BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P < .05) for DNA damage, apoptosis, response to chemical stimulus, and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Inter-decadal change in potential predictability of the East Asian summer monsoon

NASA Astrophysics Data System (ADS)

Li, Jiao; Ding, Ruiqiang; Wu, Zhiwei; Zhong, Quanjia; Li, Baosheng; Li, Jianping

2018-05-01

The significant inter-decadal change in potential predictability of the East Asian summer monsoon (EASM) has been investigated using the signal-to-noise ratio method. The relatively low potential predictability appears from the early 1950s through the late 1970s and during the early 2000s, whereas the potential predictability is relatively high from the early 1980s through the late 1990s. The inter-decadal change in potential predictability of the EASM can be attributed mainly to variations in the external signal of the EASM. The latter is mostly caused by the El Niño-Southern Oscillation (ENSO) inter-decadal variability. As a major external signal of the EASM, the ENSO inter-decadal variability experiences phase transitions from negative to positive phases in the late 1970s, and to negative phases in the late 1990s. Additionally, ENSO is generally strong (weak) during a positive (negative) phase of the ENSO inter-decadal variability. The strong ENSO is expected to have a greater influence on the EASM, and vice versa. As a result, the potential predictability of the EASM tends to be high (low) during a positive (negative) phase of the ENSO inter-decadal variability. Furthermore, a suite of Pacific Pacemaker experiments suggests that the ENSO inter-decadal variability may be a key pacemaker of the inter-decadal change in potential predictability of the EASM.

Action prediction based on anticipatory brain potentials during simulated driving.

PubMed

Khaliliardali, Zahra; Chavarriaga, Ricardo; Gheorghe, Lucian Andrei; Millán, José del R

2015-12-01

The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

Action prediction based on anticipatory brain potentials during simulated driving

NASA Astrophysics Data System (ADS)

Khaliliardali, Zahra; Chavarriaga, Ricardo; Gheorghe, Lucian Andrei; Millán, José del R.

2015-12-01

Objective. The ability of an automobile to infer the driver’s upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver’s intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. Approach. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by ‘Start’/‘Stop’ cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. Main results. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a ‘Red’ traffic light) versus events that do not require such action (No-go condition; e.g. a ‘Yellow’ light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. Significance. We show for the first time the feasibility of predicting the driver’s intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials

NASA Astrophysics Data System (ADS)

Vlasiuk, Maryna; Sadus, Richard J.

2017-06-01

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials.

PubMed

Vlasiuk, Maryna; Sadus, Richard J

2017-06-28

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Potentiality Prediction of Electric Power Replacement Based on Power Market Development Strategy

NASA Astrophysics Data System (ADS)

Miao, Bo; Yang, Shuo; Liu, Qiang; Lin, Jingyi; Zhao, Le; Liu, Chang; Li, Bin

2017-05-01

The application of electric power replacement plays an important role in promoting the development of energy conservation and emission reduction in our country. To exploit the potentiality of regional electric power replacement, the regional GDP (gross domestic product) and energy consumption are taken as potentiality evaluation indicators. The principal component factors are extracted with PCA (principal component analysis), and the integral potentiality analysis is made to the potentiality of electric power replacement in the national various regions; a region is taken as a research object, and the potentiality of electric power replacement is defined and quantified. The analytical model for the potentiality of multi-scenario electric power replacement is developed, and prediction is made to the energy consumption with the grey prediction model. The relevant theoretical research is utilized to realize prediction analysis on the potentiality amount of multi-scenario electric power replacement.

E-waste Management and Refurbishment Prediction (EMARP) Model for Refurbishment Industries.

PubMed

Resmi, N G; Fasila, K A

2017-10-01

This paper proposes a novel algorithm for establishing a standard methodology to manage and refurbish e-waste called E-waste Management And Refurbishment Prediction (EMARP), which can be adapted by refurbishing industries in order to improve their performance. Waste management, particularly, e-waste management is a serious issue nowadays. Computerization has been into waste management in different ways. Much of the computerization has happened in planning the waste collection, recycling and disposal process and also managing documents and reports related to waste management. This paper proposes a computerized model to make predictions for e-waste refurbishment. All possibilities for reusing the common components among the collected e-waste samples are predicted, thus minimizing the wastage. Simulation of the model has been done to analyse the accuracy in the predictions made by the system. The model can be scaled to accommodate the real-world scenario. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification of informative features for predicting proinflammatory potentials of engine exhausts.

PubMed

Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

2017-08-18

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

Predicting Subsequent Service Potential for Former Army Prisoners

DTIC Science & Technology

1983-03-25

Esteem Scale, and Hudson’s (1974) Index of Self - Esteem , fou9d significant d_ e.re.e beteen-gr duates-and-non-gj The results of the two studies are...a greater need for autonomy, on the EPPS, and less self - esteem , on the Rosenberg (1965) scale. 400 Table 1 Predicting Graduation/Discharge from...Acceptance (CPI) .588 Education Completed .410 Socialization (CPI) -.445 Highest Pay Grade -.388 Social Presence (CPI) -.443 Marital Status .388 Self - Esteem (Rosenberg

Potential Predictability and Prediction Skill for Southern Peru Summertime Rainfall

NASA Astrophysics Data System (ADS)

WU, S.; Notaro, M.; Vavrus, S. J.; Mortensen, E.; Block, P. J.; Montgomery, R. J.; De Pierola, J. N.; Sanchez, C.

2016-12-01

The central Andes receive over 50% of annual climatological rainfall during the short period of January-March. This summertime rainfall exhibits strong interannual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. An improved seasonal prediction skill of summertime rainfall would aid in water resource planning and allocation across the water-limited southern Peru. While various underlying mechanisms have been proposed by past studies for the drivers of interannual variability in summertime rainfall across southern Peru, such as the El Niño-Southern Oscillation (ENSO), Madden Julian Oscillation (MJO), and extratropical forcings, operational forecasts continue to be largely based on rudimentary ENSO-based indices, such as NINO3.4, justifying further exploration of predictive skill. In order to bridge this gap between the understanding of driving mechanisms and the operational forecast, we performed systematic studies on the predictability and prediction skill of southern Peru summertime rainfall by constructing statistical forecast models using best available weather station and reanalysis datasets. At first, by assuming the first two empirical orthogonal functions (EOFs) of summertime rainfall are predictable, the potential predictability skill was evaluated for southern Peru. Then, we constructed a simple regression model, based on the time series of tropical Pacific sea-surface temperatures (SSTs), and a more advanced Linear Inverse Model (LIM), based on the EOFs of tropical ocean SSTs and large-scale atmosphere variables from reanalysis. Our results show that the LIM model consistently outperforms the more rudimentary regression models on the forecast skill of domain averaged precipitation index and individual station indices. The improvement of forecast correlation skill ranges from 10% to over 200% for different

Inroads to predict in vivo toxicology-an introduction to the eTOX Project.

PubMed

Briggs, Katharine; Cases, Montserrat; Heard, David J; Pastor, Manuel; Pognan, François; Sanz, Ferran; Schwab, Christof H; Steger-Hartmann, Thomas; Sutter, Andreas; Watson, David K; Wichard, Jörg D

2012-01-01

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison ("read-across"), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX ("electronic toxicity") consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Treesearch

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

Impact of predictive scoring model and e-mail messages on African American blood donors.

PubMed

Bachegowda, Lohith S; Timm, Brad; Dasgupta, Pinaki; Hillyer, Christopher D; Kessler, Debra; Rebosa, Mark; France, Christopher R; Shaz, Beth H

2017-06-01

Expanding the African American (AA) donor pool is critical to sustain transfusion support for sickle cell disease patients. The aims were to: 1) apply cognitive computing on donation related metrics to develop a predictive model that effectively identifies repeat AA donors, 2) determine whether a single e-mail communication could improve AA donor retention and compare retention results on higher versus lower predictive score donors, and 3) evaluate the effect of e-mail marketing on AA donor retention with culturally versus nonculturally tailored message. Between 2011 and 2012, 30,786 AA donors donated blood at least once on whom predictive repeat donor scores (PRDSs) was generated from donor-related metrics (frequency of donations, duration between donations, age, blood type, and sex). In 2013, 28% (8657/30,786) of 2011 to 2012 donors returned to donate on whom PRDS was validated. Returning blood donors had a higher mean PRDS compared to nonreturning donors (0.649 vs. 0.268; p < 0.001). In the e-mail pilot, high PRDS (≥0.6) compared to low PRDS (<0.6) was associated with 89% higher donor presentation rate (p < 0.001), 20% higher e-mail opening rate (p < 0.001), and, specifically among those who opened the e-mail, 159% higher presentation rate (p < 0.001). Finally, blood donation rate did not differ (p = 0.79) as a function of generic (n = 9312, 1.4%) versus culturally tailored (n = 9326, 1.3%) message. Computational algorithms utilizing readily available donor metrics can identify highly committed AA donors and in conjunction with targeted e-mail communication has the potential to increase the efficiency of donor marketing. © 2017 AABB.

Prediction markets and their potential role in biomedical research--a review.

PubMed

Pfeiffer, Thomas; Almenberg, Johan

2010-01-01

Predictions markets are marketplaces for trading contracts with payoffs that depend on the outcome of future events. Popular examples are markets on the outcome of presidential elections, where contracts pay $1 if a specific candidate wins the election and $0 if someone else wins. Contract prices on prediction markets can be interpreted as forecasts regarding the outcome of future events. Further attractive properties include the potential to aggregate private information, to generate and disseminate a consensus among the market participants, and to offer incentives for the acquisition of information. It has been argued that these properties might be valuable in the context of scientific research. In this review, we give an overview of key properties of prediction markets and discuss potential benefits for science. To illustrate these benefits for biomedical research, we discuss an example application in the context of decision making in research on the genetics of diseases. Moreover, some potential practical problems of prediction market application in science are discussed, and solutions are outlined. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Development potential of e-waste recycling industry in China.

PubMed

Li, Jinhui; Yang, Jie; Liu, Lili

2015-06-01

Waste electrical and electronic equipment (WEEE or e-waste) recycling industries in China have been through several phases from spontaneous informal family workshops to qualified enterprises with treatment fund. This study attempts to analyse the development potential of the e-waste recycling industry in China from the perspective of both time and scale potential. An estimation and forecast of e-waste quantities in China shows that, the total e-waste amount reached approximately 5.5 million tonnes in 2013, with 83% of air conditioners, refrigerators, washing machines, televisions sand computers. The total quantity is expected to reach ca. 11.7 million tonnes in 2020 and 20 million tonnes in 2040, which indicates a large increase potential. Moreover, the demand for recycling processing facilities, the optimal service radius of e-waste recycling enterprises and estimation of the profitability potential of the e-waste recycling industry were analysed. Results show that, based on the e-waste collection demand, e-waste recycling enterprises therefore have a huge development potential in terms of both quantity and processing capacity, with 144 and 167 e-waste recycling facilities needed, respectively, by 2020 and 2040. In the case that e-waste recycling enterprises set up their own collection points to reduce the collection cost, the optimal collection service radius is estimated to be in the range of 173 km to 239 km. With an e-waste treatment fund subsidy, the e-waste recycling industry has a small economic profit, for example ca. US$2.5/unit for television. The annual profit for the e-waste recycling industry overall was about 90 million dollars in 2013. © The Author(s) 2015.

Chemical Potentials, Activity Coefficients, and Solubility in Aqueous NaCl Solutions: Prediction by Polarizable Force Fields.

PubMed

Moučka, Filip; Nezbeda, Ivo; Smith, William R

2015-04-14

We describe a computationally efficient molecular simulation methodology for calculating the concentration dependence of the chemical potentials of both solute and solvent in aqueous electrolyte solutions, based on simulations of the salt chemical potential alone. We use our approach to study the predictions for aqueous NaCl solutions at ambient conditions of these properties by the recently developed polarizable force fields (FFs) AH/BK3 of Kiss and Baranyai (J. Chem. Phys. 2013, 138, 204507) and AH/SWM4-DP of Lamoureux and Roux (J. Phys. Chem. B 2006, 110, 3308 - 3322) and by the nonpolarizable JC FF of Joung and Cheatham tailored to SPC/E water (J. Phys. Chem. B 2008, 112, 9020 - 9041). We also consider their predictions of the concentration dependence of the electrolyte activity coefficient, the crystalline solid chemical potential, the electrolyte solubility, and the solution specific volume. We first highlight the disagreement in the literature concerning calculations of solubility by means of molecular simulation in the case of the JC FF and provide strong evidence of the correctness of our methodology based on recent independently obtained results for this important test case. We then compare the predictions of the three FFs with each other and with experiment and draw conclusions concerning their relative merits, with particular emphasis on the salt chemical potential and activity coefficient vs concentration curves and their derivatives. The latter curves have only previously been available from Kirkwood-Buff integrals, which require approximate numerical integrations over system pair correlation functions at each concentration. Unlike the case of the other FFs, the AH/BK3 curves are nearly parallel to the corresponding experimental curves at moderate and higher concentrations. This leads to an excellent prediction of the water chemical potential via the Gibbs-Duhem equation and enables the activity coefficient curve to be brought into excellent agreement

Predicting Reduction Rates of Energetic Nitroaromatic Compounds Using Calculated One-Electron Reduction Potentials

DOE PAGES

Salter-Blanc, Alexandra; Bylaska, Eric J.; Johnston, Hayley; ...

2015-02-11

The evaluation of new energetic nitroaromatic compounds (NACs) for use in green munitions formulations requires models that can predict their environmental fate. The susceptibility of energetic NACs to nitro reduction might be predicted from correlations between rate constants (k) for this reaction and one-electron reduction potentials (E1NAC) / 0.059 V, but the mechanistic implications of such correlations are inconsistent with evidence from other methods. To address this inconsistency, we have reevaluated existing kinetic data using a (non-linear) free-energy relationship (FER) based on the Marcus theory of outer-sphere electron transfer. For most reductants, the results are inconsistent with rate limitation bymore » an initial, outer-sphere electron transfer, suggesting that the strong correlation between k and E1NAC is justified only as an empirical model. This empirical correlation was used to calibrate a new quantitative structure-activity relationship (QSAR) using previously reported values of k for non-energetic NAC reduction by Fe(II) porphyrin and newly reported values of E1NAC determined using density functional theory at the B3LYP/6-311++G(2d,2p) level with the COSMO solvation model. The QSAR was then validated for energetic NACs using newly measured kinetic data for 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), and 2,4-dinitroanisole (DNAN). The data show close agreement with the QSAR, supporting its applicability to energetic NACs.« less

Seasonal Extratropical Storm Activity Potential Predictability and its Origins during the Cold Seasons

NASA Astrophysics Data System (ADS)

Pingree-Shippee, K. A.; Zwiers, F. W.; Atkinson, D. E.

2016-12-01

Extratropical cyclones (ETCs) often produce extreme hazardous weather conditions, such as high winds, blizzard conditions, heavy precipitation, and flooding, all of which can have detrimental socio-economic impacts. The North American east and west coastal regions are both strongly influenced by ETCs and, subsequently, land-based, coastal, and maritime economic sectors in Canada and the USA all experience strong adverse impacts from extratropical storm activity from time to time. Society would benefit if risks associated with ETCs and storm activity variability could be reliably predicted for the upcoming season. Skillful prediction would enable affected sectors to better anticipate, prepare for, manage, and respond to storm activity variability and the associated risks and impacts. In this study, the potential predictability of seasonal variations in extratropical storm activity is investigated using analysis of variance to provide quantitative and geographical observational evidence indicative of whether it may be possible to predict storm activity on the seasonal timescale. This investigation will also identify origins of the potential predictability using composite analysis and large-scale teleconnections (Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation), providing the basis upon which seasonal predictions can be developed. Seasonal potential predictability and its origins are investigated for the cold seasons (OND, NDJ, DJF, JFM) during the 1979-2015 time period using daily mean sea level pressure, absolute pressure tendency, and 10-m wind speed from the ECMWF ERA-Interim reanalysis as proxies for extratropical storm activity. Results indicate potential predictability of seasonal variations in storm activity in areas strongly influenced by ETCs and with origins in the investigated teleconnections. For instance, the North Pacific storm track has considerable potential predictability and with notable origins in the SO and PDO.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

PubMed

Chen, Xi; Lu, Fang; Jiang, Lu-di; Cai, Yi-Lian; Li, Gong-Yu; Zhang, Yan-Ling

2016-07-01

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use. Copyright© by the Chinese Pharmaceutical Association.

Specific IgE for Fag e 3 Predicts Oral Buckwheat Food Challenge Test Results and Anaphylaxis: A Pilot Study.

PubMed

Yanagida, Noriyuki; Sato, Sakura; Maruyama, Nobuyuki; Takahashi, Kyohei; Nagakura, Ken-Ichi; Ogura, Kiyotake; Asaumi, Tomoyuki; Ebisawa, Motohiro

2018-01-01

Buckwheat (BW) is the source of a life-threatening allergen. Fag e 3-specific serum IgE (sIgE) is more useful than BW-sIgE for diagnosis; however, it is unknown whether Fag e 3-sIgE can predict oral food challenge (OFC) results and anaphylaxis. This study aimed to clarify the efficacy of Fag e 3-sIgE in predicting OFC results and anaphylaxis. We conducted a retrospective review of BW- and Fag e 3-sIgE data obtained using the ImmunoCAP® assay system and fluorescent enzyme-linked immunosorbent assay from children who underwent OFC using 3,072 mg of BW protein between July 2006 and March 2014 at Sagamihara National Hospital, Kanagawa, Japan. We analyzed 60 patients aged 1.9-13.4 years (median 6.0 years); 20 (33%) showed objective symptoms upon BW OFC. The patients without symptoms had significantly lower Fag e 3-sIgE than those with non-anaphylactic (p < 0.001) and anaphylactic reactions to BW (p = 0.004). Fag e 3-sIgE was the only tested factor that significantly predicted positive OFC results (odds ratio 8.93, 95% confidence interval 3.10-25.73, p < 0.001) and OFC-induced anaphylaxis (2.67, 1.12-6.35, p = 0.027). We suggest that a threshold Fag e 3-sIgE level of 18.0 kUE/L has 95% probability of provoking a positive reaction to BW. Fag e 3-sIgE predicted OFC results and OFC-induced anaphylaxis. We further emphasize paying careful attention to the risk of BW OFC-induced anaphylaxis. © 2018 The Author(s) Published by S. Karger AG, Basel.

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

PubMed Central

Junaid, Muhammad; Kaushik, Aman Chandra; Ali, Arif; Ali, Syed Shujait; Mehmood, Aamir; Wei, Dong-Qing

2018-01-01

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections. PMID:29715318

Phenology prediction component of GypsES

Treesearch

Jesse A. Logan; Lukas P. Schaub; F. William Ravlin

1991-01-01

Prediction of phenology is an important component of most pest management programs, and considerable research effort has been expended toward development of predictive tools for gypsy moth phenology. Although phenological prediction is potentially valuable for timing of spray applications (e.g. Bt, or Gypcheck) and other management activities (e.g. placement and...

Prediction of potential disease-associated microRNAs based on random walk.

PubMed

Xuan, Ping; Han, Ke; Guo, Yahong; Li, Jin; Li, Xia; Zhong, Yingli; Zhang, Zhaogong; Ding, Jian

2015-06-01

without any known related miRNAs, we extend the walking on a miRNA-disease bilayer network. During the prediction process, the similarity between diseases, the similarity between miRNAs, the known miRNA-disease associations and the topology information of the bilayer network are exploited. Moreover, the importance of information from different layers of network is considered. Our method achieves superior performance for 18 human diseases with AUC values ranging from 0.786 to 0.945. Moreover, case studies on breast neoplasms, lung neoplasms, prostatic neoplasms and 32 diseases further confirm the ability of our method to discover potential disease miRNAs. A web service for the prediction and analysis of disease miRNAs is available at http://bioinfolab.stx.hk/midp/. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Defensive motivation and attention in anticipation of different types of predictable and unpredictable threat: A startle and event-related potential investigation.

PubMed

Nelson, Brady D; Hajcak, Greg

2017-08-01

Predictability is an important characteristic of threat that impacts defensive motivation and attentional engagement. Supporting research has primarily focused on actual threat (e.g., shocks), and it is unclear whether the predictability of less intense threat (e.g., unpleasant pictures) similarly affects motivation and attention. The present study utilized a within-subject design and examined defensive motivation (startle reflex and self-reported anxiety) and attention (probe N100 and P300) in anticipation of shocks and unpleasant pictures during a no, predictable, and unpredictable threat task. This study also examined the impact of predictability on the P300 to shocks and late positive potential (LPP) to unpleasant pictures. The startle reflex and self-reported anxiety were increased in anticipation of both types of threat relative to no threat. Furthermore, startle potentiation in anticipation of unpredictable threat was greater for shocks compared to unpleasant pictures, but there was no difference for predictable threat. The probe N100 was enhanced in anticipation of unpredictable threat relative to predictable threat and no threat, and the probe P300 was suppressed in anticipation of predictable and unpredictable threat relative to no threat. These effects did not differ between the shock and unpleasant picture trials. Finally, the P300 and early LPP component were increased in response to unpredictable relative to predictable shocks and unpleasant pictures, respectively. The present study suggests that the unpredictability of unpleasant pictures increases defensive motivation, but to a lesser degree relative to actual threat. Moreover, unpredictability enhances attentional engagement in anticipation of, and in reaction to, both types of threat. © 2017 Society for Psychophysiological Research.

Ethanol-induced alcohol dehydrogenase E (AdhE) potentiates pneumolysin in Streptococcus pneumoniae.

PubMed

Luong, Truc Thanh; Kim, Eun-Hye; Bak, Jong Phil; Nguyen, Cuong Thach; Choi, Sangdun; Briles, David E; Pyo, Suhkneung; Rhee, Dong-Kwon

2015-01-01

Alcohol impairs the host immune system, rendering the host more vulnerable to infection. Therefore, alcoholics are at increased risk of acquiring serious bacterial infections caused by Streptococcus pneumoniae, including pneumonia. Nevertheless, how alcohol affects pneumococcal virulence remains unclear. Here, we showed that the S. pneumoniae type 2 D39 strain is ethanol tolerant and that alcohol upregulates alcohol dehydrogenase E (AdhE) and potentiates pneumolysin (Ply). Hemolytic activity, colonization, and virulence of S. pneumoniae, as well as host cell myeloperoxidase activity, proinflammatory cytokine secretion, and inflammation, were significantly attenuated in adhE mutant bacteria (ΔadhE strain) compared to D39 wild-type bacteria. Therefore, AdhE might act as a pneumococcal virulence factor. Moreover, in the presence of ethanol, S. pneumoniae AdhE produced acetaldehyde and NADH, which subsequently led Rex (redox-sensing transcriptional repressor) to dissociate from the adhE promoter. An increase in AdhE level under the ethanol condition conferred an increase in Ply and H2O2 levels. Consistently, S. pneumoniae D39 caused higher cytotoxicity to RAW 264.7 cells than the ΔadhE strain under the ethanol stress condition, and ethanol-fed mice (alcoholic mice) were more susceptible to infection with the D39 wild-type bacteria than with the ΔadhE strain. Taken together, these data indicate that AdhE increases Ply under the ethanol stress condition, thus potentiating pneumococcal virulence. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

In silico prediction of potential chemical reactions mediated by human enzymes.

PubMed

Yu, Myeong-Sang; Lee, Hyang-Mi; Park, Aaron; Park, Chungoo; Ceong, Hyithaek; Rhee, Ki-Hyeong; Na, Dokyun

2018-06-13

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

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

PubMed Central

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

2015-01-01

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

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding.

PubMed

Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N; Friggens, N C

2012-06-01

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment

Enzymatic Processes to Unlock the Lignin Value

PubMed Central

Hämäläinen, Veera; Grönroos, Toni; Suonpää, Anu; Heikkilä, Matti Wilhem; Romein, Bastiaan; Ihalainen, Petri; Malandra, Sara; Birikh, Klara R.

2018-01-01

Main hurdles of lignin valorization are its diverse chemical composition, recalcitrance, and poor solubility due to high-molecular weight and branched structure. Controlled fragmentation of lignin could lead to its use in higher value products such as binders, coatings, fillers, etc. Oxidative enzymes (i.e., laccases and peroxidases) have long been proposed as a potentially promising tool in lignin depolymerization. However, their application was limited to ambient pH, where lignin is poorly soluble in water. A Finnish biotechnology company, MetGen Oy, that designs and supplies industrial enzymes, has developed and brought to market several lignin oxidizing enzymes, including an extremely alkaline lignin oxidase MetZyme® LIGNO™, a genetically engineered laccase of bacterial origin. This enzyme can function at pH values as high as 10–11 and at elevated temperatures, addressing lignin at its soluble state. In this article, main characteristics of this enzyme as well as its action on bulk lignin coming from an industrial process are demonstrated. Lignin modification by MetZyme® LIGNO™ was characterized by size exclusion chromatography, UV spectroscopy, and dynamic light scattering for monitoring particle size of solubilized lignin. Under highly alkaline conditions, laccase treatment not only decreased molecular weight of lignin but also increased its solubility in water and altered its dispersion properties. Importantly, organic solvent-free soluble lignin fragmentation allowed for robust industrially relevant membrane separation technologies to be applicable for product fractionation. These enzyme-based solutions open new opportunities for biorefinery lignin valorization thus paving the way for economically viable biorefinery business. PMID:29623274

Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks.

PubMed

Yi, Faliu; Yang, Lin; Wang, Shidan; Guo, Lei; Huang, Chenglong; Xie, Yang; Xiao, Guanghua

2018-02-27

Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases, angiogenic analysis is based on the measurement of microvessel density (MVD) detected by immunostaining of CD31 or CD34. However, most retrievable public data is generally composed of Hematoxylin and Eosin (H&E)-stained pathology images, for which is difficult to get the corresponding immunohistochemistry images. The role of microvessels in H&E stained images has not been widely studied due to their complexity and heterogeneity. Furthermore, identifying microvessels manually for study is a labor-intensive task for pathologists, with high inter- and intra-observer variation. Therefore, it is important to develop automated microvessel-detection algorithms in H&E stained pathology images for clinical association analysis. In this paper, we propose a microvessel prediction method using fully convolutional neural networks. The feasibility of our proposed algorithm is demonstrated through experimental results on H&E stained images. Furthermore, the identified microvessel features were significantly associated with the patient clinical outcomes. This is the first study to develop an algorithm for automated microvessel detection in H&E stained pathology images.

Exploring the Potential of Predictive Analytics and Big Data in Emergency Care.

PubMed

Janke, Alexander T; Overbeek, Daniel L; Kocher, Keith E; Levy, Phillip D

2016-02-01

Clinical research often focuses on resource-intensive causal inference, whereas the potential of predictive analytics with constantly increasing big data sources remains largely unexplored. Basic prediction, divorced from causal inference, is much easier with big data. Emergency care may benefit from this simpler application of big data. Historically, predictive analytics have played an important role in emergency care as simple heuristics for risk stratification. These tools generally follow a standard approach: parsimonious criteria, easy computability, and independent validation with distinct populations. Simplicity in a prediction tool is valuable, but technological advances make it no longer a necessity. Emergency care could benefit from clinical predictions built using data science tools with abundant potential input variables available in electronic medical records. Patients' risks could be stratified more precisely with large pools of data and lower resource requirements for comparing each clinical encounter to those that came before it, benefiting clinical decisionmaking and health systems operations. The largest value of predictive analytics comes early in the clinical encounter, in which diagnostic and prognostic uncertainty are high and resource-committing decisions need to be made. We propose an agenda for widening the application of predictive analytics in emergency care. Throughout, we express cautious optimism because there are myriad challenges related to database infrastructure, practitioner uptake, and patient acceptance. The quality of routinely compiled clinical data will remain an important limitation. Complementing big data sources with prospective data may be necessary if predictive analytics are to achieve their full potential to improve care quality in the emergency department. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Prediction model of sinoatrial node field potential using high order partial least squares.

PubMed

Feng, Yu; Cao, Hui; Zhang, Yanbin

2015-01-01

High order partial least squares (HOPLS) is a novel data processing method. It is highly suitable for building prediction model which has tensor input and output. The objective of this study is to build a prediction model of the relationship between sinoatrial node field potential and high glucose using HOPLS. The three sub-signals of the sinoatrial node field potential made up the model's input. The concentration and the actuation duration of high glucose made up the model's output. The results showed that on the premise of predicting two dimensional variables, HOPLS had the same predictive ability and a lower dispersion degree compared with partial least squares (PLS).

Personal contextual characteristics and cognitions: predicting child abuse potential and disciplinary style.

PubMed

Rodriguez, Christina M

2010-02-01

According to Social Information Processing theory, parents' cognitive processes influence their decisions to engage in physical maltreatment, although cognitions occur in the context of other aspects of the parents' life. The present study investigated whether cognitive processes (external locus of control, inappropriate developmental expectations) predicted child abuse potential and overreactive disciplinary style beyond personal contextual factors characteristic of the parent (hostility, stress, and coping). 363 parents were recruited online. Results highlight the relative importance of the contextual characteristics (particularly stress, avoidant coping, and irritability) relative to cognitive processes in predicting abuse potential and overreactive discipline strategies, although an external locus of control also significantly contributed. Findings do not support that parents' developmental expectations uniquely predict elevated abuse risk. Results indicate stressed parents who utilize avoidance coping strategies are more likely to use overreactive discipline and report increased abuse potential. Findings are discussed with regard to implications for prevention/intervention efforts.

Health literacy and global cognitive function predict e-mail but not internet use in heart failure patients.

PubMed

Schprechman, Jared P; Gathright, Emily C; Goldstein, Carly M; Guerini, Kate A; Dolansky, Mary A; Redle, Joseph; Hughes, Joel W

2013-01-01

Background. The internet offers a potential for improving patient knowledge, and e-mail may be used in patient communication with providers. However, barriers to internet and e-mail use, such as low health literacy and cognitive impairment, may prevent patients from using technological resources. Purpose. We investigated whether health literacy, heart failure knowledge, and cognitive function were related to internet and e-mail use in older adults with heart failure (HF). Methods. Older adults (N = 119) with heart failure (69.84 ± 9.09 years) completed measures of health literacy, heart failure knowledge, cognitive functioning, and internet use in a cross-sectional study. Results. Internet and e-mail use were reported in 78.2% and 71.4% of this sample of patients with HF, respectively. Controlling for age and education, logistic regression analyses indicated that higher health literacy predicted e-mail (P < .05) but not internet use. Global cognitive function predicted e-mail (P < .05) but not internet use. Only 45% used the Internet to obtain information on HF and internet use was not associated with greater HF knowledge. Conclusions. The majority of HF patients use the internet and e-mail, but poor health literacy and cognitive impairment may prevent some patients from accessing these resources. Future studies that examine specific internet and email interventions to increase HF knowledge are needed.

Predicting the global warming potential of agro-ecosystems

NASA Astrophysics Data System (ADS)

Lehuger, S.; Gabrielle, B.; Larmanou, E.; Laville, P.; Cellier, P.; Loubet, B.

2007-04-01

Nitrous oxide, carbon dioxide and methane are the main biogenic greenhouse gases (GHG) contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate thus requires a capacity to predict the net exchanges of these gases in an integrated manner, as related to environmental conditions and crop management. Here, we used two year-round data sets from two intensively-monitored cropping systems in northern France to test the ability of the biophysical crop model CERES-EGC to simulate GHG exchanges at the plot-scale. The experiments involved maize and rapeseed crops on a loam and rendzina soils, respectively. The model was subsequently extrapolated to predict CO2 and N2O fluxes over an entire crop rotation. Indirect emissions (IE) arising from the production of agricultural inputs and from cropping operations were also added to the final GWP. One experimental site (involving a wheat-maize-barley rotation on a loamy soil) was a net source of GHG with a GWP of 350 kg CO2-C eq ha-1 yr-1, of which 75% were due to IE and 25% to direct N2O emissions. The other site (involving an oilseed rape-wheat-barley rotation on a rendzina) was a net sink of GHG for -250 kg CO2-C eq ha-1 yr-1, mainly due to a higher predicted C sequestration potential and C return from crops. Such modelling approach makes it possible to test various agronomic management scenarios, in order to design productive agro-ecosystems with low global warming impact.

Molecular cloning, expression, IgE binding activities and in silico epitope prediction of Per a 9 allergens of the American cockroach

PubMed Central

Yang, Haiwei; Chen, Hao; Jin, Min; Xie, Hua; He, Shaoheng; Wei, Ji-Fu

2016-01-01

Per a 9 is a major allergen of the American cockroach (CR), which has been recognized as an important cause of imunoglobulin E-mediated type I hypersensitivity worldwide. However, it is not neasy to obtain a substantial quantity of this allergen for use in functional studies. In the present study, the Per a 9 gene was cloned and expressed in Escherichia coli (E. coli) systems. It was found that 13/16 (81.3%) of the sera from patients with allergies caused by the American CR reacted to Per a 9, as assessed by enzyme-linked immunosorbent assay, confirming that Per a 9 is a major allergen of CR. The induction of the expression of CD63 and CCR3 in passively sensitized basophils (from sera of patients with allergies caused by the American CR) by approximately 4.2-fold indicated that recombinant Per a 9 was functionally active. Three immunoinformatics tools, including the DNASTAR Protean system, Bioinformatics Predicted Antigenic Peptides (BPAP) system and the BepiPred 1.0 server were used to predict the potential B cell epitopes, while Net-MHCIIpan-2.0 and NetMHCII-2.2 were used to predict the T cell epitopes of Per a 9. As a result, we predicted 11 peptides (23–28, 39–46, 58–64, 91–118, 131–136, 145–154, 159–165, 176–183, 290–299, 309–320 and 338–344) as potential B cell linear epitopes. In T cell prediction, the Per a 9 allergen was predicted to have 5 potential T cell epitope sequences, 119–127, 194–202, 210–218, 239–250 and 279–290. The findings of our study may prove to be useful in the development of peptide-based vaccines to combat CR-induced allergies. PMID:27840974

The eTOX data-sharing project to advance in silico drug-induced toxicity prediction.

PubMed

Cases, Montserrat; Briggs, Katharine; Steger-Hartmann, Thomas; Pognan, François; Marc, Philippe; Kleinöder, Thomas; Schwab, Christof H; Pastor, Manuel; Wichard, Jörg; Sanz, Ferran

2014-11-14

The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.

Random Forests (RFs) for Estimation, Uncertainty Prediction and Interpretation of Monthly Solar Potential

NASA Astrophysics Data System (ADS)

Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis

2017-04-01

Solar energy is clean, widely available, and arguably the most promising renewable energy resource. Taking full advantage of solar power, however, requires a deep understanding of its patterns and dependencies in space and time. The recent advances in Machine Learning brought powerful algorithms to estimate the spatio-temporal variations of solar irradiance (the power per unit area received from the Sun, W/m2), using local weather and terrain information. Such algorithms include Deep Learning (e.g. Artificial Neural Networks), or kernel methods (e.g. Support Vector Machines). However, most of these methods have some disadvantages, as they: (i) are complex to tune, (ii) are mainly used as a black box and offering no interpretation on the variables contributions, (iii) often do not provide uncertainty predictions (Assouline et al., 2016). To provide a reasonable solar mapping with good accuracy, these gaps would ideally need to be filled. We present here simple steps using one ensemble learning algorithm namely, Random Forests (Breiman, 2001) to (i) estimate monthly solar potential with good accuracy, (ii) provide information on the contribution of each feature in the estimation, and (iii) offer prediction intervals for each point estimate. We have selected Switzerland as an example. Using a Digital Elevation Model (DEM) along with monthly solar irradiance time series and weather data, we build monthly solar maps for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (GHI), and Extraterrestrial Irradiance (EI). The weather data include monthly values for temperature, precipitation, sunshine duration, and cloud cover. In order to explain the impact of each feature on the solar irradiance of each point estimate, we extend the contribution method (Kuz'min et al., 2011) to a regression setting. Contribution maps for all features can then be computed for each solar map. This provides precious information on the spatial variation of the features impact all

Computational Prediction of the Heterodimeric and Higher-Order Structure of gpE1/gpE2 Envelope Glycoproteins Encoded by Hepatitis C Virus

PubMed Central

Logan, Michael R.; Hockman, Darren; Koehler Leman, Julia; Law, John Lok Man

2017-01-01

ABSTRACT Despite the recent success of newly developed direct-acting antivirals against hepatitis C, the disease continues to be a global health threat due to the lack of diagnosis of most carriers and the high cost of treatment. The heterodimer formed by glycoproteins E1 and E2 within the hepatitis C virus (HCV) lipid envelope is a potential vaccine candidate and antiviral target. While the structure of E1/E2 has not yet been resolved, partial crystal structures of the E1 and E2 ectodomains have been determined. The unresolved parts of the structure are within the realm of what can be modeled with current computational modeling tools. Furthermore, a variety of additional experimental data is available to support computational predictions of E1/E2 structure, such as data from antibody binding studies, cryo-electron microscopy (cryo-EM), mutational analyses, peptide binding analysis, linker-scanning mutagenesis, and nuclear magnetic resonance (NMR) studies. In accordance with these rich experimental data, we have built an in silico model of the full-length E1/E2 heterodimer. Our model supports that E1/E2 assembles into a trimer, which was previously suggested from a study by Falson and coworkers (P. Falson, B. Bartosch, K. Alsaleh, B. A. Tews, A. Loquet, Y. Ciczora, L. Riva, C. Montigny, C. Montpellier, G. Duverlie, E. I. Pecheur, M. le Maire, F. L. Cosset, J. Dubuisson, and F. Penin, J. Virol. 89:10333–10346, 2015, https://doi.org/10.1128/JVI.00991-15). Size exclusion chromatography and Western blotting data obtained by using purified recombinant E1/E2 support our hypothesis. Our model suggests that during virus assembly, the trimer of E1/E2 may be further assembled into a pentamer, with 12 pentamers comprising a single HCV virion. We anticipate that this new model will provide a useful framework for HCV envelope structure and the development of antiviral strategies. IMPORTANCE One hundred fifty million people have been estimated to be infected with hepatitis C

Changes in event-related potential functional networks predict traumatic brain injury in piglets.

PubMed

Atlan, Lorre S; Lan, Ingrid S; Smith, Colin; Margulies, Susan S

2018-06-01

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species. Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured. Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy. This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Tetanus toxoid IgE may be useful in predicting allergy during childhood.

PubMed

Ciprandi, G; De Amici, M; Quaglini, S; Labò, E; Castellazzi, A M; Miraglia Del Giudice, M; Marseglia, A; Bianchi, L; Moratti, R; Marseglia, G L

2012-01-01

Hypersensitivity reactions after immunization with tetanus toxoid are occasionally observed in atopic and non-atopic individuals. High IgE levels in infancy may predict subsequent allergy. The aims of this study were: i) to evaluate the role of specific IgE to tetanus toxoid in children in response to tetanus immunization and the possible factors associated with specific IgE levels, and ii) to investigate the correlation between specific IgE levels to tetanus toxoid and the late development of allergy (up to 12 years). Initially, 278 healthy infants (152 males and 126 females, aged 12 months) living in an urban city were screened for serum total IgE and specific IgE to tetanus toxoid, after having obtained informed consent from parents. After 12 years, 151 children could be evaluated. Total IgE summed with tetanus specific IgE were significantly associated with allergy at 12 years. In conclusion, this study demonstrates that serum total IgE and tetanus specific IgE may be predictive of subsequent allergy onset.

Potential role of salinity in ENSO and MJO predictions

NASA Astrophysics Data System (ADS)

Zhu, J.; Kumar, A.; Murtugudde, R. G.; Xie, P.

2017-12-01

Studies have suggested that ocean salinity can vary in response to ENSO and MJO. For example, during an El Niño event, sea surface salinity decreases in the western and central equatorial Pacific, as a result of zonal advection of low salinity water by anomalous eastward surface currents, and to a lesser extent as a result of a rainfall excess associated with atmospheric convection and warm water displacements. However, the effect of salinity on ENSO and MJO evolutions and their forecasts has been less explored. In this analysis, we explored the potential role of salinity in ENSO and MJO predictions by conducting sensitivity experiments with NCEP CFSv2. Firstly, two forecasts experiments are conducted to explore its effect on ENSO predictions, in which the interannual variability of salinity in the ocean initial states is either included or excluded. Comparisons suggested that the salinity variability is essential to correctly forecast the 2007/08 La Niña starting from April 2007. With realistic salinity initial states, the tendency to decay of the subsurface cold condition during the spring and early summer 2007 was interrupted by positive salinity anomalies in the upper central Pacific, which working together with the Bjerknes positive feedback, contributed to the development of the La Niña event. Our study suggests that ENSO forecasts will benefit from more accurate sustained salinity observations having large-scale spatial coverage. We also assessed the potential role of salinity in MJO by evaluating a long coupled free run that has a relatively realistic MJO simulation and a set of predictability experiment, both based on CFSv2. Diagnostics of the free run suggest that, while the intraseasonal SST variations lead convections by a quarter cycle, they are almost in phase only with changes in barrier layer thickness, thereby suggesting an active role of salinity on SST. Its effect on MJO predictions is further explored by controlling the surface salinity

Bringing modeling to the masses: A web based system to predict potential species distributions

USGS Publications Warehouse

Graham, Jim; Newman, Greg; Kumar, Sunil; Jarnevich, Catherine S.; Young, Nick; Crall, Alycia W.; Stohlgren, Thomas J.; Evangelista, Paul

2010-01-01

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Prediction of Potential Hit Song and Musical Genre Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Monterola, Christopher; Abundo, Cheryl; Tugaff, Jeric; Venturina, Lorcel Ericka

Accurately quantifying the goodness of music based on the seemingly subjective taste of the public is a multi-million industry. Recording companies can make sound decisions on which songs or artists to prioritize if accurate forecasting is achieved. We extract 56 single-valued musical features (e.g. pitch and tempo) from 380 Original Pilipino Music (OPM) songs (190 are hit songs) released from 2004 to 2006. Based on an effect size criterion which measures a variable's discriminating power, the 20 highest ranked features are fed to a classifier tasked to predict hit songs. We show that regardless of musical genre, a trained feed-forward neural network (NN) can predict potential hit songs with an average accuracy of ΦNN = 81%. The accuracy is about +20% higher than those of standard classifiers such as linear discriminant analysis (LDA, ΦLDA = 61%) and classification and regression trees (CART, ΦCART = 57%). Both LDA and CART are above the proportional chance criterion (PCC, ΦPCC = 50%) but are slightly below the suggested acceptable classifier requirement of 1.25*ΦPCC = 63%. Utilizing a similar procedure, we demonstrate that different genres (ballad, alternative rock or rock) of OPM songs can be automatically classified with near perfect accuracy using LDA or NN but only around 77% using CART.

EEG potentials predict upcoming emergency brakings during simulated driving

NASA Astrophysics Data System (ADS)

Haufe, Stefan; Treder, Matthias S.; Gugler, Manfred F.; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin

2011-10-01

Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h-1 driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.

EEG potentials predict upcoming emergency brakings during simulated driving.

PubMed

Haufe, Stefan; Treder, Matthias S; Gugler, Manfred F; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin

2011-10-01

Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h(-1) driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.

Characterization of a BETA-Catenin-Associated Kinase

DTIC Science & Technology

1999-08-01

and the chambers removed. The cells were mounted with Vectashield (Vector Labs , Inc.). Antibodies The anti-ß-catenin (C19220) and anti-p27 (K25020...anti- HA mAb was purchased from Boehringer Mannheim Corp. (#186723). The anti-E-cadherin (SHE78-7) mAb was purchased from Zymed Labs , Inc...600. van de Wetering, M., R. Cavallo, D. Dooijes, M. van Beest , J. van Es, J. Louri- ero, A. Ypma, D. Hursh, T. Jones, A. Bejsovec, et al. 1997

Brain potentials measured during a Go/NoGo task predict completion of substance abuse treatment.

PubMed

Steele, Vaughn R; Fink, Brandi C; Maurer, J Michael; Arbabshirani, Mohammad R; Wilber, Charles H; Jaffe, Adam J; Sidz, Anna; Pearlson, Godfrey D; Calhoun, Vince D; Clark, Vincent P; Kiehl, Kent A

2014-07-01

U.S. nationwide estimates indicate that 50% to 80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. We tested whether pretreatment neural measures of a response inhibition (Go/NoGo) task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Adult incarcerated participants (n = 89; women n = 55) who volunteered for substance abuse treatment performed a Go/NoGo task while event-related potentials (ERPs) were recorded. Stimulus- and response-locked ERPs were compared between participants who completed (n = 68; women = 45) and discontinued (n = 21; women = 10) treatment. As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, depression, anxiety, motivation for change, and years of drug abuse). Participants who discontinued treatment exhibited deficiencies in sensory gating, as indexed by smaller P2; error-monitoring, as indexed by smaller ERN/Ne; and adjusting response strategy posterror, as indexed by larger Pe. The combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.

Brain Potentials Measured During a Go/NoGo Task Predict Completion of Substance Abuse Treatment

PubMed Central

Steele, Vaughn R.; Fink, Brandi C.; Maurer, J. Michael; Arbabshirani, Mohammad R.; Wilber, Charles H.; Jaffe, Adam J.; Sidz, Anna; Pearlson, Godfrey D.; Calhoun, Vince D.; Clark, Vincent P.; Kiehl, Kent A.

2014-01-01

Background US nationwide estimates indicate 50–80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. The purpose of the present study was to test the hypothesis that pre-treatment neural measures of a Go/NoGo task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Methods Adult incarcerated participants (N=89; Females=55) who volunteered for substance abuse treatment performed a response inhibition (Go/NoGo) task while event-related potentials (ERP) were recorded. Stimulus- and response-locked ERPs were compared between individuals who completed (N=68; Females=45) and discontinued (N=21; Females=10) treatment. Results As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, and self-reported depression, anxiety, motivation for change, and years of drug abuse). Conclusions We conclude individuals who discontinue treatment exhibited deficiencies in sensory gating, as indexed by smaller P2, error-monitoring, as indexed by smaller ERN/Ne, and adjusting response strategy post-error, as indexed by larger Pe. However, the combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. PMID:24238783

E6 and E7 Antibody Levels Are Potential Biomarkers of Recurrence in Patients with Advanced-Stage Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma.

PubMed

Spector, Matthew E; Sacco, Assuntina G; Bellile, Emily; Taylor, Jeremy M G; Jones, Tamara; Sun, Kan; Brown, William C; Birkeland, Andrew C; Bradford, Carol R; Wolf, Gregory T; Prince, Mark E; Moyer, Jeffrey S; Malloy, Kelly; Swiecicki, Paul; Eisbruch, Avraham; McHugh, Jonathan B; Chepeha, Douglas B; Rozek, Laura; Worden, Francis P

2017-06-01

Purpose: There is a paucity of biomarkers to predict failure in human papillomavirus-positive (HPV + ) oropharyngeal squamous cell carcinoma (OPSCC) following curative therapy. E6/E7 viral oncoproteins are constitutively expressed in HPV + tumors and highly immunogenic, resulting in readily detected serum antibodies. The purpose of this study is to determine whether serum E6 and E7 antibody levels can potentially serve as a biomarker of recurrence in patients with HPV+OPSCC. Experimental Design: We evaluated E6/E7 antibody levels in patients with previously untreated, advanced stage (III, IVa-b), HPV+OPSCC receiving definitive chemoradiation under a uniform protocol from 2003 to 2010. Baseline and longitudinal serum samples were obtained from our archived repository. E6/E7 serum levels were measured using a glutathione- S -transferase capture ELISA and quantified by approximating the area under the dilution curve, and were analyzed using ANOVA and linear mixed model for longitudinal analysis. Results: We compared 22 HPV+OPSCC patients who developed recurrence with 30 patients who remained disease-free. There were no differences in T classification, N classification, disease subsite, or smoking status between the groups. In a longitudinal analysis, recurrent patients had significantly higher E6 and E7 serum antibody levels than the nonrecurrent patients over the follow-up period ( P = 0.02 and P = 0.002, respectively). Patients who recurred had a lower clearance of E7 antibody than patients who remained disease-free ( P = 0.0016). Conclusions: Patients with HPV+OPSCC whose disease recurs have a lower clearance of E6 and E7 antibodies than patients who do not have recurrence. The ratio of E7 antibody at disease recurrence compared with baseline is potentially a clinically significant measurement of disease status in HPV+OPSCC. Clin Cancer Res; 23(11); 2723-9. ©2016 AACR . ©2016 American Association for Cancer Research.

Predicting the influence of liposomal lipid composition on liposome size, zeta potential and liposome-induced dendritic cell maturation using a design of experiments approach.

PubMed

Soema, Peter C; Willems, Geert-Jan; Jiskoot, Wim; Amorij, Jean-Pierre; Kersten, Gideon F

2015-08-01

In this study, the effect of liposomal lipid composition on the physicochemical characteristics and adjuvanticity of liposomes was investigated. Using a design of experiments (DoE) approach, peptide-containing liposomes containing various lipids (EPC, DOPE, DOTAP and DC-Chol) and peptide concentrations were formulated. Liposome size and zeta potential were determined for each formulation. Moreover, the adjuvanticity of the liposomes was assessed in an in vitro dendritic cell (DC) model, by quantifying the expression of DC maturation markers CD40, CD80, CD83 and CD86. The acquired data of these liposome characteristics were successfully fitted with regression models, and response contour plots were generated for each response factor. These models were applied to predict a lipid composition that resulted in a liposome with a target zeta potential. Subsequently, the expression of the DC maturation factors for this lipid composition was predicted and tested in vitro; the acquired maturation responses corresponded well with the predicted ones. These results show that a DoE approach can be used to screen various lipids and lipid compositions, and to predict their impact on liposome size, charge and adjuvanticity. Using such an approach may accelerate the formulation development of liposomal vaccine adjuvants. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Validating computational predictions of night-time ventilation in Stanford's Y2E2 building

NASA Astrophysics Data System (ADS)

Chen, Chen; Lamberti, Giacomo; Gorle, Catherine

2017-11-01

Natural ventilation can significantly reduce building energy consumption, but robust design is a challenging task. We previously presented predictions of natural ventilation performance in Stanford's Y2E2 building using two models with different levels of fidelity, embedded in an uncertainty quantification framework to identify the dominant uncertain parameters and predict quantified confidence intervals. The results showed a slightly high cooling rate for the volume-averaged temperature, and the initial thermal mass temperature and window discharge coefficients were found to have an important influence on the results. To further investigate the potential role of these parameters on the observed discrepancies, the current study is performing additional measurements in the Y2E2 building. Wall temperatures are recorded throughout the nightflush using thermocouples; flow rates through windows are measured using hotwires; and spatial variability in the air temperature is explored. The measured wall temperatures are found the be within the range of our model assumptions, and the measured velocities agree reasonably well with our CFD predications. Considerable local variations in the indoor air temperature have been recorded, largely explaining the discrepancies in our earlier validation study. Future work will therefore focus on a local validation of the CFD results with the measurements. Center for Integrated Facility Engineering (CIFE).

Potential Predictability of U.S. Summer Climate with "Perfect" Soil Moisture

NASA Technical Reports Server (NTRS)

Yang, Fanglin; Kumar, Arun; Lau, K.-M.

2004-01-01

The potential predictability of surface-air temperature and precipitation over the United States continent was assessed for a GCM forced by observed sea surface temperatures and an estimate of observed ground soil moisture contents. The latter was obtained by substituting the GCM simulated precipitation, which is used to drive the GCM's land-surface component, with observed pentad-mean precipitation at each time step of the model's integration. With this substitution, the simulated soil moisture correlates well with an independent estimate of observed soil moisture in all seasons over the entire US continent. Significant enhancements on the predictability of surface-air temperature and precipitation were found in boreal late spring and summer over the US continent. Anomalous pattern correlations of precipitation and surface-air temperature over the US continent in the June-July-August season averaged for the 1979-2000 period increased from 0.01 and 0.06 for the GCM simulations without precipitation substitution to 0.23 and 0.3 1, respectively, for the simulations with precipitation substitution. Results provide an estimate for the limits of potential predictability if soil moisture variability is to be perfectly predicted. However, this estimate may be model dependent, and needs to be substantiated by other modeling groups.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

Computational Prediction of the Heterodimeric and Higher-Order Structure of gpE1/gpE2 Envelope Glycoproteins Encoded by Hepatitis C Virus.

PubMed

Freedman, Holly; Logan, Michael R; Hockman, Darren; Koehler Leman, Julia; Law, John Lok Man; Houghton, Michael

2017-04-15

Despite the recent success of newly developed direct-acting antivirals against hepatitis C, the disease continues to be a global health threat due to the lack of diagnosis of most carriers and the high cost of treatment. The heterodimer formed by glycoproteins E1 and E2 within the hepatitis C virus (HCV) lipid envelope is a potential vaccine candidate and antiviral target. While the structure of E1/E2 has not yet been resolved, partial crystal structures of the E1 and E2 ectodomains have been determined. The unresolved parts of the structure are within the realm of what can be modeled with current computational modeling tools. Furthermore, a variety of additional experimental data is available to support computational predictions of E1/E2 structure, such as data from antibody binding studies, cryo-electron microscopy (cryo-EM), mutational analyses, peptide binding analysis, linker-scanning mutagenesis, and nuclear magnetic resonance (NMR) studies. In accordance with these rich experimental data, we have built an in silico model of the full-length E1/E2 heterodimer. Our model supports that E1/E2 assembles into a trimer, which was previously suggested from a study by Falson and coworkers (P. Falson, B. Bartosch, K. Alsaleh, B. A. Tews, A. Loquet, Y. Ciczora, L. Riva, C. Montigny, C. Montpellier, G. Duverlie, E. I. Pecheur, M. le Maire, F. L. Cosset, J. Dubuisson, and F. Penin, J. Virol. 89:10333-10346, 2015, https://doi.org/10.1128/JVI.00991-15). Size exclusion chromatography and Western blotting data obtained by using purified recombinant E1/E2 support our hypothesis. Our model suggests that during virus assembly, the trimer of E1/E2 may be further assembled into a pentamer, with 12 pentamers comprising a single HCV virion. We anticipate that this new model will provide a useful framework for HCV envelope structure and the development of antiviral strategies. IMPORTANCE One hundred fifty million people have been estimated to be infected with hepatitis C virus, and

A hadoop-based method to predict potential effective drug combination.

PubMed

Sun, Yifan; Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

A Hadoop-Based Method to Predict Potential Effective Drug Combination

PubMed Central

Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request. PMID:25147789

Evoked potentials recorded during routine EEG predict outcome after perinatal asphyxia.

PubMed

Nevalainen, Päivi; Marchi, Viviana; Metsäranta, Marjo; Lönnqvist, Tuula; Toiviainen-Salo, Sanna; Vanhatalo, Sampsa; Lauronen, Leena

2017-07-01

To evaluate the added value of somatosensory (SEPs) and visual evoked potentials (VEPs) recorded simultaneously with routine EEG in early outcome prediction of newborns with hypoxic-ischemic encephalopathy under modern intensive care. We simultaneously recorded multichannel EEG, median nerve SEPs, and flash VEPs during the first few postnatal days in 50 term newborns with hypoxic-ischemic encephalopathy. EEG background was scored into five grades and the worst two grades were considered to indicate poor cerebral recovery. Evoked potentials were classified as absent or present. Clinical outcome was determined from the medical records at a median age of 21months. Unfavorable outcome included cerebral palsy, severe mental retardation, severe epilepsy, or death. The accuracy of outcome prediction was 98% with SEPs compared to 90% with EEG. EEG alone always predicted unfavorable outcome when it was inactive (n=9), and favorable outcome when it was normal or only mildly abnormal (n=17). However, newborns with moderate or severe EEG background abnormality could have either favorable or unfavorable outcome, which was correctly predicted by SEP in all but one newborn (accuracy in this subgroup 96%). Absent VEPs were always associated with an inactive EEG, and an unfavorable outcome. However, presence of VEPs did not guarantee a favorable outcome. SEPs accurately predict clinical outcomes in newborns with hypoxic-ischemic encephalopathy and improve the EEG-based prediction particularly in those newborns with severely or moderately abnormal EEG findings. SEPs should be added to routine EEG recordings for early bedside assessment of newborns with hypoxic-ischemic encephalopathy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Potential impact of initialization on decadal predictions as assessed for CMIP5 models

NASA Astrophysics Data System (ADS)

Branstator, Grant; Teng, Haiyan

2012-06-01

To investigate the potential for initialization to improve decadal range predictions, we quantify the initial value predictability of upper 300 m temperature in the two northern ocean basins for 12 models from Coupled Model Intercomparison Project phase 5 (CMIP5), and we contrast it with the forced predictability in Representative Concentration Pathways (RCP) 4.5 climate change projections. We use a recently introduced method that produces predictability estimates from long control runs. Many initial states are considered, and we find on average 1) initialization has the potential to improve skill in the first 5 years in the North Pacific and the first 9 years in the North Atlantic, and 2) the impact from initialization becomes secondary compared to the impact of RCP4.5 forcing after 6 1/2 and 8 years in the two basins, respectively. Model-to-model and spatial variations in these limits are, however, substantial.

E-learning in medical education: the potential environmental impact.

PubMed

Walsh, Kieran

2018-03-01

Introduction There is a growing interest in the use of e-learning in medical education. However until recently there has been little interest in the potential environmental benefits of e-learning. This paper models various environmental outcomes that might emerge from the use of an e-learning resource (BMJ Learning) in CPD. Methods We modeled the use of e-learning as a component of CPD and evaluated the potential impact of this use on the learner's carbon footprint. We looked at a number of models - all from the perspective of a General Practitioner (GP). We assumed that all GPs completed 50 h or credits of CPD per year. Results High users of e-learning can reduce their carbon footprint - mainly by reducing their travel to face-to-face events (reducing printing also has a small beneficial effect). A high user of e-learning can reduce the carbon footprint that relates to their CPD by 18.5 kg. Discussion As global warming continues to pose a risk to human and environmental health, we feel that doctors have a duty to consider learning activities (such as e-learning) that are associated with a lower carbon footprint.

Predicting Energy Consumption for Potential Effective Use in Hybrid Vehicle Powertrain Management Using Driver Prediction

NASA Astrophysics Data System (ADS)

Magnuson, Brian

A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The

A Comparison of Manual Scaled and Predicted foE and foF1 Critical Frequencies

DTIC Science & Technology

1990-07-01

Statistics for the lonograms Studied 17 xi 1.0 INTRODUCTION The ARTIST autoscaling routines use a predicted foE to determine a range to search for the...recommendations are made to help improve autoscaling . 20. DISTRIBUTIONIAVAILABILITY OF ABSTRACT 21. ABSTRACT SECURITY CLASSIFICATION UUNCLASSIFIEDUNLIMITED 0...to estimate foE. In the ARTIST , the predicted foE is the CCIR model described in the CCIR Supplement Report 252-2.1 We have also tested a foE

eTOXlab, an open source modeling framework for implementing predictive models in production environments.

PubMed

Carrió, Pau; López, Oriol; Sanz, Ferran; Pastor, Manuel

2015-01-01

Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by e

A "Uses and Gratification Expectancy Model" to Predict Students' "Perceived e-Learning Experience"

ERIC Educational Resources Information Center

Mondi, Makingu; Woods, Peter; Rafi, Ahmad

2008-01-01

This study investigates "how and why" students' "Uses and Gratification Expectancy" (UGE) for e-learning resources influences their "Perceived e-Learning Experience." A "Uses and Gratification Expectancy Model" (UGEM) framework is proposed to predict students' "Perceived e-Learning Experience," and…

Testing predictions of the quantum landscape multiverse 2: the exponential inflationary potential

NASA Astrophysics Data System (ADS)

Di Valentino, Eleonora; Mersini-Houghton, Laura

2017-03-01

The 2015 Planck data release tightened the region of the allowed inflationary models. Inflationary models with convex potentials have now been ruled out since they produce a large tensor to scalar ratio. Meanwhile the same data offers interesting hints on possible deviations from the standard picture of CMB perturbations. Here we revisit the predictions of the theory of the origin of the universe from the landscape multiverse for the case of exponential inflation, for two reasons: firstly to check the status of the anomalies associated with this theory, in the light of the recent Planck data; secondly, to search for a counterexample whereby new physics modifications may bring convex inflationary potentials, thought to have been ruled out, back into the region of potentials allowed by data. Using the exponential inflation as an example of convex potentials, we find that the answer to both tests is positive: modifications to the perturbation spectrum and to the Newtonian potential of the universe originating from the quantum entanglement, bring the exponential potential, back within the allowed region of current data; and, the series of anomalies previously predicted in this theory, is still in good agreement with current data. Hence our finding for this convex potential comes at the price of allowing for additional thermal relic particles, equivalently dark radiation, in the early universe.

Molecular cloning, expression, IgE binding activities and in silico epitope prediction of Per a 9 allergens of the American cockroach.

PubMed

Yang, Haiwei; Chen, Hao; Jin, Min; Xie, Hua; He, Shaoheng; Wei, Ji-Fu

2016-12-01

Per a 9 is a major allergen of the American cockroach (CR), which has been recognized as an important cause of imunoglobulin E-mediated type I hypersensitivity worldwide. However, it is not neasy to obtain a substantial quantity of this allergen for use in functional studies. In the present study, the Per a 9 gene was cloned and expressed in Escherichia coli (E. coli) systems. It was found that 13/16 (81.3%) of the sera from patients with allergies caused by the American CR reacted to Per a 9, as assessed by enzyme-linked immunosorbent assay, confirming that Per a 9 is a major allergen of CR. The induction of the expression of CD63 and CCR3 in passively sensitized basophils (from sera of patients with allergies caused by the American CR) by approximately 4.2-fold indicated that recombinant Per a 9 was functionally active. Three immunoinformatics tools, including the DNAStar Protean system, Bioinformatics Predicted Antigenic Peptides (BPAP) system and the BepiPred 1.0 server were used to predict the potential B cell epitopes, while Net-MHCIIpan-2.0 and NetMHCII-2.2 were used to predict the T cell epitopes of Per a 9. As a result, we predicted 11 peptides (23-28, 39-46, 58-64, 91-118, 131-136, 145-154, 159-165, 176-183, 290-299, 309-320 and 338-344) as potential B cell linear epitopes. In T cell prediction, the Per a 9 allergen was predicted to have 5 potential T cell epitope sequences, 119-127, 194-202, 210-218, 239-250 and 279-290. The findings of our study may prove to be useful in the development of peptide-based vaccines to combat CR-induced allergies.

State of the art for ab initio vs empirical potentials for HeH+ (2e-), BeH+ (4e-), BeH (5e-), Li2 (6e-) and BH (6e-)

NASA Astrophysics Data System (ADS)

Dattani, Nike

For large internuclear distances, the potential energy between two atoms is known analytically, based on constants that are calculated from atomic ab initio rather than molecular ab initio. This analytic form can be built into models for molecular potentials that are fitted to spectroscopic data. Such empirical potentials constitute the most accurate molecular potentials known. For HeH+, and BeH+, the long-range form of the potential is based only on the polarizabilities for He and H respectively, for which we have included up to 4th order QED corrections. For BeH, the best ab initio potential matches all but one observed vibrational spacing to < 1 cm- accuracy, and for Li2 the discrepancy in the spacings is < 0.08 cm-1 for all vibrational levels. But experimental methods such as photoassociation require the absolute energies, not spacings, and these are still several in several cm-1 disagreement. So empirical potentials are still the only reliable way to predict energies for few-electron systems. We also give predictions for various unobserved ''halo nucleonic molecules'' containing the ''halo'' isotopes: 6,8He, 11Li, 11,14Be and 8 , 17 , 19B.

Using kinematic analysis of movement to predict the time occurrence of an evoked potential associated with a motor command.

PubMed

O'Reilly, Christian; Plamondon, Réjean; Landou, Mohamed K; Stemmer, Brigitte

2013-01-01

This article presents an exploratory study investigating the possibility of predicting the time occurrence of a motor event related potential (ERP) from a kinematic analysis of human movements. Although the response-locked motor potential may link the ERP components to the recorded response, to our knowledge no previous attempt has been made to predict a priori (i.e. before any contact with the electroencephalographic data) the time occurrence of an ERP component based only on the modeling of an overt response. The proposed analysis relies on the delta-lognormal modeling of velocity, as proposed by the kinematic theory of rapid human movement used in several studies of motor control. Although some methodological aspects of this technique still need to be fine-tuned, the initial results showed that the model-based kinematic analysis allowed the prediction of the time occurrence of a motor command ERP in most participants in the experiment. The average map of the motor command ERPs showed that this signal was stronger in electrodes close to the contra-lateral motor area (Fz, FCz, FC1, and FC3). These results seem to support the claims made by the kinematic theory that a motor command is emitted at time t(0), the time reference parameter of the model. This article proposes a new time marker directly associated with a cerebral event (i.e. the emission of a motor command) that can be used for the development of new data analysis methodologies and for the elaboration of new experimental protocols based on ERP. © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Combined visual and motor evoked potentials predict multiple sclerosis disability after 20 years.

PubMed

Schlaeger, Regina; Schindler, Christian; Grize, Leticia; Dellas, Sophie; Radue, Ernst W; Kappos, Ludwig; Fuhr, Peter

2014-09-01

The development of predictors of multiple sclerosis (MS) disability is difficult due to the complex interplay of pathophysiological and adaptive processes. The purpose of this study was to investigate whether combined evoked potential (EP)-measures allow prediction of MS disability after 20 years. We examined 28 patients with clinically definite MS according to Poser's criteria with Expanded Disability Status Scale (EDSS) scores, combined visual and motor EPs at entry (T0), 6 (T1), 12 (T2) and 24 (T3) months, and a cranial magnetic resonance imaging (MRI) scan at T0 and T2. EDSS testing was repeated at year 14 (T4) and year 20 (T5). Spearman rank correlation was used. We performed a multivariable regression analysis to examine predictive relationships of the sum of z-transformed EP latencies (s-EPT0) and other baseline variables with EDSST5. We found that s-EPT0 correlated with EDSST5 (rho=0.72, p<0.0001) and ΔEDSST5-T0 (rho=0.50, p=0.006). Backward selection resulted in the prediction model: E (EDSST5)=3.91-2.22×therapy+0.079×age+0.057×s-EPT0 (Model 1, R (2)=0.58) with therapy as binary variable (1=any disease-modifying therapy between T3 and T5, 0=no therapy). Neither EDSST0 nor T2-lesion or gadolinium (Gd)-enhancing lesion quantities at T0 improved prediction of EDSST5. The area under the receiver operating characteristic (ROC) curve was 0.89 for model 1. These results further support a role for combined EP-measures as predictors of long-term disability in MS. © The Author(s) 2014.

The Natural History of IgE-Mediated Food Allergy: Can Skin Prick Tests and Serum-Specific IgE Predict the Resolution of Food Allergy?

PubMed Central

Peters, Rachel L.; Gurrin, Lyle C.; Dharmage, Shyamali C.; Koplin, Jennifer J.; Allen, Katrina J.

2013-01-01

IgE-mediated food allergy is a transient condition for some children, however there are few indices to predict when and in whom food allergy will resolve. Skin prick test (SPT) and serum-specific IgE levels (sIgE) are usually monitored in the management of food allergy and are used to predict the development of tolerance or persistence of food allergy. The aim of this article is to review the published literature that investigated the predictive value of SPT and sIgE in development of tolerance in children with a previous diagnosis of peanut, egg and milk allergy. A systematic search identified twenty-six studies, of which most reported SPT or sIgE thresholds which predicted persistent or resolved allergy. However, results were inconsistent between studies. Previous research was hampered by several limitations including the absence of gold standard test to diagnose food allergy or tolerance, biased samples in retrospective audits and lack of systematic protocols for triggering re-challenges. There is a need for population-based, prospective studies that use the gold standard oral food challenge (OFC) to diagnose food allergy at baseline and follow-up to develop SPT and sIgE thresholds that predict the course of food allergy. PMID:24132133

Evaluation of in silico tools to predict the skin sensitization potential of chemicals.

PubMed

Verheyen, G R; Braeken, E; Van Deun, K; Van Miert, S

2017-01-01

Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE Ultra) or rule based (OECD Toolbox, Toxtree, Derek Nexus). In practice, several of these in silico tools are used in gap filling and read-across, but here their use was limited to make predictions based on presence/absence of structural features associated to sensitization. The top 400 ranking substances of the ATSDR 2011 Priority List of Hazardous Substances were selected as a starting point. Experimental information was identified for 160 chemically diverse substances (82 positive and 78 negative). The prediction for skin sensitization potential was compared with the experimental data. Rule-based tools perform slightly better, with accuracies ranging from 0.6 (OECD Toolbox) to 0.78 (Derek Nexus), compared with statistical tools that had accuracies ranging from 0.48 (Vega) to 0.73 (CASE Ultra - LLNA weak model). Combining models increased the performance, with positive and negative predictive values up to 80% and 84%, respectively. However, the number of substances that were predicted positive or negative for skin sensitization in both models was low. Adding more substances to the dataset will increase the confidence in the conclusions reached. The insights obtained in this evaluation are incorporated in a web database www.asopus.weebly.com that provides a potential end user context for the scope and performance of different in silico tools with respect to a common dataset of curated skin sensitization data.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

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

Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

2007-07-01

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200

Climate matching as a tool for predicting potential North American spread of Brown Treesnakes

USGS Publications Warehouse

Rodda, Gordon H.; Reed, Robert N.; Jarnevich, Catherine S.; Witmer, G.W.; Pitt, W. C.; Fagerstone, K.A.

2007-01-01

Climate matching identifies extralimital destinations that could be colonized by a potential invasive species on the basis of similarity to climates found in the species’ native range. Climate is a proxy for the factors that determine whether a population will reproduce enough to offset mortality. Previous climate matching models (e.g., Genetic Algorithm for Rule-set Prediction [GARP]) for brown treesnakes (Boiga irregularis) were unsatisfactory, perhaps because the models failed to allow different combinations of climate attributes to influence a species’ range limits in different parts of the range. Therefore, we explored the climate space described by bivariate parameters of native range temperature and rainfall, allowing up to two months of aestivation in the warmer portions of the range, or four months of hibernation in temperate climes. We found colonization area to be minimally sensitive to assumptions regarding hibernation temperature thresholds. Although brown treesnakes appear to be limited by dry weather in the interior of Australia, aridity rarely limits potential distribution in most of the world. Potential colonization area in North America is limited primarily by cold. Climatically suitable portions of the United States (US) mainland include the Central Valley of California, mesic patches in the Southwest, and the southeastern coastal plain from Texas to Virginia.

Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency.

PubMed

Stinear, Cathy M; Byblow, Winston D; Ackerley, Suzanne J; Barber, P Alan; Smith, Marie-Claire

2017-04-01

Several clinical measures and biomarkers are associated with motor recovery after stroke, but none are used to guide rehabilitation for individual patients. The objective of this study was to evaluate the implementation of upper limb predictions in stroke rehabilitation, by combining clinical measures and biomarkers using the Predict Recovery Potential (PREP) algorithm. Predictions were provided for patients in the implementation group (n=110) and withheld from the comparison group (n=82). Predictions guided rehabilitation therapy focus for patients in the implementation group. The effects of predictive information on clinical practice (length of stay, therapist confidence, therapy content, and dose) were evaluated. Clinical outcomes (upper limb function, impairment and use, independence, and quality of life) were measured 3 and 6 months poststroke. The primary clinical practice outcome was inpatient length of stay. The primary clinical outcome was Action Research Arm Test score 3 months poststroke. Length of stay was 1 week shorter for the implementation group (11 days; 95% confidence interval, 9-13 days) than the comparison group (17 days; 95% confidence interval, 14-21 days; P =0.001), controlling for upper limb impairment, age, sex, and comorbidities. Therapists were more confident ( P =0.004) and modified therapy content according to predictions for the implementation group ( P <0.05). The algorithm correctly predicted the primary clinical outcome for 80% of patients in both groups. There were no adverse effects of algorithm implementation on patient outcomes at 3 or 6 months poststroke. PREP algorithm predictions modify therapy content and increase rehabilitation efficiency after stroke without compromising clinical outcome. URL: http://anzctr.org.au. Unique identifier: ACTRN12611000755932. © 2017 American Heart Association, Inc.

Effect of foam on temperature prediction and heat recovery potential from biological wastewater treatment.

PubMed

Corbala-Robles, L; Volcke, E I P; Samijn, A; Ronsse, F; Pieters, J G

2016-05-15

Heat is an important resource in wastewater treatment plants (WWTPs) which can be recovered. A prerequisite to determine the theoretical heat recovery potential is an accurate heat balance model for temperature prediction. The insulating effect of foam present on the basin surface and its influence on temperature prediction were assessed in this study. Experiments were carried out to characterize the foam layer and its insulating properties. A refined dynamic temperature prediction model, taking into account the effect of foam, was set up. Simulation studies for a WWTP treating highly concentrated (manure) wastewater revealed that the foam layer had a significant effect on temperature prediction (3.8 ± 0.7 K over the year) and thus on the theoretical heat recovery potential (30% reduction when foam is not considered). Seasonal effects on the individual heat losses and heat gains were assessed. Additionally, the effects of the critical basin temperature above which heat is recovered, foam thickness, surface evaporation rate reduction and the non-absorbed solar radiation on the theoretical heat recovery potential were evaluated. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of optical action potentials in human induced pluripotent stem cells-derived cardiomyocytes to predict drug-induced cardiac arrhythmias.

PubMed

Lu, H R; Hortigon-Vinagre, M P; Zamora, V; Kopljar, I; De Bondt, A; Gallacher, D J; Smith, G

2017-09-01

Human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) are emerging as new and human-relevant source in vitro model for cardiac safety assessment that allow us to investigate a set of 20 reference drugs for predicting cardiac arrhythmogenic liability using optical action potential (oAP) assay. Here, we describe our examination of the oAP measurement using a voltage sensitive dye (Di-4-ANEPPS) to predict adverse compound effects using hiPS-CMs and 20 cardioactive reference compounds. Fluorescence signals were digitized at 10kHz and the records subsequently analyzed off-line. Cells were exposed to 30min incubation to vehicle or compound (n=5/dose, 4 doses/compound) that were blinded to the investigating laboratory. Action potential parameters were measured, including rise time (T rise ) of the optical action potential duration (oAPD). Significant effects on oAPD were sensitively detected with 11 QT-prolonging drugs, while oAPD shortening was observed with I Ca -antagonists, I Kr -activator or ATP-sensitive K + channel (K ATP )-opener. Additionally, the assay detected varied effects induced by 6 different sodium channel blockers. The detection threshold for these drug effects was at or below the published values of free effective therapeutic plasma levels or effective concentrations by other studies. The results of this blinded study indicate that OAP is a sensitive method to accurately detect drug-induced effects (i.e., duration/QT-prolongation, shortening, beat rate, and incidence of early after depolarizations) in hiPS-CMs; therefore, this technique will potentially be useful in predicting drug-induced arrhythmogenic liabilities in early de-risking within the drug discovery phase. Copyright © 2017 Elsevier Inc. All rights reserved.

Awareness of eSafety and Potential Online Dangers among Children and Teenagers

ERIC Educational Resources Information Center

Zilka, Gila Cohen

2017-01-01

Aim/Purpose: Awareness of eSafety and potential online dangers for children and teenagers. Background: The study examined eSafety among children and teenagers from their own perspectives, through evaluations of their awareness level of eSafety and of potential online dangers. Methodology: This is a mixed-method study with both quantitative and…

[Prediction of potential geographic distribution of Lyme disease in Qinghai province with Maximum Entropy model].

PubMed

Zhang, Lin; Hou, Xuexia; Liu, Huixin; Liu, Wei; Wan, Kanglin; Hao, Qin

2016-01-01

To predict the potential geographic distribution of Lyme disease in Qinghai by using Maximum Entropy model (MaxEnt). The sero-diagnosis data of Lyme disease in 6 counties (Huzhu, Zeku, Tongde, Datong, Qilian and Xunhua) and the environmental and anthropogenic data including altitude, human footprint, normalized difference vegetation index (NDVI) and temperature in Qinghai province since 1990 were collected. By using the data of Huzhu Zeku and Tongde, the prediction of potential distribution of Lyme disease in Qinghai was conducted with MaxEnt. The prediction results were compared with the human sero-prevalence of Lyme disease in Datong, Qilian and Xunhua counties in Qinghai. Three hot spots of Lyme disease were predicted in Qinghai, which were all in the east forest areas. Furthermore, the NDVI showed the most important role in the model prediction, followed by human footprint. Datong, Qilian and Xunhua counties were all in eastern Qinghai. Xunhua was in hot spot areaⅡ, Datong was close to the north of hot spot area Ⅲ, while Qilian with lowest sero-prevalence of Lyme disease was not in the hot spot areas. The data were well modeled in MaxEnt (Area Under Curve=0.980). The actual distribution of Lyme disease in Qinghai was in consistent with the results of the model prediction. MaxEnt could be used in predicting the potential distribution patterns of Lyme disease. The distribution of vegetation and the range and intensity of human activity might be related with Lyme disease distribution.

Anti-E1E2 antibodies do predict response to triple therapy in treatment-experienced Hepatitis C Virus-cirrhosis cases.

PubMed

Petit, Marie-Anne; Berthillon, Pascale; Pradat, Pierre; Arnaud, Clémence; Bordes, Isabelle; Virlogeux, Victor; Maynard, Marianne; Bailly, François; Zoulim, Fabien; Chemin, Isabelle; Trépo, Christian

2015-12-01

We previously showed that pre-treatment serum anti-E1E2 predicted hepatitis C virus (HCV) RNA viral kinetics (VKs) and treatment outcome in patients with chronic hepatitis C receiving pegylated interferon/ribavirin (Peg-IFN/RBV) double therapy. Here, we determined whether baseline anti-E1E2 was correlated with the on-treatment VK and could predict virological outcome in treatment-experienced HCV-infected cirrhotic patients receiving protease inhibitor-based triple therapy. Sera from 19 patients with HCV genotype 1 infection and compensated cirrhosis who failed to respond to a prior course of Peg-IFN/RBV were selected at time 0 before starting triple therapy with boceprevir or telaprevir. We assessed patients with sustained viral response 12 weeks after the end of triple therapy (SVR12) by analyzing VKs at weeks 4, 12, 24, 36, 48 (end of treatment) and 60. Patients baseline characteristics were similar to the well-defined CUPIC cohort (age, HCV subtype, baseline viremia, and treatment history). Among the 19 patients, 11 achieved an SVR12. Fifteen patients were positive for pre-treatment anti-E1E2 and all of them achieved SVR12. Moreover, anti-E1E2 and SVR12 correlated with prior response to IFN/RBV therapy (relapse, partial or null response). Baseline anti-E1E2 could be considered as a new biomarker to predict SVR12 after triple therapy in this most difficult-to-treat population. These results warrant further validation on larger cohorts including patients receiving highly effective direct-acting antivirals to explore whether this test could help in better defining treatment duration for these very costly molecules. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Predicting high risk of exacerbations in bronchiectasis: the E-FACED score.

PubMed

Martinez-Garcia, M A; Athanazio, R A; Girón, R; Máiz-Carro, L; de la Rosa, D; Olveira, C; de Gracia, J; Vendrell, M; Prados-Sánchez, C; Gramblicka, G; Corso Pereira, M; Lundgren, F L; Fernandes De Figueiredo, M; Arancibia, F; Rached, S Z

2017-01-01

Although the FACED score has demonstrated a great prognostic capacity in bronchiectasis, it does not include the number or severity of exacerbations as a separate variable, which is important in the natural history of these patients. Construction and external validation of a new index, the E-FACED, to evaluate the predictive capacity of exacerbations and mortality. The new score was constructed on the basis of the complete cohort for the construction of the original FACED score, while the external validation was undertaken with six cohorts from three countries (Brazil, Argentina, and Chile). The main outcome was the number of annual exacerbations/hospitalizations, with all-cause and respiratory-related deaths as the secondary outcomes. A statistical evaluation comprised the relative weight and ideal cut-off point for the number or severity of the exacerbations and was incorporated into the FACED score (E-FACED). The results obtained after the application of FACED and E-FACED were compared in both the cohorts. A total of 1,470 patients with bronchiectasis (819 from the construction cohorts and 651 from the external validation cohorts) were followed up for 5 years after diagnosis. The best cut-off point was at least two exacerbations in the previous year (two additional points), meaning that the E-FACED has nine points of growing severity. E-FACED presented an excellent prognostic capacity for exacerbations (areas under the receiver operating characteristic curve: 0.82 for at least two exacerbations in 1 year and 0.87 for at least one hospitalization in 1 year) that was statistically better than that of the FACED score (0.72 and 0.78, P <0.05, respectively). The predictive capacities for all-cause and respiratory mortality were 0.87 and 0.86, respectively, with both being similar to those of the FACED. E-FACED score significantly increases the FACED capacity to predict future yearly exacerbations while maintaining the score's simplicity and prognostic capacity for

Predicting high risk of exacerbations in bronchiectasis: the E-FACED score

PubMed Central

Martinez-Garcia, MA; Athanazio, RA; Girón, R; Máiz-Carro, L; de la Rosa, D; Olveira, C; de Gracia, J; Vendrell, M; Prados-Sánchez, C; Gramblicka, G; Corso Pereira, M; Lundgren, FL; Fernandes De Figueiredo, M; Arancibia, F; Rached, SZ

2017-01-01

Background Although the FACED score has demonstrated a great prognostic capacity in bronchiectasis, it does not include the number or severity of exacerbations as a separate variable, which is important in the natural history of these patients. Objective Construction and external validation of a new index, the E-FACED, to evaluate the predictive capacity of exacerbations and mortality. Methods The new score was constructed on the basis of the complete cohort for the construction of the original FACED score, while the external validation was undertaken with six cohorts from three countries (Brazil, Argentina, and Chile). The main outcome was the number of annual exacerbations/hospitalizations, with all-cause and respiratory-related deaths as the secondary outcomes. A statistical evaluation comprised the relative weight and ideal cut-off point for the number or severity of the exacerbations and was incorporated into the FACED score (E-FACED). The results obtained after the application of FACED and E-FACED were compared in both the cohorts. Results A total of 1,470 patients with bronchiectasis (819 from the construction cohorts and 651 from the external validation cohorts) were followed up for 5 years after diagnosis. The best cut-off point was at least two exacerbations in the previous year (two additional points), meaning that the E-FACED has nine points of growing severity. E-FACED presented an excellent prognostic capacity for exacerbations (areas under the receiver operating characteristic curve: 0.82 for at least two exacerbations in 1 year and 0.87 for at least one hospitalization in 1 year) that was statistically better than that of the FACED score (0.72 and 0.78, P<0.05, respectively). The predictive capacities for all-cause and respiratory mortality were 0.87 and 0.86, respectively, with both being similar to those of the FACED. Conclusion E-FACED score significantly increases the FACED capacity to predict future yearly exacerbations while maintaining the

Two Years of ePrescription in Slovenia - Applications and Potentials.

PubMed

Stanimirovic, Dalibor; Savic, Dusan

2018-01-01

ePrescription is one of the most successful eHealth solutions in Slovenia. Since its national roll-out in early 2016, the quality of its operations has been constantly improving, and the number of users has been growing ever since to reach today's 90% of all healthcare providers. ePrescription facilitates more transparent and safer prescribing of medications, an overview of possible medication interactions, and reduction of administrative and opportunity costs. This paper initially explores the current state of ePrescription in Slovenia and different aspects of its application. Based on the research findings, the paper finally outlines potentials of ePrescription, which could be transformed into tangible benefits with particular implications for healthcare system. The research is based on focus group methodology. Structured discussions were conducted with eminent experts currently in charge of ePrescription (and other eHealth solutions) development and implementation in Slovenia. Research results imply that certain application aspects are relatively easy to define and evaluate, while the overall potentials of ePrescription are difficult to determine in many cases, and relatively unexplored in terms of their implications and operational feasibility.

Ternary chalcogenides C s 2 Z n 3 S e 4 and C s 2 Z n 3 T e 4 : Potential p -type transparent conducting materials

DOE PAGES

Shi, Hongliang; Saparov, Bayrammurad; Singh, David J.; ...

2014-11-11

Here we report prediction of two new ternary chalcogenides that can potentially be used as p-type transparent conductors along with experimental synthesis and initial characterization of these previously unknown compounds, Cs 2Zn 3Ch 4 (Ch = Se, Te). In particular, the structures are predicted based on density functional calculations and confirmed by experiments. Phase diagrams, electronic structure, optical properties, and defect properties of Cs 2Zn 3Se 4 and Cs 2Zn 3Te 4 are calculated to assess the viability of these materials as p-type TCMs. Cs 2Zn 3Se 4 and Cs 2Zn 3Te 4, which are stable under ambient air, displaymore » large optical band gaps (calculated to be 3.61 and 2.83 eV, respectively) and have small hole effective masses (0.5-0.77 m e) that compare favorably with other proposed p-type TCMs. Defect calculations show that undoped Cs2Zn3Se4 and Cs2Zn3Te4 are p-type materials. However, the free hole concentration may be limited by low-energy native donor defects, e.g., Zn interstitials. Lastly, non-equilibrium growth techniques should be useful for suppressing the formation of native donor defects, thereby increasing the hole concentration.« less

Predicting the timing and potential of the spring emergence of overwintered populations of Heliothis spp

NASA Technical Reports Server (NTRS)

Hartstack, A. W.; Witz, J. A.; Lopez, J. D. (Principal Investigator)

1981-01-01

The current state of knowledge dealing with the prediction of the overwintering population and spring emergence of Heliothis spp., a serious pest of numerous crops is surveyed. Current literature is reviewed in detail. Temperature and day length are the primary factors which program H. spp. larva for possible diapause. Although studies on the interaction of temperature and day length are reported, the complete diapause induction process is not identified sufficiently to allow accurate prediction of diapause timing. Mortality during diapause is reported as highly variable. The factors causing mortality are identified, but only a few are quantified. The spring emergence of overwintering H. spp. adults and mathematical models which predict the timing of emergence are reviewed. Timing predictions compare favorably to observed field data; however, prediction of actual numbers of emerging moths is not possible. The potential for use of spring emergence predictions in pest management applications, as an early warning of potential crop damage, are excellent. Research requirements to develop such an early warning system are discussed.

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

PubMed

Kirouac, Daniel C; Schaefer, Gabriele; Chan, Jocelyn; Merchant, Mark; Orr, Christine; Huang, Shih-Min A; Moffat, John; Liu, Lichuan; Gadkar, Kapil; Ramanujan, Saroja

2017-01-01

Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

DNDO Report: Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation

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

Behne, Patrick Alan

The modeling of the detectability of special nuclear material (SNM) at ports and border crossings requires accurate knowledge of the background radiation at those locations. Background radiation originates from two main sources, cosmic and terrestrial. Cosmic background is produced by high-energy galactic cosmic rays (GCR) entering the atmosphere and inducing a cascade of particles that eventually impact the earth’s surface. The solar modulation potential represents one of the primary inputs to modeling cosmic background radiation. Usosokin et al. formally define solar modulation potential as “the mean energy loss [per unit charge] of a cosmic ray particle inside the heliosphere…” Modulationmore » potential, a function of elevation, location, and time, shares an inverse relationship with cosmic background radiation. As a result, radiation detector thresholds require adjustment to account for differing background levels, caused partly by differing solar modulations. Failure to do so can result in higher rates of false positives and failed detection of SNM for low and high levels of solar modulation potential, respectively. This study focuses on solar modulation’s time dependence, and seeks the best method to predict modulation for future dates using Python. To address the task of predicting future solar modulation, we utilize both non-linear least squares sinusoidal curve fitting and cubic spline interpolation. This material will be published in transactions of the ANS winter meeting of November, 2016.« less

Interannual Variability, Global Teleconnection, and Potential Predictability Associated with the Asian Summer Monsoon

NASA Technical Reports Server (NTRS)

Lau, K. M.; Kim, K. M.; Li, J. Y.

2001-01-01

In this Chapter, aspects of global teleconnections associated with the interannual variability of the Asian summer monsoon (ASM) are discussed. The basic differences in the basic dynamics of the South Asian Monsoon and the East Asian monsoon, and their implications on global linkages are discussed. Two teleconnection modes linking ASM variability to summertime precipitation over the continental North America were identified. These modes link regional circulation and precipitation anomalies over East Asia and continental North America, via coupled atmosphere-ocean variations over the North Pacific. The first mode has a large zonally symmetrical component and appears to be associated with subtropical jetstream variability and the second mode with Rossby wave dispersion. Both modes possess strong sea surface temperature (SST) expressions in the North Pacific. Results show that the two teleconnection modes may have its origin in intrinsic modes of sea surface temperature variability in the extratropical oceans, which are forced in part by atmospheric variability and in part by air-sea interaction. The potential predictability of the ASM associated with SST variability in different ocean basins is explored using a new canonical ensemble correlation prediction scheme. It is found that SST anomalies in tropical Pacific, i.e., El Nino, is the most dominant forcing for the ASM, especially over the maritime continent and eastern Australia. SST anomalies in the India Ocean may trump the influence from El Nino in western Australia and western maritime continent. Both El Nino, and North Pacific SSTs contribute to monsoon precipitation anomalies over Japan, southern Korea, northern and central China. By optimizing SST variability signals from the world ocean basins using CEC, the overall predictability of ASM can be substantially improved.

Tyramine Hydrochloride Based Label-Free System for Operating Various DNA Logic Gates and a DNA Caliper for Base Number Measurements.

PubMed

Fan, Daoqing; Zhu, Xiaoqing; Dong, Shaojun; Wang, Erkang

2017-07-05

DNA is believed to be a promising candidate for molecular logic computation, and the fluorogenic/colorimetric substrates of G-quadruplex DNAzyme (G4zyme) are broadly used as label-free output reporters of DNA logic circuits. Herein, for the first time, tyramine-HCl (a fluorogenic substrate of G4zyme) is applied to DNA logic computation and a series of label-free DNA-input logic gates, including elementary AND, OR, and INHIBIT logic gates, as well as a two to one encoder, are constructed. Furthermore, a DNA caliper that can measure the base number of target DNA as low as three bases is also fabricated. This DNA caliper can also perform concatenated AND-AND logic computation to fulfil the requirements of sophisticated logic computing. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Role of learning potential in cognitive remediation: Construct and predictive validity.

PubMed

Davidson, Charlie A; Johannesen, Jason K; Fiszdon, Joanna M

2016-03-01

The construct, convergent, discriminant, and predictive validity of Learning Potential (LP) was evaluated in a trial of cognitive remediation for adults with schizophrenia-spectrum disorders. LP utilizes a dynamic assessment approach to prospectively estimate an individual's learning capacity if provided the opportunity for specific related learning. LP was assessed in 75 participants at study entry, of whom 41 completed an eight-week cognitive remediation (CR) intervention, and 22 received treatment-as-usual (TAU). LP was assessed in a "test-train-test" verbal learning paradigm. Incremental predictive validity was assessed as the degree to which LP predicted memory skill acquisition above and beyond prediction by static verbal learning ability. Examination of construct validity confirmed that LP scores reflected use of trained semantic clustering strategy. LP scores correlated with executive functioning and education history, but not other demographics or symptom severity. Following the eight-week active phase, TAU evidenced little substantial change in skill acquisition outcomes, which related to static baseline verbal learning ability but not LP. For the CR group, LP significantly predicted skill acquisition in domains of verbal and visuospatial memory, but not auditory working memory. Furthermore, LP predicted skill acquisition incrementally beyond relevant background characteristics, symptoms, and neurocognitive abilities. Results suggest that LP assessment can significantly improve prediction of specific skill acquisition with cognitive training, particularly for the domain assessed, and thereby may prove useful in individualization of treatment. Published by Elsevier B.V.

An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture-data assimilation

NASA Astrophysics Data System (ADS)

Cenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, Nazzareno

2017-11-01

The assimilation of satellite-derived soil moisture estimates (soil moisture-data assimilation, SM-DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM-DA in recent years (e.g. the Advanced SCATterometer - ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM-DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014-February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM-DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM-DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further

Predicting Child Abuse Potential: An Empirical Investigation of Two Theoretical Frameworks

ERIC Educational Resources Information Center

Begle, Angela Moreland; Dumas, Jean E.; Hanson, Rochelle F.

2010-01-01

This study investigated two theoretical risk models predicting child maltreatment potential: (a) Belsky's (1993) developmental-ecological model and (b) the cumulative risk model in a sample of 610 caregivers (49% African American, 46% European American; 53% single) with a child between 3 and 6 years old. Results extend the literature by using a…

Prediction and identification of potential immunodominant epitopes in glycoproteins B, C, E, G, and I of herpes simplex virus type 2.

PubMed

Pan, Mingjie; Wang, Xingsheng; Liao, Jianmin; Yin, Dengke; Li, Suqin; Pan, Ying; Wang, Yao; Xie, Guangyan; Zhang, Shumin; Li, Yuexi

2012-01-01

Twenty B candidate epitopes of glycoproteins B (gB2), C (gC2), E (gE2), G (gG2), and I (gI2) of herpes simplex virus type 2 (HSV-2) were predicted using DNAstar, Biosun, and Antheprot methods combined with the polynomial method. Subsequently, the biological functions of the peptides were tested via experiments in vitro. Among the 20 epitope peptides, 17 could react with the antisera to the corresponding parent proteins in the EIA tests. In particular, five peptides, namely, gB2(466-473) (EQDRKPRN), gC2(216-223) (GRTDRPSA), gE2(483-491) (DPPERPDSP), gG2(572-579) (EPPDDDDS), and gI2(286-295) (CRRRYRRPRG) had strong reaction with the antisera. All conjugates of the five peptides with the carrier protein BSA could stimulate mice into producing antibodies. The antisera to these peptides reacted strongly with the corresponding parent glycoproteins during the Western Blot tests, and the peptides reacted strongly with the antibodies against the parent glycoproteins during the EIA tests. The antisera against the five peptides could neutralize HSV-2 infection in vitro, which has not been reported until now. These results suggest that the immunodominant epitopes screened using software algorithms may be used for virus diagnosis and vaccine design against HSV-2.

Accuracy of professional sports drafts in predicting career potential.

PubMed

Koz, D; Fraser-Thomas, J; Baker, J

2012-08-01

The forecasting of talented players is a crucial aspect of building a successful sports franchise and professional sports invest significant resources in making player choices in sport drafts. The current study examined the relationship between career performance (i.e. games played) and draft round for the National Football League, National Hockey League, National Basketball League, and Major League Baseball for players drafted from 1980 to 1989 (n = 4874) against the assumption of a linear relationship between performance and draft round (i.e. that players with the most potential will be selected before players of lower potential). A two-step analysis revealed significant differences in games played across draft rounds (step 1) and a significant negative relationship between draft round and games played (step 2); however, the amount of variance accounted for was relatively low (less than 17%). Results highlight the challenges of accurately evaluating amateur talent. © 2011 John Wiley & Sons A/S.

Inflationary predictions of double-well, Coleman-Weinberg, and hilltop potentials with non-minimal coupling

NASA Astrophysics Data System (ADS)

Bostan, Nilay; Güleryüz, Ömer; Nefer Şenoğuz, Vedat

2018-05-01

We discuss how the non-minimal coupling ξphi2R between the inflaton and the Ricci scalar affects the predictions of single field inflation models where the inflaton has a non-zero vacuum expectation value (VEV) v after inflation. We show that, for inflaton values both above the VEV and below the VEV during inflation, under certain conditions the inflationary predictions become approximately the same as the predictions of the Starobinsky model. We then analyze inflation with double-well and Coleman-Weinberg potentials in detail, displaying the regions in the v-ξ plane for which the spectral index ns and the tensor-to-scalar ratio r values are compatible with the current observations. r is always larger than 0.002 in these regions. Finally, we consider the effect of ξ on small field inflation (hilltop) potentials.

Predicting Continuance—Findings from a Longitudinal Study of Older Adults Using an eHealth Newsletter

PubMed Central

Forquer, Heather A.; Christensen, John L.; Tan, Andy S.L.

2014-01-01

While eHealth technologies are promisingly efficient and widespread, theoretical frameworks capable of predicting long-term use, termed continuance, are lacking. Attempts to extend prominent information technology (IT) theories to the area of eHealth have been limited by small sample sizes, cross-sectional designs, self-reported as opposed to actual use measures, and a focus on technology adoption rather than continuance. To address these gaps in the literature, the present analysis includes empirical evidence of actual use of an eHealth technology over the course of one year. This large (n=4,570) longitudinal study focuses on older adults, a population with many health needs, and among whom eHealth use may be particularly important. With three measurement points over the course of a year, this study examined the effects of utilitarian and hedonic beliefs on the continued use of an eHealth newsletter using constructs from IT adoption and continuance theories. Additional analyses compared the relative strength of intentions compared to earlier use in predicting later use. Usage intention was strongly predicted by both hedonic beliefs and utilitarian beliefs. In addition, utilitarian beliefs had both direct effects on intention, as well as indirect effects, mediated by hedonic beliefs. While intention predicted subsequent use, earlier use was a significantly stronger predictor of use than intention. These findings make a theoretical contribution to an emerging literature by shedding light on the complex interplay of reasoned action and automaticity in the context of eHealth continuance. PMID:24446900

Predicting continuance-findings from a longitudinal study of older adults using an eHealth newsletter.

PubMed

Forquer, Heather A; Christensen, John L; Tan, Andy S L

2014-01-01

While eHealth technologies are promisingly efficient and widespread, theoretical frameworks capable of predicting long-term use, termed continuance, are lacking. Attempts to extend prominent information technology (IT) theories to the area of eHealth have been limited by small sample sizes, cross-sectional designs, self-reported as opposed to actual use measures, and a focus on technology adoption rather than continuance. To address these gaps in the literature, this analysis includes empirical evidence of actual use of an eHealth technology over the course of one year. This large (n = 4,570) longitudinal study focuses on older adults, a population with many health needs and among whom eHealth use may be particularly important. With three measurement points over the course of a year, this study examined the effects of utilitarian and hedonic beliefs on the continued use of an eHealth newsletter using constructs from IT adoption and continuance theories. Additional analyses compared the relative strength of intentions compared to earlier use in predicting later use. Usage intention was strongly predicted by both hedonic beliefs and utilitarian beliefs. In addition, utilitarian beliefs had both direct effects on intention and indirect effects, mediated by hedonic beliefs. While intention predicted subsequent use, earlier use was a significantly stronger predictor of use than intention. These findings make a theoretical contribution to an emerging literature by shedding light on the complex interplay of reasoned action and automaticity in the context of eHealth continuance.

PREDICTING ABUSE POTENTIAL OF STIMULANTS AND OTHER DOPAMINERGIC DRUGS: OVERVIEW AND RECOMMENDATIONS

PubMed Central

Huskinson, Sally L.; Naylor, Jennifer E.; Rowlett, James K.; Freeman, Kevin B.

2014-01-01

Examination of a drug’s abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. PMID:24662599

Mechanism-based classification of PAH mixtures to predict carcinogenic potential

DOE PAGES

Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; ...

2015-04-22

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[ a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[ def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profilesmore » measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.« less

Predicting Reading Growth with Event-Related Potentials: Thinking Differently about Indexing “Responsiveness”

PubMed Central

Lemons, Christopher J.; Key, Alexandra P.F.; Fuchs, Douglas; Yoder, Paul J.; Fuchs, Lynn S.; Compton, Donald L.; Williams, Susan M.; Bouton, Bobette

2009-01-01

The purpose of this study was to determine if event-related potential (ERP) data collected during three reading-related tasks (Letter Sound Matching, Nonword Rhyming, and Nonword Reading) could be used to predict short-term reading growth on a curriculum-based measure of word identification fluency over 19 weeks in a sample of 29 first-grade children. Results indicate that ERP responses to the Letter Sound Matching task were predictive of reading change and remained so after controlling for two previously validated behavioral predictors of reading, Rapid Letter Naming and Segmenting. ERP data for the other tasks were not correlated with reading change. The potential for cognitive neuroscience to enhance current methods of indexing responsiveness in a response-to-intervention (RTI) model is discussed. PMID:20514353

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

PubMed Central

Fontaine, Bertrand; Peña, José Luis; Brette, Romain

2014-01-01

Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo. PMID:24722397

Computationally predicted IgE epitopes of walnut allergens contribute to cross-reactivity with peanuts

PubMed Central

Maleki, Soheila J.; Teuber, Suzanne S.; Cheng, Hsiaopo; Chen, Deliang; Comstock, Sarah S.; Ruan, Sanbao; Schein, Catherine H.

2011-01-01

Background Cross reactivity between peanuts and tree nuts implies that similar IgE epitopes are present in their proteins. Objective To determine whether walnut sequences similar to known peanut IgE binding sequences, according to the property distance (PD) scale implemented in the Structural Database of Allergenic Proteins (SDAP), react with IgE from sera of patients with allergy to walnut and/or peanut. Methods Patient sera were characterized by Western blotting for IgE-binding to nut protein extracts, and to peptides from walnut and peanut allergens, similar to known peanut epitopes as defined by low PD values, synthesized on membranes. Competitive ELISA was used to show that peanut and predicted walnut epitope sequences compete with purified Ara h 2 for binding to IgE in serum from a cross-reactive patient. Results Sequences from the vicilin walnut allergen Jug r 2 which had low PD values to epitopes of the peanut allergen Ara h 2, a 2s-albumin, bound IgE in sera from five patients who reacted to either walnut, peanut or both. A walnut epitope recognized by 6 patients mapped to a surface-exposed region on a model of the N-terminal pro-region of Jug r 2. A predicted walnut epitope competed for IgE binding to Ara h 2 in serum as well as the known IgE epitope from Ara h 2. Conclusions Sequences with low PD value (<8.5) to known IgE epitopes could contribute to cross-reactivity between allergens. This further validates the PD scoring method for predicting cross-reactive epitopes in allergens. PMID:21883278

Predicting wildfire ignitions, escapes, and large fire activity using Predictive Service’s 7-Day Fire Potential Outlook in the western USA

Treesearch

Karin L. Riley; Crystal Stonesifer; Haiganoush Preisler; Dave Calkin

2014-01-01

Can fire potential forecasts assist with pre-positioning of fire suppression resources, which could result in a cost savings to the United States government? Here, we present a preliminary assessment of the 7-Day Fire Potential Outlook forecasts made by the Predictive Services program. We utilized historical fire occurrence data and archived forecasts to assess how...

Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules

PubMed Central

Desai, Aarti; Singh, Vivek K.; Jere, Abhay

2016-01-01

Introduction Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage. Results The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably). Conclusions Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing. PMID:27271321

Molecular effective coverage surface area of optical clearing agents for predicting optical clearing potential

NASA Astrophysics Data System (ADS)

Feng, Wei; Ma, Ning; Zhu, Dan

2015-03-01

The improvement of methods for optical clearing agent prediction exerts an important impact on tissue optical clearing technique. The molecular dynamic simulation is one of the most convincing and simplest approaches to predict the optical clearing potential of agents by analyzing the hydrogen bonds, hydrogen bridges and hydrogen bridges type forming between agents and collagen. However, the above analysis methods still suffer from some problem such as analysis of cyclic molecule by reason of molecular conformation. In this study, a molecular effective coverage surface area based on the molecular dynamic simulation was proposed to predict the potential of optical clearing agents. Several typical cyclic molecules, fructose, glucose and chain molecules, sorbitol, xylitol were analyzed by calculating their molecular effective coverage surface area, hydrogen bonds, hydrogen bridges and hydrogen bridges type, respectively. In order to verify this analysis methods, in vitro skin samples optical clearing efficacy were measured after 25 min immersing in the solutions, fructose, glucose, sorbitol and xylitol at concentration of 3.5 M using 1951 USAF resolution test target. The experimental results show accordance with prediction of molecular effective coverage surface area. Further to compare molecular effective coverage surface area with other parameters, it can show that molecular effective coverage surface area has a better performance in predicting OCP of agents.

Separate analysis of human papillomavirus E6 and E7 messenger RNAs to predict cervical neoplasia progression

PubMed Central

Liu, Shuling; Lachkar, Bouchra; Zhang, Shuang; Xu, Chenyang; Tenjimbayashi, Yuri; Shikama, Ayumi; Tasaka, Nobutaka; Akiyama, Azusa; Sakurai, Manabu; Nakao, Sari; Ochi, Hiroyuki; Onuki, Mamiko; Matsumoto, Koji; Yoshikawa, Hiroyuki; Satoh, Toyomi

2018-01-01

A few studies previously suggested that human papillomavirus (HPV) E6 messenger RNA (mRNA) may exist uniformly in all grades of cervical intraepithelial neoplasia (CIN), whereas the detection rate of E7 mRNA may increase with disease progression from low-grade CIN to invasive carcinoma. The aim of this study was to clarify the different roles of E6 and E7 mRNAs in cervical carcinogenesis. The presence of each E6 and E7 mRNA was analyzed in 171 patients with pathologically-diagnosed CIN or cervical carcinoma. We utilized a RT-PCR assay based on consensus primers which could detect E6 mRNA (full-length E6/E7 transcript) and E7 mRNAs (spliced E6*/E7 transcripts) separately for various HPV types. E7 mRNAs were detected in 6% of CIN1, 12% of CIN2, 24% of CIN3, and 54% of cervical carcinoma. The presence of E7 mRNAs was significantly associated with progression from low-grade CIN to invasive carcinoma in contrast with E6 mRNA or high-risk HPV (HR-HPV) DNA (p = 0.00011, 0.80 and 0.54). The presence of both E6 and E7 mRNAs was significantly associated with HPV16/18 DNA but not with HR-HPV DNA (p = 0.0079 and 0.21), while the presence of E6 mRNA was significantly associated with HR-HPV DNA but not with HPV16/18 DNA (p = 0.036 and 0.089). The presence of both E6 and E7 mRNAs showed high specificity and low sensitivity (100% and 19%) for detecting CIN2+ by contrast with the positivity for HR-HPV DNA showing low specificity and high sensitivity (19% and 89%). The positive predictive value for detecting CIN2+ was even higher by the presence of both E6 and E7 mRNAs than by the positivity for HR-HPV DNA (100% vs. 91%). In 31 patients followed up for CIN1-2, the presence of both E6 and E7 mRNAs showed significant association with the occurrence of upgraded abnormal cytology in contrast with E6 mRNA, HR-HPV DNA, or HPV16/18 DNA (p = 0.034, 0.73, 0.53, and 0.72). Our findings support previous studies according to which E7 mRNA is more closely involved in cervical carcinogenesis than

Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

PubMed Central

2011-01-01

Background Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element. Results This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by

Reduced model prediction of electron temperature profiles in microtearing-dominated National Spherical Torus eXperiment plasmas

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

Kaye, S. M., E-mail: skaye@pppl.gov; Guttenfelder, W.; Bell, R. E.

A representative H-mode discharge from the National Spherical Torus eXperiment is studied in detail to utilize it as a basis for a time-evolving prediction of the electron temperature profile using an appropriate reduced transport model. The time evolution of characteristic plasma variables such as β{sub e}, ν{sub e}{sup ∗}, the MHD α parameter, and the gradient scale lengths of T{sub e}, T{sub i}, and n{sub e} were examined as a prelude to performing linear gyrokinetic calculations to determine the fastest growing micro instability at various times and locations throughout the discharge. The inferences from the parameter evolutions and the linear stabilitymore » calculations were consistent. Early in the discharge, when β{sub e} and ν{sub e}{sup ∗} were relatively low, ballooning parity modes were dominant. As time progressed and both β{sub e} and ν{sub e}{sup ∗} increased, microtearing became the dominant low-k{sub θ} mode, especially in the outer half of the plasma. There are instances in time and radius, however, where other modes, at higher-k{sub θ}, may, in addition to microtearing, be important for driving electron transport. Given these results, the Rebut-Lallia-Watkins (RLW) electron thermal diffusivity model, which is based on microtearing-induced transport, was used to predict the time-evolving electron temperature across most of the profile. The results indicate that RLW does a good job of predicting T{sub e} for times and locations where microtearing was determined to be important, but not as well when microtearing was predicted to be stable or subdominant.« less

Prediction of Reduction Potentials of Copper Proteins with Continuum Electrostatics and Density Functional Theory

PubMed Central

Fowler, Nicholas J.; Blanford, Christopher F.

2017-01-01

Abstract Blue copper proteins, such as azurin, show dramatic changes in Cu2+/Cu+ reduction potential upon mutation over the full physiological range. Hence, they have important functions in electron transfer and oxidation chemistry and have applications in industrial biotechnology. The details of what determines these reduction potential changes upon mutation are still unclear. Moreover, it has been difficult to model and predict the reduction potential of azurin mutants and currently no unique procedure or workflow pattern exists. Furthermore, high‐level computational methods can be accurate but are too time consuming for practical use. In this work, a novel approach for calculating reduction potentials of azurin mutants is shown, based on a combination of continuum electrostatics, density functional theory and empirical hydrophobicity factors. Our method accurately reproduces experimental reduction potential changes of 30 mutants with respect to wildtype within experimental error and highlights the factors contributing to the reduction potential change. Finally, reduction potentials are predicted for a series of 124 new mutants that have not yet been investigated experimentally. Several mutants are identified that are located well over 10 Å from the copper center that change the reduction potential by more than 85 mV. The work shows that secondary coordination sphere mutations mostly lead to long‐range electrostatic changes and hence can be modeled accurately with continuum electrostatics. PMID:28815759

Predicting the Spatial Distribution of Aspen Growth Potential in the Upper Great Lakes Region

Treesearch

Eric J. Gustafson; Sue M. Lietz; John L. Wright

2003-01-01

One way to increase aspen yields is to produce aspen on sites where aspen growth potential is highest. Aspen growth rates are typically predicted using site index, but this is impractical for landscape-level assessments. We tested the hypothesis that aspen growth can be predicted from site and climate variables and generated a model to map the spatial variability of...

Reduced model prediction of electron temperature profiles in microtearing-dominated National Spherical Torus eXperiment plasmas

NASA Astrophysics Data System (ADS)

Kaye, S. M.; Guttenfelder, W.; Bell, R. E.; Gerhardt, S. P.; LeBlanc, B. P.; Maingi, R.

2014-08-01

A representative H-mode discharge from the National Spherical Torus eXperiment is studied in detail to utilize it as a basis for a time-evolving prediction of the electron temperature profile using an appropriate reduced transport model. The time evolution of characteristic plasma variables such as β e , νe ∗ , the MHD α parameter, and the gradient scale lengths of Te, Ti, and ne were examined as a prelude to performing linear gyrokinetic calculations to determine the fastest growing micro instability at various times and locations throughout the discharge. The inferences from the parameter evolutions and the linear stability calculations were consistent. Early in the discharge, when βe and νe ∗ were relatively low, ballooning parity modes were dominant. As time progressed and both βe and νe ∗ increased, microtearing became the dominant low-kθ mode, especially in the outer half of the plasma. There are instances in time and radius, however, where other modes, at higher-kθ, may, in addition to microtearing, be important for driving electron transport. Given these results, the Rebut-Lallia-Watkins (RLW) electron thermal diffusivity model, which is based on microtearing-induced transport, was used to predict the time-evolving electron temperature across most of the profile. The results indicate that RLW does a good job of predicting Te for times and locations where microtearing was determined to be important, but not as well when microtearing was predicted to be stable or subdominant.

Binding energy of e^+Li using the Peach model potential.

NASA Astrophysics Data System (ADS)

Shertzer, Janine; Ward, Sandra

2006-05-01

The l-independent, parametric model potential developed by Peach^1 for describing the electron interaction with the alkali ion core yields energy levels that are in excellent agreement with experiment. Because of its relative simplicity, this model potential is an attractive choice for studying e^+- Li collisions;^2,3 the e^+-ion core interaction is obtained by changing the sign of the static term in the interaction. In order to test the usefulness of the potential for describing the physics of an effective three-body system, we calculated the binding energy of e^+Li. This is a stringent test, because the system is very weakly bound. Our results are in excellent agreement with previous calculations,^4 including those using the exact four-body Hamiltonian.^5 This work was funded by NSF under collaborative Grant PHYS-0440714 (JS) and PHYS-0440565 (SJW). ^1G. Peach, H.E. Saraph and M.J. Seaton, J. Phys. B 21, 3669 (1988). ^2M.S.T. Watts and J.W. Humberston, J. Phys. B 25, L491 (1992). ^3S. J. Ward and J. Shertzer, Phys. Rev. A 68, 032720 (2003). ^4J. Mitroy, M.W.J. Bromley, and G.G. Ryzhikh, J. Phys. B 35, R81 (2002). ^5Massimo Mella, Gabriele Morosi, and Dario Bressanini, J. Chem. Phys. 111, 108 (1999).

Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

PubMed

Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-06-23

Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd

Nurses' Assessment of Rehabilitation Potential and Prediction of Functional Status at Discharge from Inpatient Rehabilitation

ERIC Educational Resources Information Center

Myers, Jamie S.; Grigsby, Jim; Teel, Cynthia S.; Kramer, Andrew M.

2009-01-01

The goals of this study were to evaluate the accuracy of nurses' predictions of rehabilitation potential in older adults admitted to inpatient rehabilitation facilities and to ascertain whether the addition of a measure of executive cognitive function would enhance predictive accuracy. Secondary analysis was performed on prospective data collected…

Quinone 1 e – and 2 e – /2 H + Reduction Potentials: Identification and Analysis of Deviations from Systematic Scaling Relationships

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

Huynh, Mioy T.; Anson, Colin W.; Cavell, Andrew C.

Quinones participate in diverse electron transfer and proton-coupled electron transfer processes in chemistry and biology. An experimental study of common quinones reveals a non-linear correlation between the 1 e – and 2 e –/2 H + reduction potentials. This unexpected observation prompted a computational study of 128 different quinones, probing their 1 e – reduction potentials, pKa values, and 2 e –/2 H + reduction potentials. The density functional theory calculations reveal an approximately linear correlation between these three properties and an effective Hammett constant associated with the quinone substituent(s). However, deviations from this linear scaling relationship are evident formore » quinones that feature halogen substituents, charged substituents, intramolecular hydrogen bonding in the hydroquinone, and/or sterically bulky substituents. These results, particularly the different substituent effects on the 1 e – versus 2 e – /2 H + reduction potentials, have important implications for designing quinones with tailored redox properties.« less

Predicting abuse potential of stimulants and other dopaminergic drugs: overview and recommendations.

PubMed

Huskinson, Sally L; Naylor, Jennifer E; Rowlett, James K; Freeman, Kevin B

2014-12-01

Examination of a drug's abuse potential at multiple levels of analysis (molecular/cellular action, whole-organism behavior, epidemiological data) is an essential component to regulating controlled substances under the Controlled Substances Act (CSA). We reviewed studies that examined several central nervous system (CNS) stimulants, focusing on those with primarily dopaminergic actions, in drug self-administration, drug discrimination, and physical dependence. For drug self-administration and drug discrimination, we distinguished between experiments conducted with rats and nonhuman primates (NHP) to highlight the common and unique attributes of each model in the assessment of abuse potential. Our review of drug self-administration studies suggests that this procedure is important in predicting abuse potential of dopaminergic compounds, but there were many false positives. We recommended that tests to determine how reinforcing a drug is relative to a known drug of abuse may be more predictive of abuse potential than tests that yield a binary, yes-or-no classification. Several false positives also occurred with drug discrimination. With this procedure, we recommended that future research follow a standard decision-tree approach that may require examining the drug being tested for abuse potential as the training stimulus. This approach would also allow several known drugs of abuse to be tested for substitution, and this may reduce false positives. Finally, we reviewed evidence of physical dependence with stimulants and discussed the feasibility of modeling these phenomena in nonhuman animals in a rational and practical fashion. This article is part of the Special Issue entitled 'CNS Stimulants'. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting and mapping malaria under climate change scenarios: the potential redistribution of malaria vectors in Africa.

PubMed

Tonnang, Henri E Z; Kangalawe, Richard Y M; Yanda, Pius Z

2010-04-23

Malaria is rampant in Africa and causes untold mortality and morbidity. Vector-borne diseases are climate sensitive and this has raised considerable concern over the implications of climate change on future disease risk. The problem of malaria vectors (Anopheles mosquitoes) shifting from their traditional locations to invade new zones is an important concern. The vision of this study was to exploit the sets of information previously generated by entomologists, e.g. on geographical range of vectors and malaria distribution, to build models that will enable prediction and mapping the potential redistribution of Anopheles mosquitoes in Africa. The development of the modelling tool was carried out through calibration of CLIMEX parameters. The model helped estimate the potential geographical distribution and seasonal abundance of the species in relation to climatic factors. These included temperature, rainfall and relative humidity, which characterized the living environment for Anopheles mosquitoes. The same parameters were used in determining the ecoclimatic index (EI). The EI values were exported to a GIS package for special analysis and proper mapping of the potential future distribution of Anopheles gambiae and Anophles arabiensis within the African continent under three climate change scenarios. These results have shown that shifts in these species boundaries southward and eastward of Africa may occur rather than jumps into quite different climatic environments. In the absence of adequate control, these predictions are crucial in understanding the possible future geographical range of the vectors and the disease, which could facilitate planning for various adaptation options. Thus, the outputs from this study will be helpful at various levels of decision making, for example, in setting up of an early warning and sustainable strategies for climate change and climate change adaptation for malaria vectors control programmes in Africa.

Progastrin: a potential predictive marker of liver metastasis in colorectal cancer.

PubMed

Westwood, David A; Patel, Oneel; Christophi, Christopher; Shulkes, Arthur; Baldwin, Graham S

2017-07-01

Staging of colorectal cancer often fails to discriminate outcomes of patients with morphologically similar tumours that exhibit different clinical behaviours. Data from several studies suggest that the gastrin family of growth factors potentiates colorectal cancer tumourigenesis. The aim of this study was to investigate whether progastrin expression may predict clinical outcome in colorectal cancer. Patients with colorectal adenocarcinoma of identical depth of invasion who had not received neoadjuvant therapy were included. The patients either had stage IIa disease with greater than 3-year disease-free survival without adjuvant therapy or stage IV disease with liver metastases on staging CT. Progastrin expression in tumour sections was scored with reference to the intensity and area of immunohistochemical staining. Progastrin expression by stage IV tumours was significantly greater than stage IIa tumours with mean progastrin immunopositivity scores of 2.1 ± 0.2 versus 0.5 ± 0.2, respectively (P < 0.001). This is the first study to show that progastrin expression may be predictive of aggressive tumour behaviour in patients with colorectal cancer and supports its clinical relevance and potential use as a biomarker.

Bioaccumulation Assessment using Predictive Approaches

EPA Science Inventory

Mandated efforts to assess chemicals for their potential to bioaccumulate within the environment are increasingly moving into the realm of data inadequacy. Consequently, there is an increasing reliance on predictive tools to complete regulatory requirements in a timely and cost-e...

Reduced model prediction of electron temperature profiles in microtearing-dominated National Spherical Torus eXperiment plasmas

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

Kaye, S. M.; Guttenfelder, W.; Bell, R. E.

A representative H-mode discharge from the National Spherical Torus eXperiment is studied in detail to utilize it as a basis for a time-evolving prediction of the electron temperature profile using an appropriate reduced transport model. The time evolution of characteristic plasma variables such as βe, ν*e, the MHD α parameter, and the gradient scale lengths of Te, Ti, and ne were examined as a prelude to performing linear gyrokinetic calculations to determine the fastest growing micro instability at various times and locations throughout the discharge. The inferences from the parameter evolutions and the linear stability calculations were consistent. Early inmore » the discharge, when βe and ν*e were relatively low, ballooning parity modes were dominant. As time progressed and both βe and ν*e increased, microtearing became the dominant low-κθ mode, especially in the outer half of the plasma. There are instances in time and radius, however, where other modes, at higher-κθ, may, in addition to microtearing, be important for driving electron transport. Given these results, the Rebut-Lallia-Watkins (RLW) electron thermal diffusivity model, which is based on microtearing-induced transport, was used to predict the time-evolving electron temperature across most of the profile. The results indicate that RLW does a good job of predicting Te for times and locations where microtearing was determined to be important, but not as well when microtearing was predicted to be stable or subdominant.« less

Prediction of Reduction Potentials of Copper Proteins with Continuum Electrostatics and Density Functional Theory.

PubMed

Fowler, Nicholas J; Blanford, Christopher F; Warwicker, Jim; de Visser, Sam P

2017-11-02

Blue copper proteins, such as azurin, show dramatic changes in Cu 2+ /Cu + reduction potential upon mutation over the full physiological range. Hence, they have important functions in electron transfer and oxidation chemistry and have applications in industrial biotechnology. The details of what determines these reduction potential changes upon mutation are still unclear. Moreover, it has been difficult to model and predict the reduction potential of azurin mutants and currently no unique procedure or workflow pattern exists. Furthermore, high-level computational methods can be accurate but are too time consuming for practical use. In this work, a novel approach for calculating reduction potentials of azurin mutants is shown, based on a combination of continuum electrostatics, density functional theory and empirical hydrophobicity factors. Our method accurately reproduces experimental reduction potential changes of 30 mutants with respect to wildtype within experimental error and highlights the factors contributing to the reduction potential change. Finally, reduction potentials are predicted for a series of 124 new mutants that have not yet been investigated experimentally. Several mutants are identified that are located well over 10 Å from the copper center that change the reduction potential by more than 85 mV. The work shows that secondary coordination sphere mutations mostly lead to long-range electrostatic changes and hence can be modeled accurately with continuum electrostatics. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis.

PubMed

Wang, QuanQiu; Xu, Rong

2017-07-01

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0

Characterization of the glass transition of water predicted by molecular dynamics simulations using nonpolarizable intermolecular potentials.

PubMed

Kreck, Cara A; Mancera, Ricardo L

2014-02-20

Molecular dynamics simulations allow detailed study of the experimentally inaccessible liquid state of supercooled water below its homogeneous nucleation temperature and the characterization of the glass transition. Simple, nonpolarizable intermolecular potentials are commonly used in classical molecular dynamics simulations of water and aqueous systems due to their lower computational cost and their ability to reproduce a wide range of properties. Because the quality of these predictions varies between the potentials, the predicted glass transition of water is likely to be influenced by the choice of potential. We have thus conducted an extensive comparative investigation of various three-, four-, five-, and six-point water potentials in both the NPT and NVT ensembles. The T(g) predicted from NPT simulations is strongly correlated with the temperature of minimum density, whereas the maximum in the heat capacity plot corresponds to the minimum in the thermal expansion coefficient. In the NVT ensemble, these points are instead related to the maximum in the internal pressure and the minimum of its derivative, respectively. A detailed analysis of the hydrogen-bonding properties at the glass transition reveals that the extent of hydrogen-bonds lost upon the melting of the glassy state is related to the height of the heat capacity peak and varies between water potentials.

Anesthetic level prediction using a QCM based E-nose.

PubMed

Saraoğlu, H M; Ozmen, A; Ebeoğlu, M A

2008-06-01

Anesthetic level measurement is a real time process. This paper presents a new method to measure anesthesia level in surgery rooms at hospitals using a QCM based E-Nose. The E-Nose system contains an array of eight different coated QCM sensors. In this work, the best linear reacting sensor is selected from the array and used in the experiments. Then, the sensor response time was observed about 15 min using classic method, which is impractical for on-line anesthetic level detection during a surgery. Later, the sensor transition data is analyzed to reach a decision earlier than the classical method. As a result, it is found out that the slope of transition data gives valuable information to predict the anesthetic level. With this new method, we achieved to find correct anesthetic levels within 100 s.

Predicting the potential distribution of the amphibian pathogen Batrachochytrium dendrobatidis in East and Southeast Asia.

PubMed

Moriguchi, Sachiko; Tominaga, Atsushi; Irwin, Kelly J; Freake, Michael J; Suzuki, Kazutaka; Goka, Koichi

2015-04-08

Batrachochytrium dendrobatidis (Bd) is the pathogen responsible for chytridiomycosis, a disease that is associated with a worldwide amphibian population decline. In this study, we predicted the potential distribution of Bd in East and Southeast Asia based on limited occurrence data. Our goal was to design an effective survey area where efforts to detect the pathogen can be focused. We generated ecological niche models using the maximum-entropy approach, with alleviation of multicollinearity and spatial autocorrelation. We applied eigenvector-based spatial filters as independent variables, in addition to environmental variables, to resolve spatial autocorrelation, and compared the model's accuracy and the degree of spatial autocorrelation with those of a model estimated using only environmental variables. We were able to identify areas of high suitability for Bd with accuracy. Among the environmental variables, factors related to temperature and precipitation were more effective in predicting the potential distribution of Bd than factors related to land use and cover type. Our study successfully predicted the potential distribution of Bd in East and Southeast Asia. This information should now be used to prioritize survey areas and generate a surveillance program to detect the pathogen.

Exploring the Predictive Validity of the Susceptibility to Smoking Construct for Tobacco Cigarettes, Alternative Tobacco Products, and E-Cigarettes.

PubMed

Cole, Adam G; Kennedy, Ryan David; Chaurasia, Ashok; Leatherdale, Scott T

2017-12-06

Within tobacco prevention programming, it is useful to identify youth that are at risk for experimenting with various tobacco products and e-cigarettes. The susceptibility to smoking construct is a simple method to identify never-smoking students that are less committed to remaining smoke-free. However, the predictive validity of this construct has not been tested within the Canadian context or for the use of other tobacco products and e-cigarettes. This study used a large, longitudinal sample of secondary school students that reported never using tobacco cigarettes and non-current use of alternative tobacco products or e-cigarettes at baseline in Ontario, Canada. The sensitivity, specificity, and positive and negative predictive values of the susceptibility construct for predicting tobacco cigarette, e-cigarette, cigarillo or little cigar, cigar, hookah, and smokeless tobacco use one and two years after baseline measurement were calculated. At baseline, 29.4% of the sample was susceptible to future tobacco product or e-cigarette use. The sensitivity of the construct ranged from 43.2% (smokeless tobacco) to 59.5% (tobacco cigarettes), the specificity ranged from 70.9% (smokeless tobacco) to 75.9% (tobacco cigarettes), and the positive predictive value ranged from 2.6% (smokeless tobacco) to 32.2% (tobacco cigarettes). Similar values were calculated for each measure of the susceptibility construct. A significant number of youth that did not currently use tobacco products or e-cigarettes at baseline reported using tobacco products and e-cigarettes over a two-year follow-up period. The predictive validity of the susceptibility construct was high and the construct can be used to predict other tobacco product and e-cigarette use among youth. This study presents the predictive validity of the susceptibility construct for the use of tobacco cigarettes among secondary school students in Ontario, Canada. It also presents a novel use of the susceptibility construct for

Computational Prediction of the Immunomodulatory Potential of RNA Sequences.

PubMed

Nagpal, Gandharva; Chaudhary, Kumardeep; Dhanda, Sandeep Kumar; Raghava, Gajendra Pal Singh

2017-01-01

Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

PubMed

Lu, Yao; Deng, Jingyuan; Rhodes, Judith C; Lu, Hui; Lu, Long Jason

2014-06-01

Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation". In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to

Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

NASA Astrophysics Data System (ADS)

Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

2017-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of

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

Towards prediction of heatwaves based on the complementary relationship between actual and potential evaporation - energy partitioning and hydrologic attributes

NASA Astrophysics Data System (ADS)

Or, D.; Aminzadeh, M.; Roderick, M. L.

2017-12-01

Prediction of extreme climate events such as heatwaves that are characterized by prolonged periods of high air temperatures (accompanied by low precipitation and high radiation) provides an opportunity to potentially mitigate the associated environmental, social and economic impacts. Vegetation may respond to these extreme conditions by reducing evaporative flux either due to soil water depletion or inability to meet the atmospheric evaporative demand (high canopy resistance). We implement a newly generalized Complementary Relationship (CR) for spatially heterogeneous land surfaces to predict the actual evaporation from drying landscapes covered by different vegetation types (i.e., grassland and forest). A strong correlation between air temperature and sensible heat flux anomalies identified from FLUXNET network data suggests that abrupt changes in sensible heat flux above climatological means can serve as indicators for predicting the onset of a heatwave. We thus capitalize on the inherent coupling between evaporative and sensible heat fluxes linked to moisture availability within the CR framework to predict rapid increase in regional sensible heat flux associated with soil drying (low precipitation) or with extreme evaporative demand (high radiation) while soil moisture is not limiting. The proposed approach evaluated using FLUXNET datasets provides an energy constraint framework based on the CR concept to obtain new insights into the onset of heatwaves and climate extremes such as regional droughts.

In silico and in vitro prediction of gastrointestinal absorption from potential drug eremantholide C.

PubMed

Caldeira, Tamires G; Saúde-Guimarães, Dênia A; Dezani, André B; Serra, Cristina Helena Dos Reis; de Souza, Jacqueline

2017-11-01

Analysis of the biopharmaceutical properties of eremantholide C, sesquiterpene lactone with proven pharmacological activity and low toxicity, is required to evaluate its potential to become a drug. Preliminary analysis of the physicochemical characteristics of eremantholide C was performed in silico. Equilibrium solubility was evaluated using the shake-flask method, at 37.0 °C, 100 rpm during 72 h in biorelevant media. The permeability was analysed using parallel artificial membrane permeability assay, at 37.0 °C, 50 rpm for 5 h. The donor compartment was composed of an eremantholide C solution in intestinal fluid simulated without enzymes, while the acceptor compartment consisted of phosphate buffer. Physicochemical characteristics predicted in silico indicated that eremantholide C has a low solubility and high permeability. In-vitro data of eremantholide C showed low solubility, with values for the dose/solubility ratio (ml): 9448.82, 10 389.61 e 15 000.00 for buffers acetate (pH 4.5), intestinal fluid simulated without enzymes (pH 6.8) and phosphate (pH 7.4), respectively. Also, it showed high permeability, with effective permeability of 30.4 × 10 -6 cm/s, a higher result compared with propranolol hydrochloride (9.23 × 10 -6 cm/s). The high permeability combined with its solubility, pharmacological activity and low toxicity demonstrate the importance of eremantholide C as a potential drug candidate. © 2017 Royal Pharmaceutical Society.

Do resting brain dynamics predict oddball evoked-potential?

PubMed Central

2011-01-01

Background The oddball paradigm is widely applied to the investigation of cognitive function in neuroscience and in neuropsychiatry. Whether cortical oscillation in the resting state can predict the elicited oddball event-related potential (ERP) is still not clear. This study explored the relationship between resting electroencephalography (EEG) and oddball ERPs. The regional powers of 18 electrodes across delta, theta, alpha and beta frequencies were correlated with the amplitude and latency of N1, P2, N2 and P3 components of oddball ERPs. A multivariate analysis based on partial least squares (PLS) was applied to further examine the spatial pattern revealed by multiple correlations. Results Higher synchronization in the resting state, especially at the alpha spectrum, is associated with higher neural responsiveness and faster neural propagation, as indicated by the higher amplitude change of N1/N2 and shorter latency of P2. None of the resting quantitative EEG indices predict P3 latency and amplitude. The PLS analysis confirms that the resting cortical dynamics which explains N1/N2 amplitude and P2 latency does not show regional specificity, indicating a global property of the brain. Conclusions This study differs from previous approaches by relating dynamics in the resting state to neural responsiveness in the activation state. Our analyses suggest that the neural characteristics carried by resting brain dynamics modulate the earlier/automatic stage of target detection. PMID:22114868

A review of biomarkers for predicting clinical reactivity to foods with a focus on specific immunoglobulin E antibodies.

PubMed

Sato, Sakura; Yanagida, Noriyuki; Ohtani, Kiyotaka; Koike, Yumi; Ebisawa, Motohiro

2015-06-01

The purpose of this study is to assess the latest studies that focus on specific immunoglobulin (Ig)E antibodies for predicting clinical reactivity to foods. Persistent hen's egg and cow's milk allergy patients have higher antigen-specific IgE levels at all ages than those who have outgrown these allergies. Recent studies on the natural histories of hen's egg and cow's milk allergies suggested that baseline antigen-specific IgEs are the most important predictors of tolerance. Oral immunotherapy (OIT), which is a novel therapeutic approach for food allergy, requires biomarkers for predicting outcomes after therapy. Several studies indicate that the initial antigen-specific IgE level may be a useful biomarker for the prognosis of OIT. Recently, component-resolved diagnostics (CRD) has been used for food allergy diagnosis. Current studies have suggested that Ara h 2, omega-5 gliadin and ovomucoid are good diagnostic markers for peanut, wheat and egg allergies, respectively. Antigen-specific IgE can be a useful biomarker for predicting clinical reactivity to food allergies. Monitoring hen's egg and cow's milk-specific IgE is useful for predicting prognosis, and baseline specific IgE levels may be associated with the outcome of OIT. The use of CRD provides us with a better tool for diagnosing food allergy.

Recall of Point-of-Sale Marketing Predicts Cigar and E-Cigarette Use among Texas Youth.

PubMed

Pasch, Keryn E; Nicksic, Nicole E; Opara, Samuel C; Jackson, Christian; Harrell, Melissa B; Perry, Cheryl L

2017-10-23

While research has documented associations between recall of point-of sale tobacco marketing and youth tobacco use, much of the research is cross-sectional and focused on cigarettes. The present longitudinal study examined recall of tobacco marketing at the point-of-sale and multiple types of tobacco use six months later. The Texas Adolescent Tobacco Advertising and Marketing Surveillance System (TATAMS) is a large-scale, representative study of 6th, 8th, and 10th graders in 79 middle and high schools in five counties in Texas. Weighted logistic regression examined associations between recall of tobacco advertisements and products on display at baseline and ever use, current use, and susceptibility to use for cigarette, e-cigarette, cigar, and smokeless products six months later. Students' recall of signs marketing e-cigarettes at baseline predicted ever e-cigarette use and increased susceptibility to use e-cigarettes at follow-up across all store types. Recall of e-cigarette displays only predicted susceptibility to use e-cigarettes at follow-up, across all store types. Both recall of signs marketing cigars and cigar product displays predicted current and ever cigar smoking and increased susceptibility to smoking cigars at follow-up, across all store types. Recall of cigarette and smokeless product marketing and displays was not associated with tobacco use measures. The point-of-sale environment continues to be an important influence on youth tobacco use. Restrictions on point-of-sale marketing, particularly around schools, are warranted. Cross-sectional studies have shown that exposure to point-of-sale cigarette marketing is associated with use of cigarettes among youth, though longitudinal evidence of the same is sparse and mixed. Cross-sectional studies have found that recall of cigars, smokeless product, and e-cigarette tobacco marketing at point-of-sale is associated with curiosity about tobacco use or intentions to use tobacco among youth, but limited

Quantification, Prediction, and the Online Impact of Sentence Truth-Value: Evidence From Event-Related Potentials

PubMed Central

2015-01-01

Do negative quantifiers like “few” reduce people’s ability to rapidly evaluate incoming language with respect to world knowledge? Previous research has addressed this question by examining whether online measures of quantifier comprehension match the “final” interpretation reflected in verification judgments. However, these studies confounded quantifier valence with its impact on the unfolding expectations for upcoming words, yielding mixed results. In the current event-related potentials study, participants read negative and positive quantifier sentences matched on cloze probability and on truth-value (e.g., “Most/Few gardeners plant their flowers during the spring/winter for best results”). Regardless of whether participants explicitly verified the sentences or not, true-positive quantifier sentences elicited reduced N400s compared with false-positive quantifier sentences, reflecting the facilitated semantic retrieval of words that render a sentence true. No such facilitation was seen in negative quantifier sentences. However, mixed-effects model analyses (with cloze value and truth-value as continuous predictors) revealed that decreasing cloze values were associated with an interaction pattern between truth-value and quantifier, whereas increasing cloze values were associated with more similar truth-value effects regardless of quantifier. Quantifier sentences are thus understood neither always in 2 sequential stages, nor always in a partial-incremental fashion, nor always in a maximally incremental fashion. Instead, and in accordance with prediction-based views of sentence comprehension, quantifier sentence comprehension depends on incorporation of quantifier meaning into an online, knowledge-based prediction for upcoming words. Fully incremental quantifier interpretation occurs when quantifiers are incorporated into sufficiently strong online predictions for upcoming words. PMID:26375784

Predictive microbiology in food packaging applications

USDA-ARS?s Scientific Manuscript database

Predictive microbiology including growth, inactivation, surface transfer (or cross-contamination), and survival, plays important roles in understanding microbial food safety. Growth models may involve the growth potential of a specified pathogen under different stresses, e.g., temperature, pH, wate...

Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

PubMed

Mirams, Gary R; Davies, Mark R; Brough, Stephen J; Bridgland-Taylor, Matthew H; Cui, Yi; Gavaghan, David J; Abi-Gerges, Najah

2014-01-01

Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety

On the potential use of radar-derived information in operational numerical weather prediction

NASA Technical Reports Server (NTRS)

Mcpherson, R. D.

1986-01-01

Estimates of requirements likely to be levied on a new observing system for mesoscale meteonology are given. Potential observing systems for mesoscale numerical weather prediction are discussed. Thermodynamic profiler radiometers, infrared radiometer atmospheric sounders, Doppler radar wind profilers and surveillance radar, and moisture profilers are among the instruments described.

Adhesion Potential of Intestinal Microbes Predicted by Physico-Chemical Characterization Methods

PubMed Central

Niederberger, Tobias; Fischer, Peter; Rühs, Patrick Alberto

2015-01-01

Bacterial adhesion to epithelial surfaces affects retention time in the human gastro-intestinal tract and therefore significantly contributes to interactions between bacteria and their hosts. Bacterial adhesion among other factors is strongly influenced by physico-chemical factors. The accurate quantification of these physico-chemical factors in adhesion is however limited by the available measuring techniques. We evaluated surface charge, interfacial rheology and tensiometry (interfacial tension) as novel approaches to quantify these interactions and evaluated their biological significance via an adhesion assay using intestinal epithelial surface molecules (IESM) for a set of model organisms present in the human gastrointestinal tract. Strain pairs of Lactobacillus plantarum WCFS1 with its sortase knockout mutant Lb. plantarum NZ7114 and Lb. rhamnosus GG with Lb. rhamnosus DSM 20021T were used with Enterococcus faecalis JH2-2 as control organism. Intra-species comparison revealed significantly higher abilities for Lb. plantarum WCSF1 and Lb. rhamnosus GG vs. Lb. plantarum NZ7114 and Lb. rhamnosus DSM 20021T to dynamically increase interfacial elasticity (10−2 vs. 10−3 Pa*m) and reduce interfacial tension (32 vs. 38 mN/m). This further correlated for Lb. plantarum WCSF1 and Lb. rhamnosus GG vs. Lb. plantarum NZ7114 and Lb. rhamnosus DSM 20021T with the decrease of relative hydrophobicity (80–85% vs. 57–63%), Zeta potential (-2.9 to -4.5 mV vs. -8.0 to -13.8 mV) and higher relative adhesion capacity to IESM (3.0–5.0 vs 1.5–2.2). Highest adhesion to the IESM collagen I and fibronectin was found for Lb. plantarum WCFS1 (5.0) and E. faecalis JH2-2 (4.2) whereas Lb. rhamnosus GG showed highest adhesion to type II mucus (3.8). Significantly reduced adhesion (2 fold) to the tested IESM was observed for Lb. plantarum NZ7114 and Lb. rhamnosus DSM 20021T corresponding with lower relative hydrophobicity, Zeta potential and abilities to modify interfacial

Diagnosis of 25 genotypes of human papillomaviruses for their physical statuses in cervical precancerous/cancerous lesions: a comparison of E2/E6E7 ratio-based vs. multiple E1-L1/E6E7 ratio-based detection techniques.

PubMed

Zhang, Rong; He, Yi-feng; Chen, Mo; Chen, Chun-mei; Zhu, Qiu-jing; Lu, Huan; Wei, Zhen-hong; Li, Fang; Zhang, Xiao-xin; Xu, Cong-jian; Yu, Long

2014-10-02

Cervical lesions caused by integrated human papillomavirus (HPV) infection are highly dangerous because they can quickly develop into invasive cancers. However, clinicians are currently hampered by the lack of a quick, convenient and precise technique to detect integrated/mixed infections of various genotypes of HPVs in the cervix. This study aimed to develop a practical tool to determine the physical status of different HPVs and evaluate its clinical significance. The target population comprised 1162 women with an HPV infection history of > six months and an abnormal cervical cytological finding. The multiple E1-L1/E6E7 ratio analysis, a novel technique, was developed based on determining the ratios of E1/E6E7, E2/E6E7, E4E5/E6E7, L2/E6E7 and L1/E6E7 within the viral genome. Any imbalanced ratios indicate integration. Its diagnostic and predictive performances were compared with those of E2/E6E7 ratio analysis. The detection accuracy of both techniques was evaluated using the gold-standard technique "detection of integrated papillomavirus sequences" (DIPS). To realize a multigenotypic detection goal, a primer and probe library was established. The integration rate of a particular genotype of HPV was correlated with its tumorigenic potential and women with higher lesion grades often carried lower viral loads. The E1-L1/E6E7 ratio analysis achieved 92.7% sensitivity and 99.0% specificity in detecting HPV integration, while the E2/E6E7 ratio analysis showed a much lower sensitivity (75.6%) and a similar specificity (99.3%). Interference due to episomal copies was observed in both techniques, leading to false-negative results. However, some positive results of E1-L1/E6E7 ratio analysis were missed by DIPS due to its stochastic detection nature. The E1-L1/E6E7 ratio analysis is more efficient than E2/E6E7 ratio analysis and DIPS in predicting precancerous/cancerous lesions, in which both positive predictive values (36.7%-82.3%) and negative predictive values (75

On Predicting Form and Meaning in a Second Language

ERIC Educational Resources Information Center

Ito, Aine; Martin, Andrea E.; Nieuwland, Mante S.

2017-01-01

We used event-related potentials (ERP) to investigate whether Spanish-English bilinguals preactivate form and meaning of predictable words. Participants read high-cloze sentence contexts (e.g., "The student is going to the library to borrow a..."), followed by the predictable word ("book"), a word that was form-related…

Predicting and Modelling the Growth of Potentially Pathogenic Bacteria in Coalho Cheese.

PubMed

de Araújo, Valdenice Gomes; de Oliveira Arruda, Maria Digian; Dantas Duarte, Francisca Nayara; de Sousa, Janaína Maria Batista; da Costa Lima, Maiara; da Conceição, Maria Lúcia; Schaffner, Donald W; de Souza, Evandro Leite

2017-07-01

Coalho is a semihard medium- to high-moisture cheese produced in various states in the northeastern region of Brazil. This study was conducted to predict the growth kinetics (maximum growth rate, Grmax) of Escherichia coli, Listeria monocytogenes, Salmonella, and Staphylococcus aureus using the ComBase predictor with various combinations of temperature, pH, and water activity (a w ) in commercial Coalho cheese samples. The growth of two antibiotic-resistant derivative strains of L. monocytogenes (parental strains ATCC 19115 and ATCC 7644) and S. aureus (parental strains ATCC 13565 and ATCC 19095) was measured in commercial Coalho cheese samples during 14 days of storage as a function of the initial contamination level (3 and 5 log CFU/g) and storage temperature (7.5 and 12°C). The highest Grmax values predicted by ComBase under the various conditions of temperature, pH, and a w were for L. monocytogenes (0.006 to 0.065 log CFU/g/h) and S. aureus (0.003 to 0.048 log CFU/g/h). The Grmax values predicted by ComBase for E. coli and Salmonella were 0.007 to 0.026 and 0.008 to 0.041 log CFU/g/h, respectively. An experimental challenge in Coalho cheese revealed that the populations of all tested antibiotic-resistant derivative strains of L. monocytogenes and S. aureus increased (>0.5 log CFU/g) by day 14 of storage at 7.5 or 12°C. L. monocytogenes and S. aureus had higher Grmax values in cheese samples stored at 12°C than those stored at 7.5°C. The ComBase growth predictions under the temperature, pH, and a w conditions in commercial Coalho cheese samples were generally fail-safe for predicting the growth of L. monocytogenes and S. aureus in the actual product. These results indicate that Coalho cheese has pH and a w characteristics that allow the growth of E. coli, L. monocytogenes, Salmonella, and S. aureus. These cheeses are typically stored at temperatures that do not prevent the growth of these bacteria.

Computer prediction of three-dimensional potential flow fields in which aircraft propellers operate: Computer program description and users manual

NASA Technical Reports Server (NTRS)

Jumper, S. J.

1979-01-01

A method was developed for predicting the potential flow velocity field at the plane of a propeller operating under the influence of a wing-fuselage-cowl or nacelle combination. A computer program was written which predicts the three dimensional potential flow field. The contents of the program, its input data, and its output results are described.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-learning Methods

NASA Astrophysics Data System (ADS)

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-03-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

A Valuable Tool in Predicting Poor Outcome due to Sepsis in Pediatric Intensive Care Unit: Tp-e/QT Ratio.

PubMed

Ozdemir, Rahmi; Isguder, Rana; Kucuk, Mehmet; Karadeniz, Cem; Ceylan, Gokhan; Katipoglu, Nagehan; Yilmazer, Murat Muhtar; Yozgat, Yilmaz; Mese, Timur; Agin, Hasan

2016-10-01

To assess the feasibility of 12-lead electrocardiographic (ECG) measures such as P wave dispersion (PWd), QT interval, QT dispersion (QTd), Tp-e interval, Tp-e/QT and Tp-e/QTc ratio in predicting poor outcome in patients diagnosed with sepsis in pediatric intensive care unit (PICU). Ninety-three patients diagnosed with sepsis, severe sepsis or septic shock and 103 age- and sex-matched healthy children were enrolled into the study. PWd, QT interval, QTd, Tp-e interval and Tp-e/QT, Tp-e/QTc ratios were obtained from a 12-lead electrocardiogram. PWd, QTd, Tp-e interval and Tp-e/QT, Tp-e/QTc ratios were significantly higher in septic patients compared with the controls. During the study period, 41 patients had died. In multivariate logistic regression analyses, only Tp-e/QT ratio was found to be an independent predictor of mortality. The ECG measurements can predict the poor outcome in patients with sepsis. The Tp-e/QT ratio may be a valuable tool in predicting mortality for patients with sepsis in the PICU. © The Author [2016]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

PubMed

Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

2013-01-01

A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P < .001). In the updating process, age, history, and additional candidate predictors did not significantly increase discrimination, being 94%, and leaving only 4 predictors of the original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Known Allergen Structures Predict Schistosoma mansoni IgE-Binding Antigens in Human Infection

PubMed Central

Farnell, Edward J.; Tyagi, Nidhi; Ryan, Stephanie; Chalmers, Iain W.; Pinot de Moira, Angela; Jones, Frances M.; Wawrzyniak, Jakub; Fitzsimmons, Colin M.; Tukahebwa, Edridah M.; Furnham, Nicholas; Maizels, Rick M.; Dunne, David W.

2015-01-01

The IgE response has been associated with both allergic reactions and immunity to metazoan parasites. Recently, we hypothesized that all environmental allergens bear structural homology to IgE-binding antigens from metazoan parasites and that this homology defines the relatively small number of protein families containing allergenic targets. In this study, known allergen structures (Pfam domains) from major environmental allergen families were used to predict allergen-like (SmProfilin, SmVAL-6, SmLipocalin, SmHSP20, Sm triosephosphate isomerase, SmThioredoxin, Sm superoxide dismutase, SmCyclophilin, and Sm phosphoglycerate kinase) and non-allergen-like [Sm dynein light chain (SmDLC), SmAldolase SmAK, SmUbiquitin, and Sm14-3-3] proteins in Schistosoma mansoni. Recombinant antigens were produced in Escherichia coli and IgG1, IgG4, and IgE responses against them measured in a cohort of people (n = 222) infected with S. mansoni. All allergen-like antigens were targeted by IgE responses in infected subjects, whilst IgE responses to the non-allergen-like antigens, SmAK, SmUbiquitin, and Sm14-3-3 were essentially absent being of both low prevalence and magnitude. Two new IgE-binding Pfam domain families, not previously described in allergen family databases, were also found, with prevalent IgE responses against SmDLC (PF01221) and SmAldolase (PF00274). Finally, it was demonstrated that immunoregulatory serological processes typically associated with allergens also occurred in responses to allergen-like proteins in S. mansoni infections, including the production of IgG4 in people responding with IgE and the down-regulation of IgE in response to increased antigen exposure from S. mansoni eggs. This study establishes that structures of known allergens can be used to predict IgE responses against homologous parasite allergen-like molecules (parallergens) and that serological responses with IgE/IgG4 to parallergens mirror those seen against allergens, supporting our

Prediction of tolerance in children with IgE mediated cow's milk allergy by microarray profiling and chemometric approach.

PubMed

Wulfert, F; Sanyasi, G; Tongen, L; Watanabe, L A; Wang, X; Renault, N K; Falcone, F H; Jacob, C M A; Alcocer, M J C

2012-08-31

The sera of a retrospective cohort (n=41) composed of children with well characterized cow's milk allergy collected from multiple visits were analyzed using a protein microarray system measuring four classes of immunoglobulins. The frequency of the visits, age and gender distribution reflected real situation faced by the clinicians at a pediatric reference center for food allergy in São Paulo, Brazil. The profiling array results have shown that total IgG and IgA share similar specificity whilst IgM and in particular IgE are distantly related. The correlation of specificity of IgE and IgA is variable amongst the patients and this relationship cannot be used to predict atopy or the onset of tolerance to milk. The array profiling technique has corroborated the clinical selection criteria for this cohort albeit it clearly suggested that 4 out of the 41 patients might have allergies other than milk origin. There was also a good correlation between the array data and ImmunoCAP results, casein in particular. By using qualitative and quantitative multivariate analysis routines it was possible to produce validated statistical models to predict with reasonable accuracy the onset of tolerance to milk proteins. If expanded to larger study groups, the array profiling in combination with the multivariate techniques show potential to improve the prognostic of milk allergic patients. Copyright © 2012 Elsevier B.V. All rights reserved.

[Predictive factors of anxiety disorders].

PubMed

Domschke, K

2014-10-01

Anxiety disorders are among the most frequent mental disorders in Europe (12-month prevalence 14%) and impose a high socioeconomic burden. The pathogenesis of anxiety disorders is complex with an interaction of biological, environmental and psychosocial factors contributing to the overall disease risk (diathesis-stress model). In this article, risk factors for anxiety disorders will be presented on several levels, e.g. genetic factors, environmental factors, gene-environment interactions, epigenetic mechanisms, neuronal networks ("brain fear circuit"), psychophysiological factors (e.g. startle response and CO2 sensitivity) and dimensional/subclinical phenotypes of anxiety (e.g. anxiety sensitivity and behavioral inhibition), and critically discussed regarding their potential predictive value. The identification of factors predictive of anxiety disorders will possibly allow for effective preventive measures or early treatment interventions, respectively, and reduce the individual patient's suffering as well as the overall socioeconomic burden of anxiety disorders.

Seasonal predictability of Arctic Sea Ice: assessing its limits and potential in a GCM and implications for observations.

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, E.

2012-12-01

Arctic sea ice has exhibited a dramatic decrease both in area and thickness over the recent decades, particularly during the summer months. This decrease has led to growing interest in the potential predictability of summer sea ice, spurred in part by the socioeconomic implications. Here we present results of several parallel experiments designed to assess and understand the limits and potential for seasonal predictability of Arctic sea ice, with an emphasis on the summer minimum. Building on our experience from the SEARCH Outlook, we present results of a coupled general circulation model (GCM) hindcast simulation of Arctic summer sea ice variability for the satellite period (1979-present). These are initialized with spring sea ice volume anomalies obtained from a modelling and assimilation system, considered to be a close representation of reality. We show that there is significant predictability, yet the stochastic forcing imparted mainly by the atmosphere can lead to large errors in the hindcast. The model, however, can simulate anomalous runs that lie beyond a Gaussian distribution. Additionally, we investigate the regional characteristics of predictability and its links to sea ice dynamics and the spatio-temporal behavior of sea ice anomalies. We show a distinct difference between models. Unfortunately, observational data of thickness are not yet detailed enough to assess the models. Our results indicate the potential for detailed ice thickness observations in improving regional predictability. Finally, we discuss the importance of experiment design in predictability experiments, and show that predictions made with models that have a large mean state bias in sea ice require a careful initialization in order to fully capture all initial value predictability.

The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows.

PubMed

McParland, S; Berry, D P

2016-05-01

Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in

Peak-summer East Asian rainfall predictability and prediction part II: extratropical East Asia

NASA Astrophysics Data System (ADS)

Yim, So-Young; Wang, Bin; Xing, Wen

2016-07-01

The part II of the present study focuses on northern East Asia (NEA: 26°N-50°N, 100°-140°E), exploring the source and limit of the predictability of the peak summer (July-August) rainfall. Prediction of NEA peak summer rainfall is extremely challenging because of the exposure of the NEA to midlatitude influence. By examining four coupled climate models' multi-model ensemble (MME) hindcast during 1979-2010, we found that the domain-averaged MME temporal correlation coefficient (TCC) skill is only 0.13. It is unclear whether the dynamical models' poor skills are due to limited predictability of the peak-summer NEA rainfall. In the present study we attempted to address this issue by applying predictable mode analysis method using 35-year observations (1979-2013). Four empirical orthogonal modes of variability and associated major potential sources of variability are identified: (a) an equatorial western Pacific (EWP)-NEA teleconnection driven by EWP sea surface temperature (SST) anomalies, (b) a western Pacific subtropical high and Indo-Pacific dipole SST feedback mode, (c) a central Pacific-El Nino-Southern Oscillation mode, and (d) a Eurasian wave train pattern. Physically meaningful predictors for each principal component (PC) were selected based on analysis of the lead-lag correlations with the persistent and tendency fields of SST and sea-level pressure from March to June. A suite of physical-empirical (P-E) models is established to predict the four leading PCs. The peak summer rainfall anomaly pattern is then objectively predicted by using the predicted PCs and the corresponding observed spatial patterns. A 35-year cross-validated hindcast over the NEA yields a domain-averaged TCC skill of 0.36, which is significantly higher than the MME dynamical hindcast (0.13). The estimated maximum potential attainable TCC skill averaged over the entire domain is around 0.61, suggesting that the current dynamical prediction models may have large rooms to improve

Prediction of Binding Energy of Keap1 Interaction Motifs in the Nrf2 Antioxidant Pathway and Design of Potential High-Affinity Peptides.

PubMed

Karttunen, Mikko; Choy, Wing-Yiu; Cino, Elio A

2018-06-07

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor and principal regulator of the antioxidant pathway. The Kelch domain of Kelch-like ECH-associated protein 1 (Keap1) binds to motifs in the N-terminal region of Nrf2, promoting its degradation. There is interest in developing ligands that can compete with Nrf2 for binding to Kelch, thereby activating its transcriptional activities and increasing antioxidant levels. Using experimental Δ G bind values of Kelch-binding motifs determined previously, a revised hydrophobicity-based model was developed for estimating Δ G bind from amino acid sequence and applied to rank potential uncharacterized Kelch-binding motifs identified from interaction databases and BLAST searches. Model predictions and molecular dynamics (MD) simulations suggested that full-length MAD2A binds Kelch more favorably than a high-affinity 20-mer Nrf2 E78P peptide, but that the motif in isolation is not a particularly strong binder. Endeavoring to develop shorter peptides for activating Nrf2, new designs were created based on the E78P peptide, some of which showed considerable propensity to form binding-competent structures in MD, and were predicted to interact with Kelch more favorably than the E78P peptide. The peptides could be promising new ligands for enhancing the oxidative stress response.

A Review of Auditory Prediction and Its Potential Role in Tinnitus Perception.

PubMed

Durai, Mithila; O'Keeffe, Mary G; Searchfield, Grant D

2018-06-01

The precise mechanisms underlying tinnitus perception and distress are still not fully understood. A recent proposition is that auditory prediction errors and related memory representations may play a role in driving tinnitus perception. It is of interest to further explore this. To obtain a comprehensive narrative synthesis of current research in relation to auditory prediction and its potential role in tinnitus perception and severity. A narrative review methodological framework was followed. The key words Prediction Auditory, Memory Prediction Auditory, Tinnitus AND Memory, Tinnitus AND Prediction in Article Title, Abstract, and Keywords were extensively searched on four databases: PubMed, Scopus, SpringerLink, and PsychINFO. All study types were selected from 2000-2016 (end of 2016) and had the following exclusion criteria applied: minimum age of participants <18, nonhuman participants, and article not available in English. Reference lists of articles were reviewed to identify any further relevant studies. Articles were short listed based on title relevance. After reading the abstracts and with consensus made between coauthors, a total of 114 studies were selected for charting data. The hierarchical predictive coding model based on the Bayesian brain hypothesis, attentional modulation and top-down feedback serves as the fundamental framework in current literature for how auditory prediction may occur. Predictions are integral to speech and music processing, as well as in sequential processing and identification of auditory objects during auditory streaming. Although deviant responses are observable from middle latency time ranges, the mismatch negativity (MMN) waveform is the most commonly studied electrophysiological index of auditory irregularity detection. However, limitations may apply when interpreting findings because of the debatable origin of the MMN and its restricted ability to model real-life, more complex auditory phenomenon. Cortical oscillatory

Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.

PubMed

Suo, Chen; Hrydziuszko, Olga; Lee, Donghwan; Pramana, Setia; Saputra, Dhany; Joshi, Himanshu; Calza, Stefano; Pawitan, Yudi

2015-08-15

Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/. yudi.pawitan@ki.se Supplementary data are

Geostatistical enhancement of european hydrological predictions

NASA Astrophysics Data System (ADS)

Pugliese, Alessio; Castellarin, Attilio; Parajka, Juraj; Arheimer, Berit; Bagli, Stefano; Mazzoli, Paolo; Montanari, Alberto; Blöschl, Günter

2016-04-01

Geostatistical Enhancement of European Hydrological Prediction (GEEHP) is a research experiment developed within the EU funded SWITCH-ON project, which proposes to conduct comparative experiments in a virtual laboratory in order to share water-related information and tackle changes in the hydrosphere for operational needs (http://www.water-switch-on.eu). The main objective of GEEHP deals with the prediction of streamflow indices and signatures in ungauged basins at different spatial scales. In particular, among several possible hydrological signatures we focus in our experiment on the prediction of flow-duration curves (FDCs) along the stream-network, which has attracted an increasing scientific attention in the last decades due to the large number of practical and technical applications of the curves (e.g. hydropower potential estimation, riverine habitat suitability and ecological assessments, etc.). We apply a geostatistical procedure based on Top-kriging, which has been recently shown to be particularly reliable and easy-to-use regionalization approach, employing two different type of streamflow data: pan-European E-HYPE simulations (http://hypeweb.smhi.se/europehype) and observed daily streamflow series collected in two pilot study regions, i.e. Tyrol (merging data from Austrian and Italian stream gauging networks) and Sweden. The merger of the two study regions results in a rather large area (~450000 km2) and might be considered as a proxy for a pan-European application of the approach. In a first phase, we implement a bidirectional validation, i.e. E-HYPE catchments are set as training sites to predict FDCs at the same sites where observed data are available, and vice-versa. Such a validation procedure reveals (1) the usability of the proposed approach for predicting the FDCs over the entire river network of interest using alternatively observed data and E-HYPE simulations and (2) the accuracy of E-HYPE-based predictions of FDCs in ungauged sites. In a

Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.

PubMed

Fang, Jiansong; Yang, Ranyao; Gao, Li; Zhou, Dan; Yang, Shengqian; Liu, Ai-Lin; Du, Guan-hua

2013-11-25

Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

PubMed Central

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-01-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist. PMID:29651416

The predictive role of E/e' on ischemic stroke and atrial fibrillation in Japanese patients without atrial fibrillation.

PubMed

Arai, Riku; Suzuki, Shinya; Semba, Hiroaki; Arita, Takuto; Yagi, Naoharu; Otsuka, Takayuki; Sagara, Koichi; Sasaki, Kenichi; Kano, Hiroto; Matsuno, Shunsuke; Kato, Yuko; Uejima, Tokuhisa; Oikawa, Yuji; Kunihara, Takashi; Yajima, Junji; Yamashita, Takeshi

2018-07-01

The predictive role of E/e' on ischemic stroke (IS) and atrial fibrillation (AF) in Japanese patients without AF are unclear. Shinken database includes all the new patients visiting the Cardiovascular Institute Hospital in Tokyo, Japan. E/e' has been routinely measured since 2007. Patients without AF for whom E/e' was measured at the initial visit between 2007 and 2014 (n=11 477, mean age 57.2 years old, men 59.5%) were divided into E/e' tertiles (<8.04, 8.04-11.00, >11.00). During the mean follow-up period of 1.8 years, 58 IS and 140 new appearances of AF were observed. High E/e' tertile was associated with more prevalence of atherothrombotic risks. The cumulative incidence of IS events and new appearance of AF at 6 years in low, middle, and high E/e' tertiles were 0.5%, 1.4%, and 3.0%/year (log-rank test, p<0.001), and 2.5%, 2.9%, and 4.2%/year (log-rank test, p=0.007), respectively. In multivariate analysis, high E/e' tertile was independently associated with IS (HR, 2.857, 95%CI 1.257-6.495, p=0.012). Although high E/e' tertile was independently associated with new appearance of AF when adjusted for coexistence of atherothrombotic risk factors (HR, 1.694, 95%CI, 1.097-2.616, p=0.017), the association was attenuated after adjustment for left atrial dimension. E/e' was significantly associated with incidence of IS and new appearance of AF in non-AF patients. Copyright © 2018 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Using Predictive Analytics to Predict Power Outages from Severe Weather

NASA Astrophysics Data System (ADS)

Wanik, D. W.; Anagnostou, E. N.; Hartman, B.; Frediani, M. E.; Astitha, M.

2015-12-01

The distribution of reliable power is essential to businesses, public services, and our daily lives. With the growing abundance of data being collected and created by industry (i.e. outage data), government agencies (i.e. land cover), and academia (i.e. weather forecasts), we can begin to tackle problems that previously seemed too complex to solve. In this session, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. We will show a performance evaluation of outage predictive models built for Eversource Energy (formerly Connecticut Light & Power) for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to >15,000 outages). High resolution weather simulations (simulated with the Weather and Research Forecast Model) were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes.

Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.

PubMed

Haque, M Muksitul; Holder, Lawrence B; Skinner, Michael K

2015-01-01

Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs). Different environmental toxicants have been shown to promote exposure (i.e., toxicant) specific signatures of germline epimutations. Analysis of genomic features associated with these epimutations identified low-density CpG regions (<3 CpG / 100bp) termed CpG deserts and a number of unique DNA sequence motifs. The rat genome was annotated for these and additional relevant features. The objective of the current study was to use a machine learning computational approach to predict all potential epimutations in the genome. A number of previously identified sperm epimutations were used as training sets. A novel machine learning approach using a sequential combination of Active Learning and Imbalance Class Learner analysis was developed. The transgenerational sperm epimutation analysis identified approximately 50K individual sites with a 1 kb mean size and 3,233 regions that had a minimum of three adjacent sites with a mean size of 3.5 kb. A select number of the most relevant genomic features were identified with the low density CpG deserts being a critical genomic feature of the features selected. A similar independent analysis with transgenerational somatic cell epimutation training sets identified a smaller number of 1,503 regions of genome-wide predicted sites and differences in genomic feature contributions. The predicted genome-wide germline (sperm) epimutations were found to be distinct from the predicted somatic cell epimutations. Validation of the genome-wide germline predicted sites used two recently identified transgenerational sperm epimutation signature sets from the pesticides dichlorodiphenyltrichloroethane (DDT) and methoxychlor (MXC) exposure lineage F3 generation. Analysis of this positive validation data set

Metagenomic Functional Potential Predicts Degradation Rates of a Model Organophosphorus Xenobiotic in Pesticide Contaminated Soils

PubMed Central

Jeffries, Thomas C.; Rayu, Smriti; Nielsen, Uffe N.; Lai, Kaitao; Ijaz, Ali; Nazaries, Loic; Singh, Brajesh K.

2018-01-01

Chemical contamination of natural and agricultural habitats is an increasing global problem and a major threat to sustainability and human health. Organophosphorus (OP) compounds are one major class of contaminant and can undergo microbial degradation, however, no studies have applied system-wide ecogenomic tools to investigate OP degradation or use metagenomics to understand the underlying mechanisms of biodegradation in situ and predict degradation potential. Thus, there is a lack of knowledge regarding the functional genes and genomic potential underpinning degradation and community responses to contamination. Here we address this knowledge gap by performing shotgun sequencing of community DNA from agricultural soils with a history of pesticide usage and profiling shifts in functional genes and microbial taxa abundance. Our results showed two distinct groups of soils defined by differing functional and taxonomic profiles. Degradation assays suggested that these groups corresponded to the organophosphorus degradation potential of soils, with the fastest degrading community being defined by increases in transport and nutrient cycling pathways and enzymes potentially involved in phosphorus metabolism. This was against a backdrop of taxonomic community shifts potentially related to contamination adaptation and reflecting the legacy of exposure. Overall our results highlight the value of using holistic system-wide metagenomic approaches as a tool to predict microbial degradation in the context of the ecology of contaminated habitats. PMID:29515526

Vitamin E and breast cancer prevention: current status and future potential.

PubMed

Kline, Kimberly; Lawson, Karla A; Yu, Weiping; Sanders, Bob G

2003-01-01

Vitamin E is a collective term used to refer to a number of structurally and functionally different compounds. Although some vitamin E compounds are popular supplements marketed for their potential beneficial antioxidant effects for a number of chronic diseases including various forms of cancer, a recent report by the National Academy of Sciences Food and Nutrition Board concluded that too little is known at present to provide definitive answers regarding whether taking larger doses of dietary antioxidants will help prevent chronic diseases. Recent reviews of epidemiological data suggest that dietary source vitamin E may provide some protection against breast cancer, while vitamin E supplements do not. A majority of studies investigating the protective effects of certain types of vitamin E in animal models of mammary cancer prevention conclude that there is little or no effect. The study of vitamin E is complex, and the vitamin E field faces many scientific challenges.

HCV prevalence can predict HIV epidemic potential among people who inject drugs: mathematical modeling analysis.

PubMed

Akbarzadeh, Vajiheh; Mumtaz, Ghina R; Awad, Susanne F; Weiss, Helen A; Abu-Raddad, Laith J

2016-12-03

Hepatitis C virus (HCV) and HIV are both transmitted through percutaneous exposures among people who inject drugs (PWID). Ecological analyses on global epidemiological data have identified a positive association between HCV and HIV prevalence among PWID. Our objective was to demonstrate how HCV prevalence can be used to predict HIV epidemic potential among PWID. Two population-level models were constructed to simulate the evolution of HCV and HIV epidemics among PWID. The models described HCV and HIV parenteral transmission, and were solved both deterministically and stochastically. The modeling results provided a good fit to the epidemiological data describing the ecological HCV and HIV association among PWID. HCV was estimated to be eight times more transmissible per shared injection than HIV. A threshold HCV prevalence of 29.0% (95% uncertainty interval (UI): 20.7-39.8) and 46.5% (95% UI: 37.6-56.6) were identified for a sustainable HIV epidemic (HIV prevalence >1%) and concentrated HIV epidemic (HIV prevalence >5%), respectively. The association between HCV and HIV was further described with six dynamical regimes depicting the overlapping epidemiology of the two infections, and was quantified using defined and estimated measures of association. Modeling predictions across a wide range of HCV prevalence indicated overall acceptable precision in predicting HIV prevalence at endemic equilibrium. Modeling predictions were found to be robust with respect to stochasticity and behavioral and biological parameter uncertainty. In an illustrative application of the methodology, the modeling predictions of endemic HIV prevalence in Iran agreed with the scale and time course of the HIV epidemic in this country. Our results show that HCV prevalence can be used as a proxy biomarker of HIV epidemic potential among PWID, and that the scale and evolution of HIV epidemic expansion can be predicted with sufficient precision to inform HIV policy, programming, and resource

Prediction of preservative sensitization potential using surface marker CD86 and/or CD54 expression on human cell line, THP-1.

PubMed

Sakaguchi, Hitoshi; Miyazawa, Masaaki; Yoshida, Yukiko; Ito, Yuichi; Suzuki, Hiroyuki

2007-02-01

Preservatives are important components in many products, but have a history of purported allergy. Several assays [e.g., guinea pig maximization test (GPMT), local lymph node assay (LLNA)] are used to evaluate allergy potential of preservatives. We recently developed the human Cell Line Activation Test (h-CLAT), an in vitro skin sensitization test using human THP-1 cells. This test evaluates the augmentation of CD86 and CD54 expression, which are key events in the sensitization process, as an indicator of allergy following treatment with test chemical. Earlier, we found that a sub-toxic concentration was needed for the up-regulation of surface marker expression. In this study, we further evaluate the capability of h-CLAT to predict allergy potential using eight preservatives. Cytotoxicity was determined using propidium iodide with flow cytometry analysis and five doses that produce a 95, 85, 75, 65, and 50% cell viability were selected. If a material did not have any cytotoxicity at the highest technical dose (HTD), five doses are set using serial 1.3 dilutions of the HTD. The test materials used were six known allergic preservatives (e.g., methylchloroisothiazolinone/methylisothiazolinone, formaldehyde), and two non-allergic preservatives (methylparaben and 4-hydroxybenzoic acid). All allergic preservatives augmented CD86 and/or CD54 expression, indicating h-CLAT correctly identified the allergens. No augmentation was observed with the non-allergic preservatives; also correctly identified by h-CLAT. In addition, we report two threshold concentrations that may be used to categorize skin sensitization potency like the LLNA estimated concentration that yield a three-fold stimulation (EC3) value. These corresponding values are the estimated concentration which gives a relative fluorescence intensity (RFI) = 150 for CD86 and an RFI = 200 for CD54. These data suggest that h-CLAT, using THP-1 cells, may be able to predict the allergy potential of preservatives and

Application of a GIS-/remote sensing-based approach for predicting groundwater potential zones using a multi-criteria data mining methodology.

PubMed

Mogaji, Kehinde Anthony; Lim, Hwee San

2017-07-01

This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of

Potential ecological risk assessment and prediction of soil heavy-metal pollution around coal gangue dump

NASA Astrophysics Data System (ADS)

Jiang, X.; Lu, W. X.; Zhao, H. Q.; Yang, Q. C.; Yang, Z. P.

2014-06-01

The aim of the present study is to evaluate the potential ecological risk and trend of soil heavy-metal pollution around a coal gangue dump in Jilin Province (Northeast China). The concentrations of Cd, Pb, Cu, Cr and Zn were monitored by inductively coupled plasma mass spectrometry (ICP-MS). The potential ecological risk index method developed by Hakanson (1980) was employed to assess the potential risk of heavy-metal pollution. The potential ecological risk in the order of ER(Cd) > ER(Pb) > ER(Cu) > ER(Cr) > ER(Zn) have been obtained, which showed that Cd was the most important factor leading to risk. Based on the Cd pollution history, the cumulative acceleration and cumulative rate of Cd were estimated, then the fixed number of years exceeding the standard prediction model was established, which was used to predict the pollution trend of Cd under the accelerated accumulation mode and the uniform mode. Pearson correlation analysis and correspondence analysis are employed to identify the sources of heavy metals and the relationship between sampling points and variables. These findings provided some useful insights for making appropriate management strategies to prevent or decrease heavy-metal pollution around a coal gangue dump in the Yangcaogou coal mine and other similar areas elsewhere.

A catchment-scale model to predict spatial and temporal burden of E. coli on pasture from grazing livestock.

PubMed

Oliver, David M; Bartie, Phil J; Louise Heathwaite, A; Reaney, Sim M; Parnell, Jared A Q; Quilliam, Richard S

2018-03-01

Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen &Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1km 2 grid cell (Ayr: r=0.57; p<0.001, Lunan: r=0.32; p<0.001). There was a significant difference in the predicted maximum E. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (P<0.001), driven largely by livestock presence. The ViPER model thus describes, at the landscape scale, spatial nuances in the vulnerability of E. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

An operational mesoscale ensemble data assimilation and prediction system: E-RTFDDA

NASA Astrophysics Data System (ADS)

Liu, Y.; Hopson, T.; Roux, G.; Hacker, J.; Xu, M.; Warner, T.; Swerdlin, S.

2009-04-01

Mesoscale (2-2000 km) meteorological processes differ from synoptic circulations in that mesoscale weather changes rapidly in space and time, and physics processes that are parameterized in NWP models play a great role. Complex interactions of synoptic circulations, regional and local terrain, land-surface heterogeneity, and associated physical properties, and the physical processes of radiative transfer, cloud and precipitation and boundary layer mixing, are crucial in shaping regional weather and climate. Mesoscale ensemble analysis and prediction should sample the uncertainties of mesoscale modeling systems in representing these factors. An innovative mesoscale Ensemble Real-Time Four Dimensional Data Assimilation (E-RTFDDA) and forecasting system has been developed at NCAR. E-RTFDDA contains diverse ensemble perturbation approaches that consider uncertainties in all major system components to produce multi-scale continuously-cycling probabilistic data assimilation and forecasting. A 30-member E-RTFDDA system with three nested domains with grid sizes of 30, 10 and 3.33 km has been running on a Department of Defense high-performance computing platform since September 2007. It has been applied at two very different US geographical locations; one in the western inter-mountain area and the other in the northeastern states, producing 6 hour analyses and 48 hour forecasts, with 4 forecast cycles a day. The operational model outputs are analyzed to a) assess overall ensemble performance and properties, b) study terrain effect on mesoscale predictability, c) quantify the contribution of different ensemble perturbation approaches to the overall forecast skill, and d) assess the additional contributed skill from an ensemble calibration process based on a quantile-regression algorithm. The system and the results will be reported at the meeting.

Predictable patterns of the May-June rainfall anomaly over East Asia

NASA Astrophysics Data System (ADS)

Xing, Wen; Wang, Bin; Yim, So-Young; Ha, Kyung-Ja

2017-02-01

During early summer (May-June, MJ), East Asia (EA) subtropical front is a defining feature of Asian monsoon, which produces the most prominent precipitation band in the global subtropics. Here we show that dynamical prediction of early summer EA (20°N-45°N, 100°E-130°E) rainfall made by four coupled climate models' ensemble hindcast (1979-2010) yields only a moderate skill and cannot be used to estimate predictability. The present study uses an alternative, empirical orthogonal function (EOF)-based physical-empirical (P-E) model approach to predict rainfall anomaly pattern and estimate its potential predictability. The first three leading modes are physically meaningful and can be, respectively, attributed to (a) the interaction between the anomalous western North Pacific subtropical high and underlying Indo-Pacific warm ocean, (b) the forcing associated with North Pacific sea surface temperature (SST) anomaly, and (c) the development of equatorial central Pacific SST anomalies. A suite of P-E models is established to forecast the first three leading principal components. All predictors are 0 month ahead of May, so the prediction here is named as a 0 month lead prediction. The cross-validated hindcast results demonstrate that these modes may be predicted with significant temporal correlation skills (0.48-0.72). Using the predicted principal components and the corresponding EOF patterns, the total MJ rainfall anomaly was hindcasted for the period of 1979-2015. The time-mean pattern correlation coefficient (PCC) score reaches 0.38, which is significantly higher than dynamical models' multimodel ensemble skill (0.21). The estimated potential maximum attainable PCC is around 0.65, suggesting that the dynamical prediction models may have large rooms to improve. Limitations and future work are discussed.

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios.

PubMed

Ge, Xuezhen; He, Shanyong; Wang, Tao; Yan, Wei; Zong, Shixiang

2015-01-01

As the primary pest of palm trees, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) has caused serious harm to palms since it first invaded China. The present study used CLIMEX 1.1 to predict the potential distribution of R. ferrugineus in China according to both current climate data (1981-2010) and future climate warming estimates based on simulated climate data for the 2020s (2011-2040) provided by the Tyndall Center for Climate Change Research (TYN SC 2.0). Additionally, the Ecoclimatic Index (EI) values calculated for different climatic conditions (current and future, as simulated by the B2 scenario) were compared. Areas with a suitable climate for R. ferrugineus distribution were located primarily in central China according to the current climate data, with the northern boundary of the distribution reaching to 40.1°N and including Tibet, north Sichuan, central Shaanxi, south Shanxi, and east Hebei. There was little difference in the potential distribution predicted by the four emission scenarios according to future climate warming estimates. The primary prediction under future climate warming models was that, compared with the current climate model, the number of highly favorable habitats would increase significantly and expand into northern China, whereas the number of both favorable and marginally favorable habitats would decrease. Contrast analysis of EI values suggested that climate change and the density of site distribution were the main effectors of the changes in EI values. These results will help to improve control measures, prevent the spread of this pest, and revise the targeted quarantine areas.

Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios

PubMed Central

Ge, Xuezhen; He, Shanyong; Wang, Tao; Yan, Wei; Zong, Shixiang

2015-01-01

As the primary pest of palm trees, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) has caused serious harm to palms since it first invaded China. The present study used CLIMEX 1.1 to predict the potential distribution of R. ferrugineus in China according to both current climate data (1981–2010) and future climate warming estimates based on simulated climate data for the 2020s (2011–2040) provided by the Tyndall Center for Climate Change Research (TYN SC 2.0). Additionally, the Ecoclimatic Index (EI) values calculated for different climatic conditions (current and future, as simulated by the B2 scenario) were compared. Areas with a suitable climate for R. ferrugineus distribution were located primarily in central China according to the current climate data, with the northern boundary of the distribution reaching to 40.1°N and including Tibet, north Sichuan, central Shaanxi, south Shanxi, and east Hebei. There was little difference in the potential distribution predicted by the four emission scenarios according to future climate warming estimates. The primary prediction under future climate warming models was that, compared with the current climate model, the number of highly favorable habitats would increase significantly and expand into northern China, whereas the number of both favorable and marginally favorable habitats would decrease. Contrast analysis of EI values suggested that climate change and the density of site distribution were the main effectors of the changes in EI values. These results will help to improve control measures, prevent the spread of this pest, and revise the targeted quarantine areas. PMID:26496438

Rapid biochemical methane potential prediction of urban organic waste with near-infrared reflectance spectroscopy.

PubMed

Fitamo, T; Triolo, J M; Boldrin, A; Scheutz, C

2017-08-01

The anaerobic digestibility of various biomass feedstocks in biogas plants is determined with biochemical methane potential (BMP) assays. However, experimental BMP analysis is time-consuming, costly and challenging to optimise stock management and feeding to achieve improved biogas production. The aim of the present study is to develop a fast and reliable model based on near-infrared reflectance spectroscopy (NIRS) for the BMP prediction of urban organic waste (UOW). The model comprised 87 UOW samples. Additionally, 88 plant biomass samples were included, to develop a combined model predicting BMP. The coefficient of determination (R 2 ) and root mean square error in prediction (RMSE P ) of the UOW model were 0.88 and 44 mL CH 4 /g VS, while the combined model was 0.89 and 50 mL CH 4 /g VS. Improved model performance was obtained for the two individual models compared to the combined version. The BMP prediction with NIRS was satisfactory and moderately successful. Copyright © 2017 Elsevier Ltd. All rights reserved.

An equivalent potential vorticity theory applied to the analysis and prediction of severe storm dynamics

NASA Technical Reports Server (NTRS)

Paine, D. A.; Kaplan, M. L.

1976-01-01

Potential vorticity theory is developed in a description of an equivalent potential temperature topography, and a new theory suited to the description of scale interaction is elaborated. Macroscale triggering of ageostrophic flow fields at the mesoscale, in turn leading to release of convective instability along narrow zones at the microscale, is examined. Correlation of appreciable decrease in potential vorticity with such phenomena as cumulonimbi, tornados, and duststorms is examined. The relevance of a multiscale energy-momentum cascade in numerical prediction of severe mesoscale and microscale phenomena from radiosonde data is reviewed. Hypotheses for mesoscale dynamics are constructed.

Prediction of the in planta Phakopsora pachyrhizi secretome and potential effector families.

PubMed

de Carvalho, Mayra C da C G; Costa Nascimento, Leandro; Darben, Luana M; Polizel-Podanosqui, Adriana M; Lopes-Caitar, Valéria S; Qi, Mingsheng; Rocha, Carolina S; Carazzolle, Marcelo Falsarella; Kuwahara, Márcia K; Pereira, Goncalo A G; Abdelnoor, Ricardo V; Whitham, Steven A; Marcelino-Guimarães, Francismar C

2017-04-01

Asian soybean rust (ASR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, can cause losses greater than 80%. Despite its economic importance, there is no soybean cultivar with durable ASR resistance. In addition, the P. pachyrhizi genome is not yet available. However, the availability of other rust genomes, as well as the development of sample enrichment strategies and bioinformatics tools, has improved our knowledge of the ASR secretome and its potential effectors. In this context, we used a combination of laser capture microdissection (LCM), RNAseq and a bioinformatics pipeline to identify a total of 36 350 P. pachyrhizi contigs expressed in planta and a predicted secretome of 851 proteins. Some of the predicted secreted proteins had characteristics of candidate effectors: small size, cysteine rich, do not contain PFAM domains (except those associated with pathogenicity) and strongly expressed in planta. A comparative analysis of the predicted secreted proteins present in Pucciniales species identified new members of soybean rust and new Pucciniales- or P. pachyrhizi-specific families (tribes). Members of some families were strongly up-regulated during early infection, starting with initial infection through haustorium formation. Effector candidates selected from two of these families were able to suppress immunity in transient assays, and were localized in the plant cytoplasm and nuclei. These experiments support our bioinformatics predictions and show that these families contain members that have functions consistent with P. pachyrhizi effectors. © 2016 BSPP AND JOHN WILEY & SONS LTD.

Protein model quality assessment prediction by combining fragment comparisons and a consensus Cα contact potential

PubMed Central

Zhou, Hongyi; Skolnick, Jeffrey

2009-01-01

In this work, we develop a fully automated method for the quality assessment prediction of protein structural models generated by structure prediction approaches such as fold recognition servers, or ab initio methods. The approach is based on fragment comparisons and a consensus Cα contact potential derived from the set of models to be assessed and was tested on CASP7 server models. The average Pearson linear correlation coefficient between predicted quality and model GDT-score per target is 0.83 for the 98 targets which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models. PMID:18004783

CYP3A4 substrate selection and substitution in the prediction of potential drug-drug interactions.

PubMed

Galetin, Aleksandra; Ito, Kiyomi; Hallifax, David; Houston, J Brian

2005-07-01

The complexity of in vitro kinetic phenomena observed for CYP3A4 substrates (homo- or heterotropic cooperativity) confounds the prediction of drug-drug interactions, and an evaluation of alternative and/or pragmatic approaches and substrates is needed. The current study focused on the utility of the three most commonly used CYP3A4 in vitro probes for the prediction of 26 reported in vivo interactions with azole inhibitors (increase in area under the curve ranged from 1.2 to 24, 50% in the range of potent inhibition). In addition to midazolam, testosterone, and nifedipine, quinidine was explored as a more "pragmatic" substrate due to its kinetic properties and specificity toward CYP3A4 in comparison with CYP3A5. Ki estimates obtained in human liver microsomes under standardized in vitro conditions for each of the four probes were used to determine the validity of substrate substitution in CYP3A4 drug-drug interaction prediction. Detailed inhibitor-related (microsomal binding, depletion over incubation time) and substrate-related factors (cooperativity, contribution of other metabolic pathways, or renal excretion) were incorporated in the assessment of the interaction potential. All four CYP3A4 probes predicted 69 to 81% of the interactions with azoles within 2-fold of the mean in vivo value. Comparison of simple and multisite mechanistic models and interaction prediction accuracy for each of the in vitro probes indicated that midazolam and quinidine in vitro data provided the best assessment of a potential interaction, with the lowest bias and the highest precision of the prediction. Further investigations with a wider range of inhibitors are required to substantiate these findings.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Comparison of manual scaled and predicted foE and foF1 critical frequencies. Technical report

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

Gamache, R.R.; Kersey, W.T.

1990-07-01

The CCIR and Titheridge foE critical frequency prediction routines were tested by comparison with 1875 manually scaled values. The foF1 critical frequency prediction routine of Millman et al was tested by comparison with 1005 manually scaled values. Plots and statistics of the comparisons are presented and discussed. From the results recommendations are made to help improve autoscaling.

Can nutrient status of four woody plant species be predicted using field spectrometry?

NASA Astrophysics Data System (ADS)

Ferwerda, Jelle G.; Skidmore, Andrew K.

This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra, derivative spectra and continuum-removed spectra were compared in terms of predictive power. Results showed that the best predictions for nitrogen, phosphorous, and magnesium occur when using derivative spectra, and the best predictions for sodium, potassium, and calcium occur when using continuum-removed data. To test whether a general model for multiple species is also valid for individual species, a bootstrapping routine was applied. Prediction accuracies for the individual species were lower then prediction accuracies obtained for the combined dataset for all except one element/species combination, indicating that indices with high prediction accuracies at the landscape scale are less appropriate to detect the chemical content of individual species.

Psychophysiological prediction of choice: relevance to insight and drug addiction

PubMed Central

Moeller, Scott J.; Hajcak, Greg; Parvaz, Muhammad A.; Dunning, Jonathan P.; Volkow, Nora D.

2012-01-01

An important goal of addiction research and treatment is to predict behavioural responses to drug-related stimuli. This goal is especially important for patients with impaired insight, which can interfere with therapeutic interventions and potentially invalidate self-report questionnaires. This research tested (i) whether event-related potentials, specifically the late positive potential, predict choice to view cocaine images in cocaine addiction; and (ii) whether such behaviour prediction differs by insight (operationalized in this study as self-awareness of image choice). Fifty-nine cocaine abusers and 32 healthy controls provided data for the following laboratory components that were completed in a fixed-sequence (to establish prediction): (i) event-related potential recordings while passively viewing pleasant, unpleasant, neutral and cocaine images, during which early (400–1000 ms) and late (1000–2000 ms) window late positive potentials were collected; (ii) self-reported arousal ratings for each picture; and (iii) two previously validated tasks: one to assess choice for viewing these same images, and the other to group cocaine abusers by insight. Results showed that pleasant-related late positive potentials and arousal ratings predicted pleasant choice (the choice to view pleasant pictures) in all subjects, validating the method. In the cocaine abusers, the predictive ability of the late positive potentials and arousal ratings depended on insight. Cocaine-related late positive potentials better predicted cocaine image choice in cocaine abusers with impaired insight. Another emotion-relevant event-related potential component (the early posterior negativity) did not show these results, indicating specificity of the late positive potential. In contrast, arousal ratings better predicted respective cocaine image choice (and actual cocaine use severity) in cocaine abusers with intact insight. Taken together, the late positive potential could serve as a biomarker

E-bike trials’ potential to promote sustained changes in car owners mobility habits

NASA Astrophysics Data System (ADS)

Moser, Corinne; Blumer, Yann; Lena Hille, Stefanie

2018-04-01

Modal shifts hold considerable potential to mitigate carbon emissions. Electric bikes (e-bikes) represent a promising energy- and carbon-efficient alternative to cars. However, as mobility behaviour is highly habitual, convincing people to switch from cars to e-bikes is challenging. One strategy to accomplish this is the disruption of existing habits—a key idea behind an annual e-bike promotion programme in Switzerland, in which car owners can try out an e-bike for free over a two-week period in exchange for their car keys. By means of a longitudinal survey, we measured the long-term effects of this trial on mobility-related habitual associations. After one year, participants’ habitual association with car use had weakened significantly. This finding was valid both for participants who bought an e-bike after the trial and those who did not. Our findings contrast the results of other studies who find that the effect of interventions to induce modal shifts wears off over time. We conclude that an e-bike trial has the potential to break mobility habits and motivate car owners to use more sustainable means of transport.

Peak-summer East Asian rainfall predictability and prediction part I: Southeast Asia

NASA Astrophysics Data System (ADS)

Xing, Wen; Wang, Bin; Yim, So-Young

2016-07-01

The interannual variation of East Asia summer monsoon (EASM) rainfall exhibits considerable differences between early summer [May-June (MJ)] and peak summer [July-August (JA)]. The present study focuses on peak summer. During JA, the mean ridge line of the western Pacific subtropical High (WPSH) divides EASM domain into two sub-domains: the tropical EA (5°N-26.5°N) and subtropical-extratropical EA (26.5°N-50°N). Since the major variability patterns in the two sub-domains and their origins are substantially different, the Part I of this study concentrates on the tropical EA or Southeast Asia (SEA). We apply the predictable mode analysis approach to explore the predictability and prediction of the SEA peak summer rainfall. Four principal modes of interannual rainfall variability during 1979-2013 are identified by EOF analysis: (1) the WPSH-dipole sea surface temperature (SST) feedback mode in the Northern Indo-western Pacific warm pool associated with the decay of eastern Pacific El Niño/Southern Oscillation (ENSO), (2) the central Pacific-ENSO mode, (3) the Maritime continent SST-Australian High coupled mode, which is sustained by a positive feedback between anomalous Australian high and sea surface temperature anomalies (SSTA) over Indian Ocean, and (4) the ENSO developing mode. Based on understanding of the sources of the predictability for each mode, a set of physics-based empirical (P-E) models is established for prediction of the first four leading principal components (PCs). All predictors are selected from either persistent atmospheric lower boundary anomalies from March to June or the tendency from spring to early summer. We show that these four modes can be predicted reasonably well by the P-E models, thus they are identified as the predictable modes. Using the predicted PCs and the corresponding observed spatial patterns, we have made a 35-year cross-validated hindcast, setting up a bench mark for dynamic models' predictions. The P-E hindcast

Potential of the octanol-water partition coefficient (logP) to predict the dermal penetration behaviour of amphiphilic compounds in aqueous solutions.

PubMed

Korinth, Gintautas; Wellner, Tanja; Schaller, Karl Heinz; Drexler, Hans

2012-11-23

Aqueous amphiphilic compounds may exhibit enhanced skin penetration compared with neat compounds. Conventional models do not predict this percutaneous penetration behaviour. We investigated the potential of the octanol-water partition coefficient (logP) to predict dermal fluxes for eight compounds applied neat and as 50% aqueous solutions in diffusion cell experiments using human skin. Data for seven other compounds were accessed from literature. In total, seven glycol ethers, three alcohols, two glycols, and three other chemicals were considered. Of these 15 compounds, 10 penetrated faster through the skin as aqueous solutions than as neat compounds. The other five compounds exhibited larger fluxes as neat applications. For 13 of the 15 compounds, a consistent relationship was identified between the percutaneous penetration behaviour and the logP. Compared with the neat applications, positive logP were associated with larger fluxes for eight of the diluted compounds, and negative logP were associated with smaller fluxes for five of the diluted compounds. Our study demonstrates that decreases or enhancements in dermal penetration upon aqueous dilution can be predicted for many compounds from the sign of logP (i.e., positive or negative). This approach may be suitable as a first approximation in risk assessments of dermal exposure. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Genomic Prediction of Gene Bank Wheat Landraces.

PubMed

Crossa, José; Jarquín, Diego; Franco, Jorge; Pérez-Rodríguez, Paulino; Burgueño, Juan; Saint-Pierre, Carolina; Vikram, Prashant; Sansaloni, Carolina; Petroli, Cesar; Akdemir, Deniz; Sneller, Clay; Reynolds, Matthew; Tattaris, Maria; Payne, Thomas; Guzman, Carlos; Peña, Roberto J; Wenzl, Peter; Singh, Sukhwinder

2016-07-07

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, "diversity" and "prediction", including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15-20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials

Phylogenomic detection and functional prediction of genes potentially important for plant meiosis.

PubMed

Zhang, Luoyan; Kong, Hongzhi; Ma, Hong; Yang, Ji

2018-02-15

Meiosis is a specialized type of cell division necessary for sexual reproduction in eukaryotes. A better understanding of the cytological procedures of meiosis has been achieved by comprehensive cytogenetic studies in plants, while the genetic mechanisms regulating meiotic progression remain incompletely understood. The increasing accumulation of complete genome sequences and large-scale gene expression datasets has provided a powerful resource for phylogenomic inference and unsupervised identification of genes involved in plant meiosis. By integrating sequence homology and expression data, 164, 131, 124 and 162 genes potentially important for meiosis were identified in the genomes of Arabidopsis thaliana, Oryza sativa, Selaginella moellendorffii and Pogonatum aloides, respectively. The predicted genes were assigned to 45 meiotic GO terms, and their functions were related to different processes occurring during meiosis in various organisms. Most of the predicted meiotic genes underwent lineage-specific duplication events during plant evolution, with about 30% of the predicted genes retaining only a single copy in higher plant genomes. The results of this study provided clues to design experiments for better functional characterization of meiotic genes in plants, promoting the phylogenomic approach to the evolutionary dynamics of the plant meiotic machineries. Copyright © 2017 Elsevier B.V. All rights reserved.

Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

PubMed Central

Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eospredicted 64% of sputum Neupredict both sputum Eos and Neu accurately assigned only 41% of samples. Conclusion Despite statistically significant associations FeNO, IgE, blood Eos and Neu, FEV1%predicted, and age are poor surrogates, separately and combined, for accurately predicting sputum eosinophils and neutrophils. PMID:23706399

How predictable is the anomaly pattern of the Indian summer rainfall?

NASA Astrophysics Data System (ADS)

Li, Juan; Wang, Bin

2016-05-01

Century-long efforts have been devoted to seasonal forecast of Indian summer monsoon rainfall (ISMR). Most studies of seasonal forecast so far have focused on predicting the total amount of summer rainfall averaged over the entire India (i.e., all Indian rainfall index-AIRI). However, it is practically more useful to forecast anomalous seasonal rainfall distribution (anomaly pattern) across India. The unknown science question is to what extent the anomalous rainfall pattern is predictable. This study attempted to address this question. Assessment of the 46-year (1960-2005) hindcast made by the five state-of-the-art ENSEMBLE coupled dynamic models' multi-model ensemble (MME) prediction reveals that the temporal correlation coefficient (TCC) skill for prediction of AIRI is 0.43, while the area averaged TCC skill for prediction of anomalous rainfall pattern is only 0.16. The present study aims to estimate the predictability of ISMR on regional scales by using Predictable Mode Analysis method and to develop a set of physics-based empirical (P-E) models for prediction of ISMR anomaly pattern. We show that the first three observed empirical orthogonal function (EOF) patterns of the ISMR have their distinct dynamical origins rooted in an eastern Pacific-type La Nina, a central Pacific-type La Nina, and a cooling center near dateline, respectively. These equatorial Pacific sea surface temperature anomalies, while located in different longitudes, can all set up a specific teleconnection pattern that affects Indian monsoon and results in different rainfall EOF patterns. Furthermore, the dynamical models' skill for predicting ISMR distribution primarily comes primarily from these three modes. Therefore, these modes can be regarded as potentially predictable modes. If these modes are perfectly predicted, about 51 % of the total observed variability is potentially predictable. Based on understanding the lead-lag relationships between the lower boundary anomalies and the

Procalcitonin as a potential predicting factor for prognosis in bacterial meningitis.

PubMed

Park, Bong Soo; Kim, Si Eun; Park, Si Hyung; Kim, Jinseung; Shin, Kyong Jin; Ha, Sam Yeol; Park, JinSe; Kim, Sung Eun; Lee, Byung In; Park, Kang Min

2017-02-01

We investigated the potential role of serum procalcitonin in differentiating bacterial meningitis from viral meningitis, and in predicting the prognosis in patients with bacterial meningitis. This was a retrospective study of 80 patients with bacterial meningitis (13 patients died). In addition, 58 patients with viral meningitis were included as the disease control groups for comparison. The serum procalcitonin level was measured in all patients at admission. Differences in demographic and laboratory data, including the procalcitonin level, were analyzed between the groups. We used the mortality rate during hospitalization as a marker of prognosis in patients with bacterial meningitis. Multiple logistic regression analysis showed that high serum levels of procalcitonin (>0.12ng/mL) were an independently significant variable for differentiating bacterial meningitis from viral meningitis. The risk of having bacterial meningitis with high serum levels of procalcitonin was at least 6 times higher than the risk of having viral meningitis (OR=6.76, 95% CI: 1.84-24.90, p=0.004). In addition, we found that high levels of procalcitonin (>7.26ng/mL) in the blood were an independently significant predictor for death in patients with bacterial meningitis. The risk of death in patients with bacterial meningitis with high serum levels of procalcitonin may be at least 9 times higher than those without death (OR=9.09, 95% CI: 1.74-47.12, p=0.016). We found that serum procalcitonin is a useful marker for differentiating bacterial meningitis from viral meningitis, and it is also a potential predicting factor for prognosis in patients with bacterial meningitis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of e± elastic scattering cross-section ratio based on phenomenological two-photon exchange corrections

NASA Astrophysics Data System (ADS)

Qattan, I. A.

2017-06-01

I present a prediction of the e± elastic scattering cross-section ratio, Re+e-, as determined using a new parametrization of the two-photon exchange (TPE) corrections to electron-proton elastic scattering cross section σR. The extracted ratio is compared to several previous phenomenological extractions, TPE hadronic calculations, and direct measurements from the comparison of electron and positron scattering. The TPE corrections and the ratio Re+e- show a clear change of sign at low Q2, which is necessary to explain the high-Q2 form factors discrepancy while being consistent with the known Q2→0 limit. While my predictions are in generally good agreement with previous extractions, TPE hadronic calculations, and existing world data including the recent two measurements from the CLAS and VEPP-3 Novosibirsk experiments, they are larger than the new OLYMPUS measurements at larger Q2 values.

Testing predictions of the quantum landscape multiverse 1: the Starobinsky inflationary potential

NASA Astrophysics Data System (ADS)

Di Valentino, Eleonora; Mersini-Houghton, Laura

2017-03-01

The 2015 Planck data release has placed tight constraints on the allowed class of inflationary models. The current data favors concave downwards inflationary potentials while offering interesting hints on possible deviations from the standard picture of CMB perturbations. We here test the predictions of the theory of the origin of the universe from the landscape multiverse, against the most recent Planck data, for the case of concave downwards inflationary potentials, such as the Starobinsky model of inflation. By considering the quantum entanglement correction of the multiverse, we can place a lower limit on the local `SUSY breaking' scale b > 1.2 × 107 GeV at 95% c.l. from Planck TT+lowTEB. We find that this limit is consistent with the range for b that allows the landscape multiverse to explain a serie of anomalies present in the current data.

The potential predictability of fire danger provided by ECMWF forecast

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca

2017-04-01

The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.

The predictive value of ePAQ in the urodynamic diagnoses-A prospective cohort study.

PubMed

McCooty, Shanteela; Nightingale, Peter; Latthe, Pallavi

2018-01-01

To assess whether the electronic Personal Assessment Questionnaire-Pelvic Floor (ePAQ-PF) had accuracy in predicting the urodynamic diagnoses of Detrusor Overactivity (DO) and/or Urodynamic Stress Incontinence (USI). Tertiary urogynaecology unit linked to an academic university teaching hospital. Consecutive women who presented with lower urinary tract symptoms (LUTS) and were booked to have urodynamic studies. Women completed an ePAQ-PF prior to having urodynamics (UDS) by clinicians who were blinded to the ePAQ-PF results while conducting this procedure. Receiver Operating Characteristics (ROC) curves were constructed for predictive accuracy of overactive bladder (OAB) score in DO and of stress urinary incontinence (SUI) score in USI. Prospective cohort study designed to meet the requirements of the standards for reporting of diagnostic accuracy (STARD). 390 women with a mean age of 54.2 (range 21-92) years were recruited. The majority (n = 294; 75%) were White Caucasian and had two children (n = 157; 40.3%). Of them, 67.2% (n = 262) had DO and USI was confirmed in 21.5% (n = 84). The area under the ROC curve for DO was 0.704 (95% confidence interval 0.650-0.759) and for USI it was 0.731 (95% confidence interval 0.652-0.778). The OAB and SUI scores on the ePAQ-PF demonstrated that they are fair predictors in diagnosing DO and USI. As the OAB and SUI score on ePAQ-PF increased so did the likelihood of DO (up to a score of 75) and USI on UDS. © 2017 Wiley Periodicals, Inc.

Genomic Prediction of Gene Bank Wheat Landraces

PubMed Central

Crossa, José; Jarquín, Diego; Franco, Jorge; Pérez-Rodríguez, Paulino; Burgueño, Juan; Saint-Pierre, Carolina; Vikram, Prashant; Sansaloni, Carolina; Petroli, Cesar; Akdemir, Deniz; Sneller, Clay; Reynolds, Matthew; Tattaris, Maria; Payne, Thomas; Guzman, Carlos; Peña, Roberto J.; Wenzl, Peter; Singh, Sukhwinder

2016-01-01

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite

Simulation and Prediction of the Drug-Drug Interaction Potential of Naloxegol by Physiologically Based Pharmacokinetic Modeling.

PubMed

Zhou, D; Bui, K; Sostek, M; Al-Huniti, N

2016-05-01

Naloxegol, a peripherally acting μ-opioid receptor antagonist for the treatment of opioid-induced constipation, is a substrate for cytochrome P450 (CYP) 3A4/3A5 and the P-glycoprotein (P-gp) transporter. By integrating in silico, preclinical, and clinical pharmacokinetic (PK) findings, minimal and full physiologically based pharmacokinetic (PBPK) models were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models reasonably predicted the observed changes in naloxegol exposure with ketoconazole (increase of 13.1-fold predicted vs. 12.9-fold observed), diltiazem (increase of 2.8-fold predicted vs. 3.4-fold observed), rifampin (reduction of 76% predicted vs. 89% observed), and quinidine (increase of 1.2-fold predicted vs. 1.4-fold observed). The moderate CYP3A4 inducer efavirenz was predicted to reduce naloxegol exposure by ∼50%, whereas weak CYP3A inhibitors were predicted to minimally affect exposure. In summary, the PBPK models reasonably estimated interactions with various CYP3A modulators and can be used to guide dosing in clinical practice when naloxegol is coadministered with such agents. © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

New equations for predicting postoperative risk in patients with hip fracture.

PubMed

Hirose, Jun; Ide, Junji; Irie, Hiroki; Kikukawa, Kenshi; Mizuta, Hiroshi

2009-12-01

Predicting the postoperative course of patients with hip fractures would be helpful for surgical planning and risk management. We therefore established equations to predict the morbidity and mortality rates in candidates for hip fracture surgery using the Estimation of Physiologic Ability and Surgical Stress (E-PASS) risk-scoring system. First we evaluated the correlation between the E-PASS scores and postoperative morbidity and mortality rates in all 722 patients surgically treated for hip fractures during the study period (Group A). Next we established equations to predict morbidity and mortality rates. We then applied these equations to all 633 patients with hip fractures treated at seven other hospitals (Group B) and compared the predicted and actual morbidity and mortality rates to assess the predictive ability of the E-PASS and Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) systems. The ratio of actual to predicted morbidity and mortality rates was closer to 1.0 with the E-PASS than the POSSUM system. Our data suggest the E-PASS scoring system is useful for defining postoperative risk and its underlying algorithm accurately predicts morbidity and mortality rates in patients with hip fractures before surgery. This information then can be used to manage their condition and potentially improve treatment outcomes. Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

Making detailed predictions makes (some) predictions worse

NASA Astrophysics Data System (ADS)

Kelly, Theresa F.

In this paper, we investigate whether making detailed predictions about an event makes other predictions worse. Across 19 experiments, 10,895 participants, and 415,960 predictions about 724 professional sports games, we find that people who made detailed predictions about sporting events (e.g., how many hits each baseball team would get) made worse predictions about more general outcomes (e.g., which team would win). We rule out that this effect is caused by inattention or fatigue, thinking too hard, or a differential reliance on holistic information about the teams. Instead, we find that thinking about game-relevant details before predicting winning teams causes people to give less weight to predictive information, presumably because predicting details makes information that is relatively useless for predicting the winning team more readily accessible in memory and therefore incorporated into forecasts. Furthermore, we show that this differential use of information can be used to predict what kinds of games will and will not be susceptible to the negative effect of making detailed predictions.

Transcriptome profiles in sarcoidosis and their potential role in disease prediction.

PubMed

Schupp, Jonas C; Vukmirovic, Milica; Kaminski, Naftali; Prasse, Antje

2017-09-01

Sarcoidosis is a systemic disease defined by the presence of nonnecrotizing granuloma in the absence of any known cause. Although the heterogeneity of sarcoidosis is well characterized clinically, the transcriptome of sarcoidosis and underlying molecular mechanisms are not. The signal of all transcripts, small and long noncoding RNAs, can be detected using microarrays or RNA-Sequencing. Analyzing the transcriptome of tissues that are directly affected by granulomas is of great importance to understand biology of the disease and may be predictive of disease and treatment outcome. Multiple genome wide expression studies performed on sarcoidosis affected tissues were published in the last 11 years. Published studies focused on differences in gene expression between sarcoidosis vs. control tissues, stable vs. progressive sarcoidosis, as well as sarcoidosis vs. other diseases. Strikingly, all these transcriptomics data confirm the key role of TH1 immune response in sarcoidosis and particularly of interferon-γ (IFN-γ) and type I IFN-driven signaling pathways. The steps toward transcriptomics of sarcoidosis in precision medicine highlight the potentials of this approach. Large prospective follow-up studies are required to identify signatures predictive of disease progression and outcome.

Expression, Polyubiquitination, and Therapeutic Potential of Recombinant E6E7 from HPV16 Antigens Fused to Ubiquitin.

PubMed

de Oliveira, Liliane M Fernandes; Morale, Mirian G; Chaves, Agtha A M; Demasi, Marilene; Ho, Paulo L

2017-01-01

Ubiquitin-proteasome system plays an essential role in the immune response due to its involvement in the antigen generation and presentation to CD8 + T cells. Hereby, ubiquitin fused to antigens has been explored as an immunotherapeutic strategy that requires the activation of cytotoxic T lymphocytes. Here we propose to apply this ubiquitin fusion approach to a recombinant vaccine against human papillomavirus 16-infected cells. E6E7 multi-epitope antigen was fused genetically at its N- or C-terminal end to ubiquitin and expressed in Escherichia coli as inclusion bodies. The antigens were solubilized using urea and purified by nickel affinity chromatography in denatured condition. Fusion of ubiquitin to E6E7 resulted in marked polyubiquitination in vitro mainly when fused to the E6E7 N-terminal. When tested in a therapeutic scenario, the fusion of ubiquitin to E6E7 reinforced the anti-tumor protection and increased the E6/E7-specific cellular immune responses. Present results encourage the investigation of the adjuvant potential of the ubiquitin fusion to recombinant vaccines requiring CD8 + T cells.

Potential evapotranspiration and the likelihood of future drought

NASA Technical Reports Server (NTRS)

Rind, D.; Hansen, J.; Goldberg, R.; Rosenzweig, C.; Ruedy, R.

1990-01-01

The possibility that the greenhouse warming predicted by the GISS general-circulation model and other GCMs could lead to severe droughts is investigated by means of numerical simulations, with a focus on the role of potential evapotranspiration E(P). The relationships between precipitation (P), E(P), soil moisture, and vegetation changes in GCMs are discussed; the empirically derived Palmer drought-intensity index and a new supply-demand index (SDDI) based on changes in P - E(P) are described; and simulation results for the period 1960-2060 are presented in extensive tables, graphs, and computer-generated color maps. Simulations with both drought indices predict increasing drought frequency for the U.S., with effects already apparent in the 1990s and a 50-percent frequency of severe droughts by the 2050s. Analyses of arid periods during the Mesozoic and Cenozoic are shown to support the use of the SDDI in GCM drought prediction.

Intraoperative changes in transcranial motor evoked potentials and somatosensory evoked potentials predicting outcome in children with intramedullary spinal cord tumors.

PubMed

Cheng, Jason S; Ivan, Michael E; Stapleton, Christopher J; Quinones-Hinojosa, Alfredo; Gupta, Nalin; Auguste, Kurtis I

2014-06-01

Intraoperative dorsal column mapping, transcranial motor evoked potentials (TcMEPs), and somatosensory evoked potentials (SSEPs) have been used in adults to assist with the resection of intramedullary spinal cord tumors (IMSCTs) and to predict postoperative motor deficits. The authors sought to determine whether changes in MEP and SSEP waveforms would similarly predict postoperative motor deficits in children. The authors reviewed charts and intraoperative records for children who had undergone resection for IMSCTs as well as dorsal column mapping and TcMEP and SSEP monitoring. Motor evoked potential data were supplemented with electromyography data obtained using a Kartush microstimulator (Medtronic Inc.). Motor strength was graded using the Medical Research Council (MRC) scale during the preoperative, immediate postoperative, and follow-up periods. Reductions in SSEPs were documented after mechanical traction, in response to maneuvers with the cavitational ultrasonic surgical aspirator (CUSA), or both. Data from 12 patients were analyzed. Three lesions were encountered in the cervical and 7 in the thoracic spinal cord. Two patients had lesions of the cervicomedullary junction and upper spinal cord. Intraoperative MEP changes were noted in half of the patients. In these cases, normal polyphasic signals converted to biphasic signals, and these changes correlated with a loss of 1-2 grades in motor strength. One patient lost MEP signals completely and recovered strength to MRC Grade 4/5. The 2 patients with high cervical lesions showed neither intraoperative MEP changes nor motor deficits postoperatively. Dorsal columns were mapped in 7 patients, and the midline was determined accurately in all 7. Somatosensory evoked potentials were decreased in 7 patients. Two patients each had 2 SSEP decreases in response to traction intraoperatively but had no new sensory findings postoperatively. Another 2 patients had 3 traction-related SSEP decreases intraoperatively, and both

Cytomics in predictive medicine

NASA Astrophysics Data System (ADS)

Tarnok, Attila; Valet, Guenther K.

2004-07-01

Predictive Medicine aims at the detection of changes in patient's disease state prior to the manifestation of deterioration or improvement of the current status. Patient-specific, disease-course predictions with >95% or >99% accuracy during therapy would be highly valuable for everyday medicine. If these predictors were available, disease aggravation or progression, frequently accompanied by irreversible tissue damage or therapeutic side effects, could then potentially be avoided by early preventive therapy. The molecular analysis of heterogeneous cellular systems (Cytomics) by cytometry in conjunction with pattern-oriented bioinformatic analysis of the multiparametric cytometric and other data provides a promising approach to individualized or personalized medical treatment or disease management. Predictive medicine is best implemented by cell oriented measurements e.g. by flow or image cytometry. Cell oriented gene or protein arrays as well as bead arrays for the capture of solute molecules form serum, plasma, urine or liquor are equally of high value. Clinical applications of predictive medicine by Cytomics will include multi organ failure in sepsis or non infectious posttraumatic shock in intensive care, or the pretherapeutic identification of high risk patients in cancer cytostatic. Early individualized therapy may provide better survival chances for individual patient at concomitant cost containment. Predictive medicine guided early reduction or stop of therapy may lower or abrogate potential therapeutic side effects. Further important aspects of predictive medicine concern the preoperative identification of patients with a tendency for postoperative complications or coronary artery disease patients with an increased tendency for restenosis. As a consequence, better patient care and new forms of inductive scientific hypothesis development based on the interpretation of predictive data patterns are at reach.

Utility of event-related potentials in predicting antidepressant treatment response: An iSPOT-D report.

PubMed

van Dinteren, Rik; Arns, Martijn; Kenemans, Leon; Jongsma, Marijtje L A; Kessels, Roy P C; Fitzgerald, Paul; Fallahpour, Kamran; Debattista, Charles; Gordon, Evian; Williams, Leanne M

2015-11-01

It is essential to improve antidepressant treatment of major depressive disorder (MDD) and one way this could be achieved is by reducing the number of treatment steps by employing biomarkers that can predict treatment outcome. This study investigated differences between MDD patients and healthy controls in the P3 and N1 component from the event-related potential (ERP) generated in a standard two-tone oddball paradigm. Furthermore, the P3 and N1 are investigated as predictors for treatment outcome to three different antidepressants. In the international Study to Predict Optimized Treatment in Depression (iSPOT-D)--a multi-center, international, randomized, prospective practical trial--1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-XR. The study also recruited 336 healthy controls. Treatment response and remission were established after eight weeks using the 17-item Hamilton Rating Scale for Depression. P3 and N1 latencies and amplitudes were analyzed using a peak-picking approach and further replicated by using exact low resolution tomography (eLORETA). A reduced P3 was found in MDD patients compared to controls by a peak-picking analysis. This was validated in a temporal global field power analysis. Source density analysis revealed that the difference in cortical activity originated from the posterior cingulate and parahippocampal gyrus. Male non-responders to venlafaxine-XR had significantly smaller N1 amplitudes than responders. This was demonstrated by both analytical methods. Male non-responders to venlafaxine-XR had less activity originating from the left insular cortex. The observed results are discussed from a neural network viewpoint. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Exploring the Potential of Direct-To-Consumer Genomic Test Data for Predicting Adverse Drug Events.

PubMed

Zhang, Patrick M; Sarkar, Indra Neil

2018-01-01

Recent technological advancements in genetic testing and the growing accessibility of public genomic data provide researchers with a unique avenue to approach personalized medicine. This feasibility study examined the potential of direct-to-consumer (DTC) genomic tests (focusing on 23andMe) in research and clinical applications. In particular, we combined population genetics information from the Personal Genome Project with adverse event reports from AEOLUS and pharmacogenetic information from PharmGKB. Primarily, associations between drugs based on co-occurring genetic variations and associations between variants and adverse events were used to assess the potential for leveraging single nucleotide polymorphism information from 23andMe. The results of this study suggest potential clinical uses of DTC tests in light of potential drug interactions. Furthermore, the results suggest great potential for analyzing associations at a population level to facilitate knowledge discovery in the realm of predicting adverse drug events.

Predictive value of bovine follicular components as markers of oocyte developmental potential.

PubMed

Matoba, Satoko; Bender, Katrin; Fahey, Alan G; Mamo, Solomon; Brennan, Lorraine; Lonergan, Patrick; Fair, Trudee

2014-01-01

The follicle is a unique micro-environment within which the oocyte can develop and mature to a fertilisable gamete. The aim of this study was to investigate the ability of a panel of follicular parameters, including intrafollicular steroid and metabolomic profiles and theca, granulosa and cumulus cell candidate gene mRNA abundance, to predict the potential of bovine oocytes to develop to the blastocyst stage in vitro. Individual follicles were dissected from abattoir ovaries, carefully ruptured under a stereomicroscope and the oocyte was recovered and individually processed through in vitro maturation, fertilisation and culture. The mean (±s.e.m.) follicular concentrations of testosterone (62.8±4.8 ngmL(-1)), progesterone (616.8±31.9 ngmL(-1)) and oestradiol (14.4±2.4 ngmL(-1)) were not different (P>0.05) between oocytes that formed (competent) or failed to form (incompetent) blastocysts. Principal-component analysis of the quantified aqueous metabolites in follicular fluid showed differences between oocytes that formed blastocysts and oocytes that degenerated; l-alanine, glycine and l-glutamate were positively correlated and urea was negatively correlated with blastocyst formation. Follicular fluid associated with competent oocytes was significantly lower in palmitic acid (P=0.023) and total fatty acids (P=0.031) and significantly higher in linolenic acid (P=0.036) than follicular fluid from incompetent oocytes. Significantly higher (P<0.05) transcript abundance of LHCGR in granulosa cells, ESR1 and VCAN in thecal cells and TNFAIP6 in cumulus cells was associated with competent compared with incompetent oocytes.

Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter.

PubMed

Kavuluru, Ramakanth; Sabbir, A K M

2016-06-01

Electronic cigarettes (e-cigarettes or e-cigs) are a popular emerging tobacco product. Because e-cigs do not generate toxic tobacco combustion products that result from smoking regular cigarettes, they are sometimes perceived and promoted as a less harmful alternative to smoking and also as means to quit smoking. However, the safety of e-cigs and their efficacy in supporting smoking cessation is yet to be determined. Importantly, the federal drug administration (FDA) currently does not regulate e-cigs and as such their manufacturing, marketing, and sale is not subject to the rules that apply to traditional cigarettes. A number of manufacturers, advocates, and e-cig users are actively promoting e-cigs on Twitter. We develop a high accuracy supervised predictive model to automatically identify e-cig "proponents" on Twitter and analyze the quantitative variation of their tweeting behavior along popular themes when compared with other Twitter users (or tweeters). Using a dataset of 1000 independently annotated Twitter profiles by two different annotators, we employed a variety of textual features from latest tweet content and tweeter profile biography to build predictive models to automatically identify proponent tweeters. We used a set of manually curated key phrases to analyze e-cig proponent tweets from a corpus of over one million e-cig tweets along well known e-cig themes and compared the results with those generated by regular tweeters. Our model identifies e-cig proponents with 97% precision, 86% recall, 91% F-score, and 96% overall accuracy, with tight 95% confidence intervals. We find that as opposed to regular tweeters that form over 90% of the dataset, e-cig proponents are a much smaller subset but tweet two to five times more than regular tweeters. Proponents also disproportionately (one to two orders of magnitude more) highlight e-cig flavors, their smoke-free and potential harm reduction aspects, and their claimed use in smoking cessation. Given FDA is

An Accurate New Potential Function for Ground-State X{e}_2 from UV and Virial Coefficient Data

NASA Astrophysics Data System (ADS)

Le Roy, Robert J.; Mackie, J. Cameron; Chandrasekhar, Pragna

2011-06-01

Determining accurate analytic pair potentials for rare gas dimers has been a longstanding goal in molecular physics. However, most potential energy functions reported to date fail to optimally represent the available spectroscopic data, in spite of the fact that such data provide constraints of unparalleled precision on the attractive potential energy wells of these species. A recent study of ArXe showed that it is a straightforward matter to combine multi-isotopologue spectroscopic data (in that case, microwave, and high resolution UV measurements) and virial coefficients in a direct fit to obtain a flexible analytic potential function that incorporates the theoretically predicted damped inverse-power long-range behaviour. The present work reports the application of this approach to Xe_2, with a direct fit to high resolution rotationally resolved UV emission data for v''=0 and 1, band head data for v''=0-9, and virial coefficient data for T=165-950 K being used to obtain an accurate new potential energy function for the ground state of this Van der Waals molecule. Analogous results for other rare-gas pairs will also be presented, as time permits. L. Piticco, F. Merkt, A.A. Cholewinski, F.R. McCourt and R.J. Le Roy, J. Mol. Spectrosc. 264, 83 (2010). A. Wüest and K.G. Bruin and F. Merkt, Can. J. Chem. 82, 750 (2004). D.E. Freeman, K. Yoshino, and Y. Tanaka, J. Chem. Phys. 61, 4880 (1974). J.H. Dymond, K.N. Marsh, R.C. Wilhoit and K.C. Wong, in Landold-Börnstein, New Series, Group IV, edited by M. Frenkel and K.N. Marsh, Vol. 21 (2003).

On the use and potential use of seasonal to decadal climate predictions for decision-making in Europe

NASA Astrophysics Data System (ADS)

Soares, Marta Bruno; Dessai, Suraje

2014-05-01

The need for climate information to help inform decision-making in sectors susceptible to climate events and impacts is widely recognised. In Europe, developments in the science and models underpinning the study of climate variability and change have led to an increased interest in seasonal to decadal climate predictions (S2DCP). While seasonal climate forecasts are now routinely produced operationally by a number of centres around the world, decadal climate predictions are still in its infancy restricted to the realm of research. Contrary to other regions of the world, where the use of these types of forecasts, particularly at seasonal timescales, has been pursued in recent years due to higher levels of predictability, little is known about the uptake and climate information needs of end-users regarding S2DCP in Europe. To fill this gap we conducted in-depth interviews with experts and decision-makers across a range of European sectors, a workshop with European climate services providers, and a systematic literature review on the use of S2DCP in Europe. This study is part of the EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale (EUPORIAS) project which aims to develop semi-operational prototypes of impact prediction systems in Europe on seasonal to decadal timescales. We found that the emerging landscape of users and potential users of S2DCP in Europe is complex and heterogeneous. Differences in S2DCP information needs across and within organisations and sectors are largely underpinned by factors such as the institutional and regulatory context of the organisations, the plethora of activities and decision-making processes involved, the level of expertise and capacity of the users, and the availability of resources within the organisations. In addition, although the use of S2DCP across Europe is still fairly limited, particular sectors such as agriculture, health, energy, water, (re)insurance, and transport are taking the lead on

Anti-E1E2 antibodies status prior therapy favors direct-acting antiviral treatment efficacy.

PubMed

Virlogeux, Victor; Berthillon, Pascale; Bordes, Isabelle; Larrat, Sylvie; Crouy, Stéphanie; Scholtès, Caroline; Pradat, Pierre; Maynard, Marianne; Zoulim, Fabien; Leroy, Vincent; Chemin, Isabelle; Trépo, Christian; Petit, Marie-Anne

2018-03-15

Presence of anti-E1E2 antibodies was previously associated with spontaneous cure of hepatitis C virus (HCV) and predictive before treatment of a sustained virological response (SVR) to bi- or tri-therapy in naïve or experienced patients, regardless of HCV genotype. We investigated the impact of anti-E1E2 seroprevalence at baseline on treatment response in patients receiving direct-acting antiviral (DAA) therapy. We screened anti-E1E2 antibodies by ELISA in serum samples collected at treatment initiation for two groups of patients: 59 with SVR at the end of DAA treatment and 44 relapsers after DAA treatment. Nineteen patients received a combination of ribavirin (RBV) or PEG-interferon/ribavirin with sofosbuvir or daclatasvir and others received interferon-free treatment with DAA±RBV. HCV viral load was measured at different time points during treatment in a subgroup of patients. A significant association was observed between presence of anti-E1E2 and HCV viral load<6log10 prior treatment. Among patients with anti-E1E2 at baseline, 70% achieved SVR whereas among patients without anti-E1E2, only 45% achieved SVR. Conversely, 66% of patients experiencing DAA-failure were anti-E1E2 negative at baseline. In the multivariate analysis, presence of anti-E1E2 was significantly associated with SVR after adjustment on potential cofounders such as age, sex, fibrosis stage, prior HCV treatment and alanine aminotransferase (ALT) level. The presence of anti-E1E2 at treatment initiation is a predictive factor of SVR among patients treated with DAA and more likely among patients with low initial HCV viral load (<6log10). Absence of anti-E1E2 at baseline could predict DAA-treatment failure. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

The Role of Rad17 in DNA Damage Checkpoint Signaling and Initiation of Apoptosis in Mammary Cells

DTIC Science & Technology

2005-07-01

Shen et al. 1998). Antibodies to β-catenin (Transduction lab ), GSK-3β (Santa Cruz), Smad3 I-20 (Santa Cruz), Smad3 (Zymed), phspho-Smad2 (Cell...van de Wetering, M., R. Cavallo, D. Dooijes, M. van Beest , J. van Es, J. Loureiro, A. Ypma, D. Hursh, T. Jones, A. Bejsovec, M. Peifer, M. Mortin

West Europe Report, Science and Technology, No. 136.

DTIC Science & Technology

1983-02-01

their barriers and work with the large enterprises (Pechiney, Sanofi , Rhone- Poulenc) or the small and medium-size industrial enterprises on specific...traditional products of the agro-nutritional industries, —production of amino acids, antibiotics, vitamins, vaccines , hormones, en- zymes and...systems engineering; 4. Production of bioreagents for analysis, vaccines , monoclonal antibodies, and new cell-derived products for therapeutic

THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY ...

EPA Pesticide Factsheets

A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. These approaches are less well-suited, however, to the challenges of global toxicity prediction, i.e., to predicting the potential toxicity of structurally diverse chemicals across a wide range of end points of regulatory and pharmaceutical concern. New approaches that have the potential to significantly improve capabilities in predictive toxicology are elaborating the “activity” portion of the SAR paradigm. Recent advances in two areas of endeavor are particularly promising. Toxicity data informatics relies on standardized data schema, developed for particular areas of toxicological study, to facilitate data integration and enable relational exploration and mining of data across both historical and new areas of toxicological investigation. Bioassay profiling refers to large-scale high-throughput screening approaches that use chemicals as probes to broadly characterize biological response space, extending the concept of chemical “properties” to the biological activity domain. The effective capture and representation of legacy and new toxicity data into mineable form and the large-scale generation of new bioassay data in relation to chemical toxicity, both employing chemical stru

Predictive Ability from ePortfolios of Student Achievement Associated with Professional Teaching Standards: An Exploratory Case Study

ERIC Educational Resources Information Center

Payne, Phillip; Burrack, Frederick

2017-01-01

This exploratory case study, focused on a music teacher preparation program, examined the coursework ePortfolios of pre-service music teachers to determine if any parts of the ePortfolio process predicted teaching effectiveness in the classroom during the student teaching semester. Sixty-five undergraduate pre-service music teachers made up the…

Assessing predictive services' 7-day fire potential outlook

Treesearch

Karin Riley; Crystal Stonesifer; Dave Calkin; Haiganoush Preisler

2015-01-01

The Predictive Services program was created under the National Wildfire Coordinating Group in 2001 to address the need for long- and short-term decision support information for fire managers and operations personnel. The primary mission of Predictive Services is to integrate fire weather, fire danger, and resource availability to enable strategic fire suppression...

Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization

PubMed Central

Yanez, Livia Z.; Han, Jinnuo; Behr, Barry B.; Pera, Renee A. Reijo; Camarillo, David B.

2016-01-01

The causes of embryonic arrest during pre-implantation development are poorly understood. Attempts to correlate patterns of oocyte gene expression with successful embryo development have been hampered by the lack of reliable and nondestructive predictors of viability at such an early stage. Here we report that zygote viscoelastic properties can predict blastocyst formation in humans and mice within hours after fertilization, with >90% precision, 95% specificity and 75% sensitivity. We demonstrate that there are significant differences between the transcriptomes of viable and non-viable zygotes, especially in expression of genes important for oocyte maturation. In addition, we show that low-quality oocytes may undergo insufficient cortical granule release and zona-hardening, causing altered mechanics after fertilization. Our results suggest that embryo potential is largely determined by the quality and maturation of the oocyte before fertilization, and can be predicted through a minimally invasive mechanical measurement at the zygote stage. PMID:26904963

Learning a Continuous-Time Streaming Video QoE Model.

PubMed

Ghadiyaram, Deepti; Pan, Janice; Bovik, Alan C

2018-05-01

Over-the-top adaptive video streaming services are frequently impacted by fluctuating network conditions that can lead to rebuffering events (stalling events) and sudden bitrate changes. These events visually impact video consumers' quality of experience (QoE) and can lead to consumer churn. The development of models that can accurately predict viewers' instantaneous subjective QoE under such volatile network conditions could potentially enable the more efficient design of quality-control protocols for media-driven services, such as YouTube, Amazon, Netflix, and so on. However, most existing models only predict a single overall QoE score on a given video and are based on simple global video features, without accounting for relevant aspects of human perception and behavior. We have created a QoE evaluator, called the time-varying QoE Indexer, that accounts for interactions between stalling events, analyzes the spatial and temporal content of a video, predicts the perceptual video quality, models the state of the client-side data buffer, and consequently predicts continuous-time quality scores that agree quite well with human opinion scores. The new QoE predictor also embeds the impact of relevant human cognitive factors, such as memory and recency, and their complex interactions with the video content being viewed. We evaluated the proposed model on three different video databases and attained standout QoE prediction performance.

Statistical Mining of Predictability of Seasonal Precipitation over the United States

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

2001-01-01

Results from a new ensemble canonical correlation (ECC) prediction model yield a remarkable (10-20%) increases in baseline prediction skills for seasonal precipitation over the US for all seasons, compared to traditional statistical predictions. While the tropical Pacific, i.e., El Nino, contributes to the largest share of potential predictability in the southern tier States during boreal winter, the North Pacific and the North Atlantic are responsible for enhanced predictability in the northern Great Plains, Midwest and the southwest US during boreal summer. Most importantly, ECC significantly reduces the spring predictability barrier over the conterminous US, thereby raising the skill bar for dynamical predictions.

Prediction of specific depressive symptom clusters in youth with epilepsy: The NDDI-E-Y versus Neuro-QOL SF.

PubMed

Kellermann, Tanja S; Mueller, Martina; Carter, Emma G; Brooks, Byron; Smith, Gigi; Kopp, Olivia J; Wagner, Janelle L

2017-08-01

Proper assessment and early identification of depressive symptoms are essential to initiate treatment and minimize the risk for poor outcomes in youth with epilepsy (YWE). The current study examined the predictive utility of the Neurological Disorders Depression Inventory-Epilepsy for Youth (NDDI-E-Y) and the Neuro-QOL Depression Short Form (Neuro-QOL SF) in explaining variance in overall depressive symptoms and specific symptom clusters on the gold standard Children's Depression Inventory-2 (CDI-2). Cross-sectional study examining 99 YWE (female 68, mean age 14.7 years) during a routine epilepsy visit, who completed self-report measures of depressive symptoms, including the NDDI-E-Y, CDI-2, and the Neuro-QOL SF. Caregivers completed a measure of seizure severity. All sociodemographic and medical information was evaluated through electronic medical record review. After accounting for seizure and demographic variables, the NDDI-E-Y accounted for 45% of the variance in the CDI-2 Total score and the CDI-2 Ineffectiveness subscale. Furthermore, the NDDI-E-Y predicted CDI-2 Total scores and subscales similarly, with the exception of explaining significantly more variance in the CDI-2 Ineffectiveness subscale compared to the Negative Mood subscale. The NDDI-E-Y explained greater variance compared to Neuro-QOL SF across the Total (48% vs. 37%) and all CDI-2 subscale scores; however, the NDDI-E-Y emerged as a stronger predictor of only CDI-2 Ineffectiveness. Both the NDDI-E-Y and Neuro-QOL SF accounted for the lowest amount of variance in CDI-2 Negative Mood. Sensitivity was poor for the Neuro-QOL SF in predicting high versus low CDI-2 scores. The NDDI-E-Y has strong psychometrics and can be easily integrated into routine epilepsy care for quick, brief screening of depressive symptoms in YWE. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

E-Cigarettes and Potential Implications for Plastic Surgery.

PubMed

Taub, Peter J; Matarasso, Alan

2016-12-01

The use of tobacco-based products, most notably cigarettes, is related directly to wound healing problems and poorer outcomes in plastic surgery. Current abstracts have highlighted the potential complications from nicotine, specifically following plastic surgery in patients who choose to smoke. Recently, products that use electricity to vaporize liquid nitrogen have been gaining popularity. New rules were recently proposed that would give the federal government authority over electronic cigarettes. However, the health-related issues surrounding e-cigarettes are still largely unknown or misunderstood. These issues also extend to their impact on surgical procedures, notably their effect on plastic surgical procedures that rely heavily on the vascularity of either the host wound bed or the replacement tissue.

Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects.

PubMed

Belaire, J Amy; Kreakie, Betty J; Keitt, Timothy; Minor, Emily

2014-04-01

Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site-selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions. © 2013 Society for Conservation Biology.

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers.

PubMed

Nmor, Jephtha C; Sunahara, Toshihiko; Goto, Kensuke; Futami, Kyoko; Sonye, George; Akweywa, Peter; Dida, Gabriel; Minakawa, Noboru

2013-01-16

varied. Drains, foot-prints, puddles and swamp habitat types were most predictable. Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Predicted Outcome Value of E-Mail Communication: Factors that Foster Professional Relational Development between Students and Teachers

ERIC Educational Resources Information Center

Young, Stacy; Kelsey, Dawn; Lancaster, Alexander

2011-01-01

Using predicted outcome value theory as a guide, this study investigated the link between e-mail correspondence as a form of computer mediated extra class communication and how it may shape students' desire to foster student-teacher relational development. The findings revealed that when students believe their teacher e-mails the class frequently,…

High-Energy Polarization: Scientific Potential and Model Predictions

DOE PAGES

Zhang, Haocheng

2017-07-28

Understanding magnetic field strength and morphology is very important for studying astrophysical jets. Polarization signatures have been a standard way to probe the jet magnetic field. Radio and optical polarization monitoring programs have been very successful in studying the space- and time-dependent jet polarization behaviors. A new era is now arriving with high-energy polarimetry. X-ray and γ-ray polarimetry can probe the most active jet regions with the most efficient particle acceleration. This new opportunity will make a strong impact on our current understanding of jet systems. Here, this article summarizes the scientific potential and current model predictions for X-ray andmore » γ-ray polarization of astrophysical jets. In particular, we discuss the advantages of using high-energy polarimetry to constrain several important problems in the jet physics, including the jet radiation mechanisms, particle acceleration mechanisms, and jet kinetic and magnetic energy composition. Here we take blazars as a study case, but the general approach can be similarly applied to other astrophysical jets. We conclude that by comparing combined magnetohydrodynamics (MHD), particle transport, and polarization-dependent radiation transfer simulations with multi-wavelength time-dependent radiation and polarization observations, we will obtain the strongest constraints and the best knowledge of jet physics.« less

High-Energy Polarization: Scientific Potential and Model Predictions

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

Zhang, Haocheng

Understanding magnetic field strength and morphology is very important for studying astrophysical jets. Polarization signatures have been a standard way to probe the jet magnetic field. Radio and optical polarization monitoring programs have been very successful in studying the space- and time-dependent jet polarization behaviors. A new era is now arriving with high-energy polarimetry. X-ray and γ-ray polarimetry can probe the most active jet regions with the most efficient particle acceleration. This new opportunity will make a strong impact on our current understanding of jet systems. Here, this article summarizes the scientific potential and current model predictions for X-ray andmore » γ-ray polarization of astrophysical jets. In particular, we discuss the advantages of using high-energy polarimetry to constrain several important problems in the jet physics, including the jet radiation mechanisms, particle acceleration mechanisms, and jet kinetic and magnetic energy composition. Here we take blazars as a study case, but the general approach can be similarly applied to other astrophysical jets. We conclude that by comparing combined magnetohydrodynamics (MHD), particle transport, and polarization-dependent radiation transfer simulations with multi-wavelength time-dependent radiation and polarization observations, we will obtain the strongest constraints and the best knowledge of jet physics.« less

Stress state and movement potential of the Kar-e-Bas fault zone, Fars, Iran

NASA Astrophysics Data System (ADS)

Sarkarinejad, Khalil; Zafarmand, Bahareh

2017-08-01

The Kar-e-Bas or Mengharak basement-inverted fault is comprised of six segments in the Zagros foreland folded belt of Iran. In the Fars region, this fault zone associated with the Kazerun, Sabz-Pushan and Sarvestan faults serves as a lateral transfer zone that accommodates the change in shortening direction from the western central to the eastern Zagros. This study evaluates the recent tectonic stress regime of the Kar-e-Bas fault zone based on inversion of earthquake focal mechanism data, and quantifies the fault movement potential of this zone based on the relationship between fault geometric characteristics and recent tectonic stress regimes. The trend and plunge of σ 1 and σ 3 are S25°W/04°-N31°E/05° and S65°E/04°-N60°W/10°, respectively, with a stress ratio of Φ = 0.83. These results are consistent with the collision direction of the Afro-Arabian continent and the Iranian microcontinent. The near horizontal plunge of maximum and minimum principle stresses and the value of stress ratio Φ indicate that the state of stress is nearly strike-slip dominated with little relative difference between the value of two principal stresses, σ 1 and σ 2. The obliquity of the maximum compressional stress into the fault trend reveals a typical stress partitioning of thrust and strike-slip motion in the Kar-e-Bas fault zone. Analysis of the movement potential of this fault zone shows that its northern segment has a higher potential of fault activity (0.99). The negligible difference between the fault-plane dips of the segments indicates that their strike is a controlling factor in the changes in movement potential.

Human Pluripotent Stem Cell-Based Assay Predicts Developmental Toxicity Potential of ToxCast Chemicals (ACT meeting)

EPA Science Inventory

Worldwide initiatives to screen for toxicity potential among the thousands of chemicals currently in use require inexpensive and high-throughput in vitro models to meet their goals. The devTOX quickPredict platform is an in vitro human pluripotent stem cell-based assay used to as...

Ecological Model to Predict Potential Habitats of Oncomelania hupensis, the Intermediate Host of Schistosoma japonicum in the Mountainous Regions, China.

PubMed

Zhu, Hong-Ru; Liu, Lu; Zhou, Xiao-Nong; Yang, Guo-Jing

2015-01-01

Schistosomiasis japonica is a parasitic disease that remains endemic in seven provinces in the People's Republic of China (P.R. China). One of the most important measures in the process of schistosomiasis elimination in P.R. China is control of Oncomelania hupensis, the unique intermediate host snail of Schistosoma japonicum. Compared with plains/swamp and lake regions, the hilly/mountainous regions of schistosomiasis endemic areas are more complicated, which makes the snail survey difficult to conduct precisely and efficiently. There is a pressing call to identify the snail habitats of mountainous regions in an efficient and cost-effective manner. Twelve out of 56 administrative villages distributed with O. hupensis in Eryuan, Yunnan Province, were randomly selected to set up the ecological model. Thirty out of the rest of 78 villages (villages selected for building model were excluded from the villages for validation) in Eryuan and 30 out of 89 villages in Midu, Yunnan Province were selected via a chessboard method for model validation, respectively. Nine-year-average Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) as well as Digital Elevation Model (DEM) covering Eryuan and Midu were extracted from MODIS and ASTER satellite images, respectively. Slope, elevation and the distance from every village to its nearest stream were derived from DEM. Suitable survival environment conditions for snails were defined by comparing historical snail presence data and remote sensing derived images. According to the suitable conditions for snails, environment factors, i.e. NDVI, LST, elevation, slope and the distance from every village to its nearest stream, were integrated into an ecological niche model to predict O. hupensis potential habitats in Eryuan and Midu. The evaluation of the model was assessed by comparing the model prediction and field investigation. Then, the consistency rate of model validation was calculated in Eryuan and Midu

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

DOE PAGES

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane; ...

2017-02-07

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

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

Rinnan, Asmund; Bruun, Sander; Lindedam, Jane

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000more » samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.« less

New approach to predict photoallergic potentials of chemicals based on murine local lymph node assay.

PubMed

Maeda, Yosuke; Hirosaki, Haruka; Yamanaka, Hidenori; Takeyoshi, Masahiro

2018-05-23

Photoallergic dermatitis, caused by pharmaceuticals and other consumer products, is a very important issue in human health. However, S10 guidelines of the International Conference on Harmonization do not recommend the existing prediction methods for photoallergy because of their low predictability in human cases. We applied local lymph node assay (LLNA), a reliable, quantitative skin sensitization prediction test, to develop a new photoallergy prediction method. This method involves a three-step approach: (1) ultraviolet (UV) absorption analysis; (2) determination of no observed adverse effect level for skin phototoxicity based on LLNA; and (3) photoallergy evaluation based on LLNA. Photoallergic potential of chemicals was evaluated by comparing lymph node cell proliferation among groups treated with chemicals with minimal effect levels of skin sensitization and skin phototoxicity under UV irradiation (UV+) or non-UV irradiation (UV-). A case showing significant difference (P < .05) in lymph node cell proliferation rates between UV- and UV+ groups was considered positive for photoallergic reaction. After testing 13 chemicals, seven human photoallergens tested positive and the other six, with no evidence of causing photoallergic dermatitis or UV absorption, tested negative. Among these chemicals, both doxycycline hydrochloride and minocycline hydrochloride were tetracycline antibiotics with different photoallergic properties, and the new method clearly distinguished between the photoallergic properties of these chemicals. These findings suggested high predictability of our method; therefore, it is promising and effective in predicting human photoallergens. Copyright © 2018 John Wiley & Sons, Ltd.

Design and Synthesis of New Peptidomimetics as Potential Inhibitors of MurE.

PubMed

Zivec, Matej; Turk, Samo; Blanot, Didier; Gobec, Stanislav

2011-03-01

With the continuing emergence and spread of multidrug-resistant bacteria, there is an urgent need for the development of new antimicrobial agents. One possible source of new antibacterial targets is the biosynthesis of the bacterial cell-wall peptidoglycan. The assembly of the peptide stem is carried out by four essential enzymes, known as the Mur ligases (MurC, D, E and F). We have designed and synthesised a focused library of compounds as potential inhibitors of UDP-N-acetylmuramoyl-L-alanyl-D-glutamate:L-lysine ligase (MurE) from Staphylococcus aureus. This was achieved using two approaches: (i) synthesis of transition-state analogues based on the methyleneamino core; and (ii) synthesis of MurE reaction product analogues. Two methyleneamino-based compounds are identified as initial hits for inhibitors of MurE.

Potential Consequences of E-Cigarette Use: Is Youth Health Going Up in Smoke

DTIC Science & Technology

2016-10-01

composed of adolescents and young adults. Using data from the National Youth Tobacco Survey (NYTS), we examine trends over time in the use of traditional...among NYTS respondents participating in the 2011-2014 survey waves. By comparing the accuracy of these predictions across e-cigarette users and...This study draws on existing literature and publicly available data from the National Youth Tobacco Survey (NYTS) to address three fundamental

Predicting Internet/E-Commerce Use.

ERIC Educational Resources Information Center

Sexton, Randall S.; Johnson, Richard A.; Hignite, Michael A.

2002-01-01

Describes a study that analyzed variables in order to identify accurate predictors of individuals' use of the Internet and e-commerce. Results of survey research and a neural network identifies gender, overall computer use, job-related use, and home access as important characteristics that should influence use of the Internet and e-commerce.…

Probing 6D operators at future e - e + colliders

NASA Astrophysics Data System (ADS)

Chiu, Wen Han; Leung, Sze Ching; Liu, Tao; Lyu, Kun-Feng; Wang, Lian-Tao

2018-05-01

We explore the sensitivities at future e - e + colliders to probe a set of six-dimensional operators which can modify the SM predictions on Higgs physics and electroweak precision measurements. We consider the case in which the operators are turned on simultaneously. Such an analysis yields a "conservative" interpretation on the collider sensitivities, complementary to the "optimistic" scenario where the operators are individually probed. After a detail analysis at CEPC in both "conservative" and "optimistic" scenarios, we also considered the sensitivities for FCC-ee and ILC. As an illustration of the potential of constraining new physics models, we applied sensitivity analysis to two benchmarks: holographic composite Higgs model and littlest Higgs model.

Are Learning Style Preferences of Health Science Students Predictive of Their Attitudes towards E-Learning?

ERIC Educational Resources Information Center

Brown, Ted; Zoghi, Maryam; Williams, Brett; Jaberzadeh, Shapour; Roller, Louis; Palermo, Claire; McKenna, Lisa; Wright, Caroline; Baird, Marilyn; Schneider-Kolsky, Michal; Hewitt, Lesley; Sim, Jenny; Holt, Tangerine-Ann

2009-01-01

The objective for this study was to determine whether learning style preferences of health science students could predict their attitudes to e-learning. A survey comprising the "Index of Learning Styles" (ILS) and the "Online Learning Environment Survey" (OLES) was distributed to 2885 students enrolled in 10 different health…

Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system.

PubMed

Aiba née Kaneko, Maki; Hirota, Morihiko; Kouzuki, Hirokazu; Mori, Masaaki

2015-02-01

Genotoxicity is the most commonly used endpoint to predict the carcinogenicity of chemicals. The International Conference on Harmonization (ICH) M7 Guideline on Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk offers guidance on (quantitative) structure-activity relationship ((Q)SAR) methodologies that predict the outcome of bacterial mutagenicity assay for actual and potential impurities. We examined the effectiveness of the (Q)SAR approach with the combination of DEREK NEXUS as an expert rule-based system and ADMEWorks as a statistics-based system for the prediction of not only mutagenic potential in the Ames test, but also genotoxic potential in mutagenicity and clastogenicity tests, using a data set of 342 chemicals extracted from the literature. The prediction of mutagenic potential or genotoxic potential by DEREK NEXUS or ADMEWorks showed high values of sensitivity and concordance, while prediction by the combination of DEREK NEXUS and ADMEWorks (battery system) showed the highest values of sensitivity and concordance among the three methods, but the lowest value of specificity. The number of false negatives was reduced with the battery system. We also separately predicted the mutagenic potential and genotoxic potential of 41 cosmetic ingredients listed in the International Nomenclature of Cosmetic Ingredients (INCI) among the 342 chemicals. Although specificity was low with the battery system, sensitivity and concordance were high. These results suggest that the battery system consisting of DEREK NEXUS and ADMEWorks is useful for prediction of genotoxic potential of chemicals, including cosmetic ingredients.

An assay that may predict the development of IgG enhancing allergen-specific IgE binding during birch immunotherapy

PubMed Central

Selb, R.; Eckl-Dorna, J.; Vrtala, S.; Valenta, R.; Niederberger, V.

2017-01-01

Background It has been shown that birch pollen immunotherapy can induce IgG antibodies which enhance IgE binding to Bet v 1. We aimed to develop a serological assay to predict the development of antibodies which enhance IgE binding to Bet v 1 during immunotherapy. Methods In 18 patients treated by Bet v 1-fragment-specific immunotherapy, the effects of IgG antibodies specific for the fragments on the binding of IgE antibodies to Bet v 1 were measured by ELISA. Blocking and possible enhancing effects on IgE binding were compared with skin sensitivity to Bet v 1 after treatment. Results We found that fragment-specific IgG enhanced IgE binding to Bet v 1 in two patients who also showed an increase of skin sensitivity to Bet v 1. Conclusion Our results indicate that it may be possible to develop serological tests which predict the induction of unfavourable IgG antibodies enhancing the binding of IgE to Bet v 1 during immunotherapy. PMID:23998344

Improving the Accuracy of a Heliocentric Potential (HCP) Prediction Model for the Aviation Radiation Dose

NASA Astrophysics Data System (ADS)

Hwang, Junga; Yoon, Kyoung-Won; Jo, Gyeongbok; Noh, Sung-Jun

2016-12-01

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

Cyber-Management of People with Chronic Disease: A Potential Solution to eHealth Challenges

ERIC Educational Resources Information Center

Laakso, E-Liisa; Armstrong, Kylie; Usher, Wayne

2012-01-01

The evolving eHealth agenda presents a range of potential opportunities for the management and prevention of chronic disease. This paper identifies issues and barriers to the uptake of eHealth and describes a strategy ("Healthy Outcomes for Australians"[C]-HOFA) for creating a central knowledge filter and cyber space method for tracking…

Validation of pathological grading systems for predicting metastatic potential in pheochromocytoma and paraganglioma

PubMed Central

Koh, Jung-Min; Ahn, Seong Hee; Kim, Hyeonmok; Kim, Beom-Jun; Sung, Tae-Yon; Kim, Young Hoon; Hong, Suck Joon; Song, Dong Eun

2017-01-01

Purpose The Grading system for Adrenal Pheochromocytoma and Paraganglioma (GAPP) was proposed for predicting the metastatic potential of pheochromocytoma and paraganglioma to overcome the limitations of the Pheochromocytoma of the Adrenal Scaled Score (PASS). However, to date, no study validating the GAPP has been conducted, and previous studies did not include mutations in the succinate dehydrogenase type B (SDHB) gene in the score calculation. In this retrospective cohort study, we validated the prediction ability of GAPP and assessed whether it would be improved by inclusion of the loss of SDHB immunohistochemical staining. Methods We divided the tumors into non-metastatic and metastatic groups based on the presence of synchronous or metachronous metastases. The GAPP score and PASS at the initial operation were measured. Moreover, we combined some GAPP parameters with the immunohistochemical staining of SDHB to obtain a modified GAPP (M-GAPP) score. Results Metastasis occurred in 15/72 (20.8%) patients, with a mean follow-up of 43.5 months. Loss of SDHB staining was more frequent (P = 0.044) in the metastatic group. The GAPP score (P = 0.006), PASS (P = 0.003), and M-GAPP score (P<0.001) were all higher in the metastatic group. Twelve of 40 (30.0%) moderately or poorly differentiated tumors, as defined by the GAPP score, and 12/34 (35.3%) tumors with a PASS ≥4 were metastatic. Conversely, 10/19 (52.6%) tumors with an M-GAPP score ≥3 were metastatic. The area under the curve of the M-GAPP score (0.822) was significantly higher than that of the GAPP (0.728) (P = 0.012), but similar to that of the PASS (0.753) (P = 0.411). The GAPP (P = 0.032) and M-GAPP scores (P = 0.040), but not PASS (P = 0.200), negatively correlated with metastasis-free survival. Conclusion The GAPP was validated, and M-GAPP, a combination of some GAPP parameters and loss of SDHB staining, might be useful for the prediction of the metastatic potential of pheochromocytoma and paraganglioma

Predictive Models for Carcinogenicity and Mutagenicity ...

EPA Pesticide Factsheets

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

Structural insights into Cydia pomonella pheromone binding protein 2 mediated prediction of potentially active semiochemicals

NASA Astrophysics Data System (ADS)

Tian, Zhen; Liu, Jiyuan; Zhang, Yalin

2016-03-01

Given the advantages of behavioral disruption application in pest control and the damage of Cydia pomonella, due progresses have not been made in searching active semiochemicals for codling moth. In this research, 31 candidate semiochemicals were ranked for their binding potential to Cydia pomonella pheromone binding protein 2 (CpomPBP2) by simulated docking, and this sorted result was confirmed by competitive binding assay. This high predicting accuracy of virtual screening led to the construction of a rapid and viable method for semiochemicals searching. By reference to binding mode analyses, hydrogen bond and hydrophobic interaction were suggested to be two key factors in determining ligand affinity, so is the length of molecule chain. So it is concluded that semiochemicals of appropriate chain length with hydroxyl group or carbonyl group at one head tended to be favored by CpomPBP2. Residues involved in binding with each ligand were pointed out as well, which were verified by computational alanine scanning mutagenesis. Progress made in the present study helps establish an efficient method for predicting potentially active compounds and prepares for the application of high-throughput virtual screening in searching semiochemicals by taking insights into binding mode analyses.

Structural insights into Cydia pomonella pheromone binding protein 2 mediated prediction of potentially active semiochemicals

PubMed Central

Tian, Zhen; Liu, Jiyuan; Zhang, Yalin

2016-01-01

Given the advantages of behavioral disruption application in pest control and the damage of Cydia pomonella, due progresses have not been made in searching active semiochemicals for codling moth. In this research, 31 candidate semiochemicals were ranked for their binding potential to Cydia pomonella pheromone binding protein 2 (CpomPBP2) by simulated docking, and this sorted result was confirmed by competitive binding assay. This high predicting accuracy of virtual screening led to the construction of a rapid and viable method for semiochemicals searching. By reference to binding mode analyses, hydrogen bond and hydrophobic interaction were suggested to be two key factors in determining ligand affinity, so is the length of molecule chain. So it is concluded that semiochemicals of appropriate chain length with hydroxyl group or carbonyl group at one head tended to be favored by CpomPBP2. Residues involved in binding with each ligand were pointed out as well, which were verified by computational alanine scanning mutagenesis. Progress made in the present study helps establish an efficient method for predicting potentially active compounds and prepares for the application of high-throughput virtual screening in searching semiochemicals by taking insights into binding mode analyses. PMID:26928635

The Impact Hazard in the Context of Other Natural Hazards and Predictive Science

NASA Astrophysics Data System (ADS)

Chapman, C. R.

1998-09-01

The hazard due to impact of asteroids and comets has been recognized as analogous, in some ways, to other infrequent but consequential natural hazards (e.g. floods and earthquakes). Yet, until recently, astronomers and space agencies have felt no need to do what their colleagues and analogous agencies must do in order the assess, quantify, and communicate predictions to those with a practical interest in the predictions (e.g. public officials who must assess the threats, prepare for mitigation, etc.). Recent heightened public interest in the impact hazard, combined with increasing numbers of "near misses" (certain to increase as Spaceguard is implemented) requires that astronomers accept the responsibility to place their predictions and assessments in terms that may be appropriately considered. I will report on preliminary results of a multi-year GSA/NCAR study of "Prediction in the Earth Sciences: Use and Misuse in Policy Making" in which I have represented the impact hazard, while others have treated earthquakes, floods, weather, global climate change, nuclear waste disposal, acid rain, etc. The impact hazard presents an end-member example of a natural hazard, helping those dealing with more prosaic issues to learn from an extreme. On the other hand, I bring to the astronomical community some lessons long adopted in other cases: the need to understand the policy purposes of impact predictions, the need to assess potential societal impacts, the requirements to very carefully assess prediction uncertainties, considerations of potential public uses of the predictions, awareness of ethical considerations (e.g. conflicts of interest) that affect predictions and acceptance of predictions, awareness of appropriate means for publicly communicating predictions, and considerations of the international context (especially for a hazard that knows no national boundaries).

Efficient searching and annotation of metabolic networks using chemical similarity.

PubMed

Pertusi, Dante A; Stine, Andrew E; Broadbelt, Linda J; Tyo, Keith E J

2015-04-01

The urgent need for efficient and sustainable biological production of fuels and high-value chemicals has elicited a wave of in silico techniques for identifying promising novel pathways to these compounds in large putative metabolic networks. To date, these approaches have primarily used general graph search algorithms, which are prohibitively slow as putative metabolic networks may exceed 1 million compounds. To alleviate this limitation, we report two methods--SimIndex (SI) and SimZyme--which use chemical similarity of 2D chemical fingerprints to efficiently navigate large metabolic networks and propose enzymatic connections between the constituent nodes. We also report a Byers-Waterman type pathway search algorithm for further paring down pertinent networks. Benchmarking tests run with SI show it can reduce the number of nodes visited in searching a putative network by 100-fold with a computational time improvement of up to 10(5)-fold. Subsequent Byers-Waterman search application further reduces the number of nodes searched by up to 100-fold, while SimZyme demonstrates ∼ 90% accuracy in matching query substrates with enzymes. Using these modules, we have designed and annotated an alternative to the methylerythritol phosphate pathway to produce isopentenyl pyrophosphate with more favorable thermodynamics than the native pathway. These algorithms will have a significant impact on our ability to use large metabolic networks that lack annotation of promiscuous reactions. Python files will be available for download at http://tyolab.northwestern.edu/tools/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

New Biocatalysts: Essential Tools for a Sustainable 21st Century Chemical Industry

DTIC Science & Technology

2005-01-01

ethanol, high fructose corn syrup , citric acid, and amino acids also employ microbes or en- zymes. However, the inroads into commodity chemical...for manufacture of aspartame, and is illustrative of two types of biocatalyst selectivity: chemical and stereoselectivity. High - fructose corn syrup ...Current applications of biocatalysts include the production of high fruc- tose corn syrup , aspartame, semi-synthetic penicillins and award-winning cancer

Response prediction to antidepressants using scalp and source-localized loudness dependence of auditory evoked potential (LDAEP) slopes

PubMed Central

Jaworska, Natalia; Blondeau, Claude; Tessier, Pierre; Norris, Sandhaya; Fusee, Wendy; Blier, Pierre; Knott, Verner

2013-01-01

The loudness-dependence of the auditory evoked potential (LDAEP) slope may be inversely related to serotonin (5-HT) neurotransmission. Thus, steep LDAEPs tend to predict a positive response to selective serotonin reuptake inhibitor (SSRI) antidepressants, which augment 5-HT. However, LDAEPs also predict outcome to antidepressants indirectly altering 5-HT (e.g. bupropion). Hence, the LDAEP’s predicative specificity and sensitivity to antidepressant response/outcome remains elusive. Scalp N1, P2 and N1/P2 LDAEP slopes and standardized low resolution brain electromagnetic tomography (sLORETA)-localized N1 and P2 LDAEP slopes were assessed in depressed individuals (N=51) at baseline, 1 and 12 weeks post-treatment with one of three antidepressant regimens [escitalopram (ESC) + bupropion (BUP), ESC or BUP]. Clinical response was greatest with ESC+BUP at week 1. Treatment responders had steep N1 sLORETA-LDAEP baseline slopes while non-responders had shallow ones. P2 sLORETA-LDAEP slope increases at 1 week existed in responders; decreases were noted in non-responders. Exploratory analyses indicated that more BUP and ESC responders versus non-responders had steep baseline N1 sLORETA-LDAEP slopes. Additionally, slight decreases in scalp P2 LDAEP by week 1 existed for ESC treatment, while slope increases existed with ESC+BUP treatment. Only baseline N1 sLORETA-LDAEP discriminated treatment responders/non-responders. This work confirms that certain LDAEP measures are associated with treatment outcome and appear to be differentially modulated with varying antidepressant drug regimens, though this should be confirmed using larger samples. PMID:23360662

20170921 - An evaluation of selected (Q)SARs/expert systems for the Prediction of Skin Sensitization Potential (ASCCT)

EPA Science Inventory

Predictive testing to characterize substances for their skin sensitization potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximization Test (GPMT). In recent years, EU regulations have provided a strong incentiv...

Human Papillomavirus DNA Methylation Predicts Response to Treatment Using Cidofovir and Imiquimod in Vulval Intraepithelial Neoplasia 3.

PubMed

Jones, Sadie E F; Hibbitts, Samantha; Hurt, Christopher N; Bryant, Dean; Fiander, Alison N; Powell, Ned; Tristram, Amanda J

2017-09-15

Purpose: Response rates to treatment of vulval intraepithelial neoplasia (VIN) with imiquimod and cidofovir are approximately 57% and 61%, respectively. Treatment is associated with significant side effects and, if ineffective, risk of malignant progression. Treatment response is not predicted by clinical factors. Identification of a biomarker that could predict response is an attractive prospect. This work investigated HPV DNA methylation as a potential predictive biomarker in this setting. Experimental Design: DNA from 167 cases of VIN 3 from the RT3 VIN clinical trial was assessed. HPV-positive cases were identified using Greiner PapilloCheck and HPV 16 type-specific PCR. HPV DNA methylation status was assessed in three viral regions: E2, L1/L2, and the promoter, using pyrosequencing. Results: Methylation of the HPV E2 region was associated with response to treatment. For cidofovir ( n = 30), median E2 methylation was significantly higher in patients who responded ( P ≤ 0.0001); E2 methylation >4% predicted response with 88.2% sensitivity and 84.6% specificity. For imiquimod ( n = 33), median E2 methylation was lower in patients who responded to treatment ( P = 0.03; not significant after Bonferroni correction); E2 methylation <4% predicted response with 70.6% sensitivity and 62.5% specificity. Conclusions: These data indicate that cidofovir and imiquimod may be effective in two biologically defined groups. HPV E2 DNA methylation demonstrated potential as a predictive biomarker for the treatment of VIN with cidofovir and may warrant investigation in a biomarker-guided clinical trial. Clin Cancer Res; 23(18); 5460-8. ©2017 AACR . ©2017 American Association for Cancer Research.

[Prediction of the potential distribution of Tibetan medicinal Lycium ruthenicum in context of climate change].

PubMed

Lin, Li; Jin, Ling; Wang, Zhen-Heng; Cui, Zhi-Jia; Ma, Yi

2017-07-01

To predict the suitable distribution patterns of Lycium ruthenicum in the present and future under the background of climate change, and provide reference for the resources sustainable utilization and GAP standardized planting. The software of Maxent and ArcGis was used to predict the potential suitable regions and grades of L. ruthenicum in China based on the 149 distribution information, climate data of contemporary (1950-2000) and future (20-80 decade of 21 century), and considering of three greenhouse gaseous emission scenario. The results showed that：the suitable distribution regions of L. ruthenicum are mainly concentrated in Xinjiang, Qinghai, Gansu, Neimenggu, and Ningxia province in present. In addition, Shaanxi, Shanxi and Xizang are also distribution regions.The suitable distribution area of L. ruthenicum is 284.506 949×104 km2, accounted for 29.6% of the land area of China.The relatively stable area of the suitable regions accounted for 25.2% of the total suitable region area.Under the background of climate change, compared with contemporary, the total area of suitable region is reducing and moderately suitable area is increasing at different degree at the 20, 30, 40, 50, 60, 70, 80 decade of 21 century. Climate change both can change the total area of suitable regions and habitat suitability of L. ruthenicum. It could provide a strategic guidance for protection, development and utilization of L. ruthenicum though the prediction of potential suitable regions distribution of L. ruthenicum based on the mainly factor of climate change. Copyright© by the Chinese Pharmaceutical Association.

The potential of eHealth in otorhinolaryngology-head and neck surgery: patients' perspectives.

PubMed

Holderried, Martin; Ernst, C; Holderried, F; Rieger, M; Blumenstock, G; Tropitzsch, A

2017-07-01

The use of modern information and communication technologies (ICT) in daily life has significantly increased during the last several years. These essential online technologies have also found their way into the healthcare system. The use of modern ICT for health reasons can be summarized by the term 'eHealth'. Despite the potential importance of eHealth in the field of otorhinolaryngology (ORL), there is little understanding of patients' attitudes towards the deeper integration of these technologies into intersectoral care. The aim of this study was to gain a better understanding of patients' attitudes towards the use of modern ICT for intersectoral communication and information transfer in the field of ORL. Therefore, a structured interview was developed by an interdisciplinary team of otorhinolaryngologists, public health researchers, and information technology (IT) specialists. Overall, 211 ORL patients were interviewed at the Department of Otorhinolaryngology-Head and Neck Surgery, Tuebingen University Hospital, Germany, and 203 of these patients completed the interview. This study revealed ORL patients' perspectives on the potential of eHealth, especially for appointment scheduling, appointment reminders, and intersectoral communication of personal medical information. Furthermore, this study provides evidence that data security and the impacts of eHealth on the physician-patient relationship and on treatment quality warrant special attention in future research.

Prediction of STN-DBS Electrode Implantation Track in Parkinson's Disease by Using Local Field Potentials

PubMed Central

Telkes, Ilknur; Jimenez-Shahed, Joohi; Viswanathan, Ashwin; Abosch, Aviva; Ince, Nuri F.

2016-01-01

Optimal electrophysiological placement of the DBS electrode may lead to better long term clinical outcomes. Inter-subject anatomical variability and limitations in stereotaxic neuroimaging increase the complexity of physiological mapping performed in the operating room. Microelectrode single unit neuronal recording remains the most common intraoperative mapping technique, but requires significant expertise and is fraught by potential technical difficulties including robust measurement of the signal. In contrast, local field potentials (LFPs), owing to their oscillatory and robust nature and being more correlated with the disease symptoms, can overcome these technical issues. Therefore, we hypothesized that multiple spectral features extracted from microelectrode-recorded LFPs could be used to automate the identification of the optimal track and the STN localization. In this regard, we recorded LFPs from microelectrodes in three tracks from 22 patients during DBS electrode implantation surgery at different depths and aimed to predict the track selected by the neurosurgeon based on the interpretation of single unit recordings. A least mean square (LMS) algorithm was used to de-correlate LFPs in each track, in order to remove common activity between channels and increase their spatial specificity. Subband power in the beta band (11–32 Hz) and high frequency range (200–450 Hz) were extracted from the de-correlated LFP data and used as features. A linear discriminant analysis (LDA) method was applied both for the localization of the dorsal border of STN and the prediction of the optimal track. By fusing the information from these low and high frequency bands, the dorsal border of STN was localized with a root mean square (RMS) error of 1.22 mm. The prediction accuracy for the optimal track was 80%. Individual beta band (11–32 Hz) and the range of high frequency oscillations (200–450 Hz) provided prediction accuracies of 72 and 68% respectively. The best

Hydrophobic potential of mean force as a solvation function for protein structure prediction.

PubMed

Lin, Matthew S; Fawzi, Nicolas Lux; Head-Gordon, Teresa

2007-06-01

We have developed a solvation function that combines a Generalized Born model for polarization of protein charge by the high dielectric solvent, with a hydrophobic potential of mean force (HPMF) as a model for hydrophobic interaction, to aid in the discrimination of native structures from other misfolded states in protein structure prediction. We find that our energy function outperforms other reported scoring functions in terms of correct native ranking for 91% of proteins and low Z scores for a variety of decoy sets, including the challenging Rosetta decoys. This work shows that the stabilizing effect of hydrophobic exposure to aqueous solvent that defines the HPMF hydration physics is an apparent improvement over solvent-accessible surface area models that penalize hydrophobic exposure. Decoys generated by thermal sampling around the native-state basin reveal a potentially important role for side-chain entropy in the future development of even more accurate free energy surfaces.

SWAT Model Prediction of Phosphorus Loading in a South Carolina Karst Watershed with a Downstream Embayment

Treesearch

Devendra M. Amatya; Manoj K. Jha; Thomas M. Williams; Amy E. Edwards; Daniel R. Hitchcock

2013-01-01

The SWAT model was used to predict total phosphorus (TP) loadings for a 1555-ha karst watershed—Chapel Branch Creek (CBC)—which drains to a lake via a reservoir-like embayment (R-E). The model was first tested for monthly streamflow predictions from tributaries draining three potential source areas as well as the downstream R-E, followed by TP loadings using data...

Mutational analysis of a predicted double β-propeller domain of the DspA/E effector of Erwinia amylovora.

PubMed

Siamer, Sabrina; Gaubert, Stéphane; Boureau, Tristan; Brisset, Marie-Noëlle; Barny, Marie-Anne

2013-05-01

The bacterium Erwinia amylovora causes fire blight, an invasive disease that threatens apple trees, pear trees and other plants of the Rosaceae family. Erwinia amylovora pathogenicity relies on a type III secretion system and on a single effector DspA/E. This effector belongs to the widespread AvrE family of effectors whose biological function is unknown. In this manuscript, we performed a bioinformatic analysis of DspA/E- and AvrE-related effectors. Motif search identified nuclear localization signals, peroxisome targeting signals, endoplasmic reticulum membrane retention signals and leucine zipper motifs, but none of these motifs were present in all the AvrE-related effectors analysed. Protein threading analysis, however, predicted a conserved double β-propeller domain in the N-terminal part of all the analysed effector sequences. We then performed a random pentapeptide mutagenesis of DspA/E, which led to the characterization of 13 new altered proteins with a five amino acids insertion. Eight harboured the insertion inside the predicted β-propeller domain and six of these eight insertions impaired DspA/E stability or function. Conversely, the two remaining insertions generated proteins that were functional and abundantly secreted in the supernatant suggesting that these two insertions stabilized the protein. © 2013 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Prediction of phospholipidosis-inducing potential of drugs by in vitro biochemical and physicochemical assays followed by multivariate analysis.

PubMed

Kuroda, Yukihiro; Saito, Madoka

2010-03-01

An in vitro method to predict phospholipidosis-inducing potential of cationic amphiphilic drugs (CADs) was developed using biochemical and physicochemical assays. The following parameters were applied to principal component analysis, as well as physicochemical parameters: pK(a) and clogP; dissociation constant of CADs from phospholipid, inhibition of enzymatic phospholipid degradation, and metabolic stability of CADs. In the score plot, phospholipidosis-inducing drugs (amiodarone, propranolol, imipramine, chloroquine) were plotted locally forming the subspace for positive CADs; while non-inducing drugs (chlorpromazine, chloramphenicol, disopyramide, lidocaine) were placed scattering out of the subspace, allowing a clear discrimination between both classes of CADs. CADs that often produce false results by conventional physicochemical or cell-based assay methods were accurately determined by our method. Basic and lipophilic disopyramide could be accurately predicted as a nonphospholipidogenic drug. Moreover, chlorpromazine, which is often falsely predicted as a phospholipidosis-inducing drug by in vitro methods, could be accurately determined. Because this method uses the pharmacokinetic parameters pK(a), clogP, and metabolic stability, which are usually obtained in the early stages of drug development, the method newly requires only the two parameters, binding to phospholipid, and inhibition of lipid degradation enzyme. Therefore, this method provides a cost-effective approach to predict phospholipidosis-inducing potential of a drug. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Prediction of challenge test results by flour-specific IgE and skin prick test in symptomatic bakers.

PubMed

van Kampen, V; Rabstein, S; Sander, I; Merget, R; Brüning, T; Broding, H C; Keller, C; Müsken, H; Overlack, A; Schultze-Werninghaus, G; Walusiak, J; Raulf-Heimsoth, M

2008-07-01

Wheat and rye flours are among the most important allergens causing occupational asthma. Usually, the diagnosis of baker's asthma is based on inhalation challenge tests with flours. To evaluate the relevance of flour-specific serum immunoglobulin E (IgE) and skin prick test (SPT) in the diagnosis of baker's asthma and to define flour-specific IgE concentrations and wheal sizes that allow a prediction of the outcome of challenge testing. Bronchial and nasal challenge tests with wheat (rye) flour were performed in 71 (95) symptomatic bakers. Determinations of flour-specific IgE as well as SPTs were performed in all subjects. Analyses included the calculation of sensitivity, specificity, positive (PPV) and negative predictive values (NPV) at different IgE concentrations and different wheal sizes, and receiver-operating characteristics (ROC) plots with the challenge result as gold standard. Thirty-seven bakers were positive in the challenge with wheat flour, while 63 were positive with rye flour. Depending on the flour-specific IgE concentrations (wheal size), PPV was 74-100% (74-100%) for wheat and 82-100% (91-100%) for rye flour, respectively. The minimal cut-off values with a PPV of 100% were 2.32 kU/l (5.0 mm) for wheat flour and 9.64 kU/l (4.5 mm) for rye flour. The shapes of the ROC plots were similar for wheat and rye flour. High concentrations of flour-specific IgE and clear SPT results in symptomatic bakers are good predictors for a positive challenge test. Challenge tests with flours may be avoided in strongly sensitized bakers.

Beliefs about the Potential Impacts of Exploiting Non-Timber Forest Products Predict Voluntary Participation in Monitoring

NASA Astrophysics Data System (ADS)

Dantas Brites, Alice; Morsello, Carla

2017-06-01

Harvesting and trading non-timber forest products is advocated as a win-win strategy for conservation and development, yet it can produce negative ecological and socioeconomic impacts. Hence, monitoring exploitation outcomes is essential, and participatory monitoring has been suggested to be the most suitable approach. Among possible approaches, participatory monitoring is preferred because it is likely to increase people's awareness and beliefs regarding impacts or potential impacts, thus inducing behavioral changes, although the evidence in this regard is contradictory. We therefore evaluated whether people's beliefs about the potential ecological and socioeconomic impacts of non-timber forest product exploitation increased their likelihood of volunteering to monitor. We studied a community of forest inhabitants in the Brazilian Amazon who harvested and traded a commercially important non-timber forest product. Two methods of data gathering were employed: (i) a survey of 166 adults (51 households) to evaluate people's beliefs and their stated intention to engage in four different monitoring tasks and (ii) four pilot monitoring tasks to evaluate who actually participated. Based on mixed-effects regressions, the results indicated that beliefs regarding both types of impacts could predict participation in certain tasks, although gender, age and schooling were occasionally stronger predictors. On average, people had stronger beliefs about potential socioeconomic impacts than about potential ecological impacts, with the former also predicting participation in ecological data gathering. This finding reinforces the importance of monitoring both types of impacts to help achieve the win-win outcomes originally proposed by non-timber forest product trade initiatives.

Beliefs about the Potential Impacts of Exploiting Non-Timber Forest Products Predict Voluntary Participation in Monitoring.

PubMed

Dantas Brites, Alice; Morsello, Carla

2017-06-01

Harvesting and trading non-timber forest products is advocated as a win-win strategy for conservation and development, yet it can produce negative ecological and socioeconomic impacts. Hence, monitoring exploitation outcomes is essential, and participatory monitoring has been suggested to be the most suitable approach. Among possible approaches, participatory monitoring is preferred because it is likely to increase people's awareness and beliefs regarding impacts or potential impacts, thus inducing behavioral changes, although the evidence in this regard is contradictory. We therefore evaluated whether people's beliefs about the potential ecological and socioeconomic impacts of non-timber forest product exploitation increased their likelihood of volunteering to monitor. We studied a community of forest inhabitants in the Brazilian Amazon who harvested and traded a commercially important non-timber forest product. Two methods of data gathering were employed: (i) a survey of 166 adults (51 households) to evaluate people's beliefs and their stated intention to engage in four different monitoring tasks and (ii) four pilot monitoring tasks to evaluate who actually participated. Based on mixed-effects regressions, the results indicated that beliefs regarding both types of impacts could predict participation in certain tasks, although gender, age and schooling were occasionally stronger predictors. On average, people had stronger beliefs about potential socioeconomic impacts than about potential ecological impacts, with the former also predicting participation in ecological data gathering. This finding reinforces the importance of monitoring both types of impacts to help achieve the win-win outcomes originally proposed by non-timber forest product trade initiatives.

Predicting food challenge outcomes for baked milk: role of specific IgE and skin prick testing.

PubMed

Bartnikas, Lisa M; Sheehan, William J; Hoffman, Elaine B; Permaul, Perdita; Dioun, Anahita F; Friedlander, James; Baxi, Sachin N; Schneider, Lynda C; Phipatanakul, Wanda

2012-11-01

Cow's milk allergy is the most common food allergy in childhood. Many children with IgE-mediated cow's milk allergy may tolerate baked milk products, but few data exist on predictors of outcomes of baked milk challenges. To determine the relation of milk protein allergen specific IgE (sIgE) levels and skin prick test (SPT) wheal size with baked milk challenge outcomes. A retrospective medical record review was conducted of 35 baked milk challenges. SPT results, sIgE levels, demographic characteristics, and food challenge results were analyzed. Thirty-five children underwent open challenges to baked milk and 29 (83%) passed. Of those who failed, 3 (50%) passed the initial clinic challenge but developed symptoms to ongoing exposure at home, days to months later. One child who ultimately failed at home required epinephrine. Compared with those who passed, children who failed were younger (median age, 8.9 and 3.7 years, respectively; P = .02). Children with a milk SPT wheal less than 12 mm were more than 90% likely to pass a baked milk challenge, and no child with a milk SPT wheal less than 7 mm failed a baked milk challenge. We were also able to establish more than 90% predictive values for passing baked milk challenges with a casein SPT wheal of 9 mm, a milk sIgE level of 1.0 kU/L, and a casein sIgE level of 0.9 kU/L. Most children allergic to cow's milk tolerated baked milk. Milk protein SPT wheal may be more reliable than sIgE level in predicting outcomes of baked milk challenges. It is important to be aware of the possibility of late reactions to ongoing baked milk exposure. Copyright © 2012 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

The B 1Πu potential energy curve and dissociation energy of 39K2

NASA Astrophysics Data System (ADS)

Heinze, Johannes; Engelke, Friedrich

1988-07-01

The 39K2 B 1Πu potential energy curve has been determined using laser spectroscopic techniques and quantum mechanical calculations. The dissociation energy is 2407.6±0.5 cm-1 (0.2985±0.0001 eV) including a potential barrier of 298±10 cm-1 (0.037±0.0013 eV) found with its maximum at 8.08±0.05 Å (15.3±0.1 bohr). The long-range behavior matches smoothly onto the form predicted from dispersion forces. The dissociation energy of the ground state X 1Σ+g, obtained by a long-range extrapolation of the vibrational separations, is De =4444±10 cm-1 (0.5506±0.0013 eV), in agreement with recent theoretical prediction.

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

PubMed

Olawoyin, Richard

2016-10-01

The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants. Copyright © 2016 Elsevier Ltd. All rights reserved.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5’-Phosphate Production in E. coli

DOE PAGES

Oberhardt, Matthew A.; Zarecki, Raphy; Reshef, Leah; ...

2016-01-28

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous ‘replacer’ gene rescues lethality caused by inactivation of a ‘target’ gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predictedmore » target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5’-phosphate (the active form of Vitamin B 6), which we validate experimentally via multicopy suppression. Here, we perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.« less

Evaluation of copper, aluminum, and nickel interatomic potentials on predicting the elastic properties

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

Rassoulinejad-Mousavi, Seyed Moein; Mao, Yijin; Zhang, Yuwen, E-mail: zhangyu@missouri.edu

Choice of appropriate force field is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to yield reliable results. Since investigations on the mechanical behavior of materials at micro/nanoscale have been becoming much more widespread, it is necessary to determine an adequate potential which accurately models the interaction of the atoms for desired applications. In this framework, reliability of multiple embedded atom method based interatomic potentials for predicting the elastic properties was investigated. Assessments were carried out for different copper, aluminum, and nickel interatomic potentials at room temperature which is considered asmore » the most applicable case. Examined force fields for the three species were taken from online repositories of National Institute of Standards and Technology, as well as the Sandia National Laboratories, the LAMMPS database. Using molecular dynamic simulations, the three independent elastic constants, C{sub 11}, C{sub 12}, and C{sub 44}, were found for Cu, Al, and Ni cubic single crystals. Voigt-Reuss-Hill approximation was then implemented to convert elastic constants of the single crystals into isotropic polycrystalline elastic moduli including bulk modulus, shear modulus, and Young's modulus as well as Poisson's ratio. Simulation results from massive molecular dynamic were compared with available experimental data in the literature to justify the robustness of each potential for each species. Eventually, accurate interatomic potentials have been recommended for finding each of the elastic properties of the pure species. Exactitude of the elastic properties was found to be sensitive to the choice of the force fields. Those potentials that were fitted for a specific compound may not necessarily work accurately for all the existing pure species. Tabulated results in this paper might be used as a benchmark to increase assurance of using the interatomic potential that was

Evaluation of copper, aluminum, and nickel interatomic potentials on predicting the elastic properties

NASA Astrophysics Data System (ADS)

Rassoulinejad-Mousavi, Seyed Moein; Mao, Yijin; Zhang, Yuwen

2016-06-01

Choice of appropriate force field is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to yield reliable results. Since investigations on the mechanical behavior of materials at micro/nanoscale have been becoming much more widespread, it is necessary to determine an adequate potential which accurately models the interaction of the atoms for desired applications. In this framework, reliability of multiple embedded atom method based interatomic potentials for predicting the elastic properties was investigated. Assessments were carried out for different copper, aluminum, and nickel interatomic potentials at room temperature which is considered as the most applicable case. Examined force fields for the three species were taken from online repositories of National Institute of Standards and Technology, as well as the Sandia National Laboratories, the LAMMPS database. Using molecular dynamic simulations, the three independent elastic constants, C11, C12, and C44, were found for Cu, Al, and Ni cubic single crystals. Voigt-Reuss-Hill approximation was then implemented to convert elastic constants of the single crystals into isotropic polycrystalline elastic moduli including bulk modulus, shear modulus, and Young's modulus as well as Poisson's ratio. Simulation results from massive molecular dynamic were compared with available experimental data in the literature to justify the robustness of each potential for each species. Eventually, accurate interatomic potentials have been recommended for finding each of the elastic properties of the pure species. Exactitude of the elastic properties was found to be sensitive to the choice of the force fields. Those potentials that were fitted for a specific compound may not necessarily work accurately for all the existing pure species. Tabulated results in this paper might be used as a benchmark to increase assurance of using the interatomic potential that was designated for a problem.

The property distance index PD predicts peptides that cross-react with IgE antibodies

PubMed Central

Ivanciuc, Ovidiu; Midoro-Horiuti, Terumi; Schein, Catherine H.; Xie, Liping; Hillman, Gilbert R.; Goldblum, Randall M.; Braun, Werner

2009-01-01

Similarities in the sequence and structure of allergens can explain clinically observed cross-reactivities. Distinguishing sequences that bind IgE in patient sera can be used to identify potentially allergenic protein sequences and aid in the design of hypo-allergenic proteins. The property distance index PD, incorporated in our Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/), may identify potentially cross-reactive segments of proteins, based on their similarity to known IgE epitopes. We sought to obtain experimental validation of the PD index as a quantitative predictor of IgE cross-reactivity, by designing peptide variants with predetermined PD scores relative to three linear IgE epitopes of Jun a 1, the dominant allergen from mountain cedar pollen. For each of the three epitopes, 60 peptides were designed with increasing PD values (decreasing physicochemical similarity) to the starting sequence. The peptides synthesized on a derivatized cellulose membrane were probed with sera from patients who were allergic to Jun a 1, and the experimental data were interpreted with a PD classification method. Peptides with low PD values relative to a given epitope were more likely to bind IgE from the sera than were those with PD values larger than 6. Control sequences, with PD values between 18 and 20 to all the three epitopes, did not bind patient IgE, thus validating our procedure for identifying negative control peptides. The PD index is a statistically validated method to detect discrete regions of proteins that have a high probability of cross-reacting with IgE from allergic patients. PMID:18950868

Potential relationship between Hashimoto's thyroiditis and BRAF(V600E) mutation status in papillary thyroid cancer.

PubMed

Zeng, Rui-Chao; Jin, Lang-Ping; Chen, En-Dong; Dong, Si-Yang; Cai, Ye-Feng; Huang, Guan-Li; Li, Quan; Jin, Chun; Zhang, Xiao-Hua; Wang, Ou-Chen

2016-04-01

The purpose of this study was to evaluate the potential relationship between Hashimoto's thyroiditis and BRAF(V600E) mutation status in patients with papillary thyroid carcinoma (PTC). A total of 619 patients with PTC who underwent total thyroidectomy with lymph node dissection were enrolled in this study. Univariable and multivariate analyses were used. Hashimoto's thyroiditis was present in 35.9% (222 of 619) of PTCs. Multivariate logistic regressions showed that BRAF(V600E) mutation, sex, extrathyroidal extension, and lymph node metastasis were independent factors for Hashimoto's thyroiditis. Female sex, more frequent extrathyroidal extension, and a higher incidence of lymph node metastasis were significantly associated with PTCs accompanied by BRAF(V600E) mutation without Hashimoto's thyroiditis compared with PTCs accompanied by BRAF(V600E) mutation with Hashimoto's thyroiditis. Hashimoto's thyroiditis was negatively associated with BRAF(V600E) mutation, extrathyroidal extension, and lymph node metastasis. In addition, Hashimoto's thyroiditis was related to less lymph node metastasis and extrathyroidal extension in PTCs with BRAF(V600E) mutation. Therefore, Hashimoto's thyroiditis is a potentially protective factor in PTC. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1019-E1025, 2016. © 2015 Wiley Periodicals, Inc.

An Exceptionally Narrow Band-Gap (∼4 eV) Silicate Predicted in the Cubic Perovskite Structure: BaSiO3.

PubMed

Hiramatsu, Hidenori; Yusa, Hitoshi; Igarashi, Ryo; Ohishi, Yasuo; Kamiya, Toshio; Hosono, Hideo

2017-09-05

The electronic structures of 35 A 2+ B 4+ O 3 ternary cubic perovskite oxides, including their hypothetical chemical compositions, were calculated by a hybrid functional method with the expectation that peculiar electronic structures and unique carrier transport properties suitable for semiconductor applications would be hidden in high-symmetry cubic perovskite oxides. We found unique electronic structures of Si-based oxides (A = Mg, Ca, Sr, and Ba, and B = Si). In particular, the unreported cubic BaSiO 3 has a very narrow band gap (4.1 eV) compared with conventional nontransition-metal silicates (e.g., ∼9 eV for SiO 2 and the calculated value of 7.3 eV for orthorhombic BaSiO 3 ) and a small electron effective mass (0.3m 0 , where m 0 is the free electron rest mass). The narrow band gap is ascribed to the nonbonding state of Si 3s and the weakened Madelung potential. The existence of the predicted cubic perovskite structure of BaSiO 3 was experimentally verified by applying a high pressure of 141 GPa. The present finding indicates that it could be possible to develop a new transparent oxide semiconductor of earth abundant silicates if the symmetry of its crystal structure is appropriately chosen. Cubic BaSiO 3 is a candidate for high-performance oxide semiconductors if this phase can be stabilized at room temperature and ambient pressure.

Regional variations in the diversity and predicted metabolic potential of benthic prokaryotes in coastal northern Zhejiang, East China Sea

PubMed Central

Wang, Kai; Ye, Xiansen; Zhang, Huajun; Chen, Heping; Zhang, Demin; Liu, Lian

2016-01-01

Knowledge about the drivers of benthic prokaryotic diversity and metabolic potential in interconnected coastal sediments at regional scales is limited. We collected surface sediments across six zones covering ~200 km in coastal northern Zhejiang, East China Sea and combined 16 S rRNA gene sequencing, community-level metabolic prediction, and sediment physicochemical measurements to investigate variations in prokaryotic diversity and metabolic gene composition with geographic distance and under local environmental conditions. Geographic distance was the most influential factor in prokaryotic β-diversity compared with major environmental drivers, including temperature, sediment texture, acid-volatile sulfide, and water depth, but a large unexplained variation in community composition suggested the potential effects of unmeasured abiotic/biotic factors and stochastic processes. Moreover, prokaryotic assemblages showed a biogeographic provincialism across the zones. The predicted metabolic gene composition similarly shifted as taxonomic composition did. Acid-volatile sulfide was strongly correlated with variation in metabolic gene composition. The enrichments in the relative abundance of sulfate-reducing bacteria and genes relevant with dissimilatory sulfate reduction were observed and predicted, respectively, in the Yushan area. These results provide insights into the relative importance of geographic distance and environmental condition in driving benthic prokaryotic diversity in coastal areas and predict specific biogeochemically-relevant genes for future studies. PMID:27917954

IUS solid rocket motor contamination prediction methods

NASA Technical Reports Server (NTRS)

Mullen, C. R.; Kearnes, J. H.

1980-01-01

A series of computer codes were developed to predict solid rocket motor produced contamination to spacecraft sensitive surfaces. Subscale and flight test data have confirmed some of the analytical results. Application of the analysis tools to a typical spacecraft has provided early identification of potential spacecraft contamination problems and provided insight into their solution; e.g., flight plan modifications, plume or outgassing shields and/or contamination covers.

Can somatosensory and visual evoked potentials predict neurological outcome during targeted temperature management in post cardiac arrest patients?

PubMed

Choi, Seung Pill; Park, Kyu Nam; Wee, Jung Hee; Park, Jeong Ho; Youn, Chun Song; Kim, Han Joon; Oh, Sang Hoon; Oh, Yoon Sang; Kim, Soo Hyun; Oh, Joo Suk

2017-10-01

In cardiac arrest patients treated with targeted temperature management (TTM), it is not certain if somatosensory evoked potentials (SEPs) and visual evoked potentials (VEPs) can predict neurological outcomes during TTM. The aim of this study was to investigate the prognostic value of SEPs and VEPs during TTM and after rewarming. This retrospective cohort study included comatose patients resuscitated from cardiac arrest and treated with TTM between March 2007 and July 2015. SEPs and VEPs were recorded during TTM and after rewarming in these patients. Neurological outcome was assessed at discharge by the Cerebral Performance Category (CPC) Scale. In total, 115 patients were included. A total of 175 SEPs and 150 VEPs were performed. Five SEPs during treated with TTM and nine SEPs after rewarming were excluded from outcome prediction by SEPs due to an indeterminable N20 response because of technical error. Using 80 SEPs and 85 VEPs during treated with TTM, absent SEPs yielded a sensitivity of 58% and a specificity of 100% for poor outcome (CPC 3-5), and absent VEPs predicted poor neurological outcome with a sensitivity of 44% and a specificity of 96%. The AUC of combination of SEPs and VEPs was superior to either test alone (0.788 for absent SEPs and 0.713 for absent VEPs compared with 0.838 for the combination). After rewarming, absent SEPs and absent VEPs predicted poor neurological outcome with a specificity of 100%. When SEPs and VEPs were combined, VEPs slightly increased the prognostic accuracy of SEPs alone. Although one patient with absent VEP during treated with TTM had a good neurological outcome, none of the patients with good neurological outcome had an absent VEP after rewarming. Absent SEPs could predict poor neurological outcome during TTM as well as after rewarming. Absent VEPs may predict poor neurological outcome in both periods and VEPs may provide additional prognostic value in outcome prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

[Potential distribution of Panax ginseng and its predicted responses to climate change.

PubMed

Zhao, Ze Fang; Wei, Hai Yan; Guo, Yan Long; Gu, Wei

2016-11-18

This study utilized Panax ginseng as the research object. Based on BioMod2 platform, with species presence data and 22 climatic variables, the potential geographic distribution of P. ginseng under the current conditions in northeast China was simulated with ten species distribution model. And then with the receiver-operating characteristic curve (ROC) as weights, we build an ensemble model, which integrated the results of 10 models, using the ensemble model, the future distributions of P. ginseng were also projected for the periods 2050s and 2070s under the climate change scenarios of RCP 8.5, RCP 6, RCP 4.5 and RCP 2.6 emission scenarios described in the Special Report on Emissions Scenarios (SRES) of IPCC (Intergovernmental Panel on Climate Change). The results showed that for the entire region of study area, under the present climatic conditions, 10.4% of the areas were identified as suitable habitats, which were mainly located in northeast Changbai Mountains area and the southeastern region of the Xiaoxing'an Mountains. The model simulations indicated that the suitable habitats would have a relatively significant change under the different climate change scenarios, and generally the range of suitable habitats would be a certain degree of decrease. Meanwhile, the goodness-of-fit, predicted ranges, and weights of explanatory variables was various for each model. And according to the goodness-of-fit, Maxent had the highest model performance, and GAM, RF and ANN were followed, while SRE had the lowest prediction accuracy. In this study we established an ensemble model, which could improve the accuracy of the existing species distribution models, and optimization of species distribution prediction results.

Experimental observation of ion beams in the Madison Helicon eXperiment

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

Wiebold, Matt; Sung, Yung-Ta; Scharer, John E.

2011-06-15

Argon ion beams up to E{sub b} = 165 eV at P{sub rf} = 500 W are observed in the Madison Helicon eXperiment (MadHeX) helicon source with a magnetic nozzle. A two-grid retarding potential analyzer (RPA) is used to measure the ion energy distribution, and emissive and rf-filtered Langmuir probes measure the plasma potential, electron density, and temperature. The supersonic ion beam (M = v{sub i}/c{sub s} up to 5) forms over tens of Debye lengths and extends spatially for a few ion-neutral charge-exchange mean free paths. The parametric variation of the ion beam energy is explored, including flow rate,more » rf power, and magnetic field dependence. The beam energy is equal to the difference in plasma potentials in the Pyrex chamber and the grounded expansion chamber. The plasma potential in the expansion chamber remains near the predicted eV{sub p} {approx} 5kT{sub e} for argon, but the upstream potential is much higher, likely due to wall charging, resulting in accelerated ion beam energies E{sub b} = e[V{sub beam} - V{sub plasma}] > 10kT{sub e}.« less

Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database

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

Uehara, Takeki, E-mail: takeki.uehara@shionogi.co.jp; Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, 7-6-8 Asagi, Ibaraki, Osaka 567-0085; Minowa, Yohsuke

2011-09-15

The present study was performed to develop a robust gene-based prediction model for early assessment of potential hepatocarcinogenicity of chemicals in rats by using our toxicogenomics database, TG-GATEs (Genomics-Assisted Toxicity Evaluation System developed by the Toxicogenomics Project in Japan). The positive training set consisted of high- or middle-dose groups that received 6 different non-genotoxic hepatocarcinogens during a 28-day period. The negative training set consisted of high- or middle-dose groups of 54 non-carcinogens. Support vector machine combined with wrapper-type gene selection algorithms was used for modeling. Consequently, our best classifier yielded prediction accuracies for hepatocarcinogenicity of 99% sensitivity and 97% specificitymore » in the training data set, and false positive prediction was almost completely eliminated. Pathway analysis of feature genes revealed that the mitogen-activated protein kinase p38- and phosphatidylinositol-3-kinase-centered interactome and the v-myc myelocytomatosis viral oncogene homolog-centered interactome were the 2 most significant networks. The usefulness and robustness of our predictor were further confirmed in an independent validation data set obtained from the public database. Interestingly, similar positive predictions were obtained in several genotoxic hepatocarcinogens as well as non-genotoxic hepatocarcinogens. These results indicate that the expression profiles of our newly selected candidate biomarker genes might be common characteristics in the early stage of carcinogenesis for both genotoxic and non-genotoxic carcinogens in the rat liver. Our toxicogenomic model might be useful for the prospective screening of hepatocarcinogenicity of compounds and prioritization of compounds for carcinogenicity testing. - Highlights: >We developed a toxicogenomic model to predict hepatocarcinogenicity of chemicals. >The optimized model consisting of 9 probes had 99% sensitivity and 97% specificity

Potential predictability and actual skill of Boreal Summer Tropical SST and Indian summer monsoon rainfall in CFSv2-T382: Role of initial SST and teleconnections

NASA Astrophysics Data System (ADS)

Pillai, Prasanth A.; Rao, Suryachandra A.; Das, Renu S.; Salunke, Kiran; Dhakate, Ashish

2017-10-01

The present study assess the potential predictability of boreal summer (June through September, JJAS) tropical sea surface temperature (SST) and Indian summer monsoon rainfall (ISMR) using high resolution climate forecast system (CFSv2-T382) hindcasts. Potential predictability is computed using relative entropy (RE), which is the combined effect of signal strength and model spread, while the correlation between ensemble mean and observations represents the actual skill. Both actual and potential skills increase as lead time decreases for Niño3 index and equatorial East Indian Ocean (EEIO) SST anomaly and both the skills are close to each other for May IC hindcasts at zero lead. At the same time the actual skill of ISMR and El Niño Modoki index (EMI) are close to potential skill for Feb IC hindcasts (3 month lead). It is interesting to note that, both actual and potential skills are nearly equal, when RE has maximum contribution to individual year's prediction skill and its relationship with absolute error is insignificant or out of phase. The major contribution to potential predictability is from ensemble mean and the role of ensemble spread is limited for Pacific SST and ISMR hindcasts. RE values are able to capture the predictability contribution from both initial SST and simultaneous boundary forcing better than ensemble mean, resulting in higher potential skill compared to actual skill for all ICs. For Feb IC hindcasts at 3 month lead time, initial month SST (Feb SST) has important predictive component for El Niño Modoki and ISMR leading to higher value of actual skill which is close to potential skill. This study points out that even though the simultaneous relationship between ensemble mean ISMR and global SST is similar for all ICs, the predictive component from initial SST anomalies are captured well by Feb IC (3 month lead) hindcasts only. This resulted in better skill of ISMR for Feb IC (3 month lead) hindcasts compared to May IC (0 month lead

Potential of SENTINEL-2 images for predicting common topsoil properties over Temperate and Mediterranean agroecosystems

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Gomez, Cécile; Fouad, Youssef; Gilliot, Jean-Marc; Lagacherie, Philippe

2017-04-01

This study aimed at exploring the potential of SENTINEL-2 (S2A) multispectral satellite images for predicting several topsoil properties in two contrasted environments: a temperate region marked by intensive annual crop cultivation and soils derived from either loess or colluvium and/or marine limestone or chalk for one part (Versailles Plain, 221 km2), and a Mediterranean region marked by vineyard cultivation and soils derived from either lacustrine limestone, calcareous sandstones, colluvium, or alluvial deposits (La Peyne catchment, 48 km2) for the other part. Two S2A images (acquired in mid-March 2016 over each site) were atmospherically corrected. Then NDVI was computed and thresholded (0.35) in order to extract bare soils. Prediction models of soil properties based on partial least squares regressions (PLSR) were built from S2A spectra of 72 and 143 sampling locations in the Versailles Plain and La Peyne catchment, respectively. Ten soil properties were investigated in both regions: pH, cation exchange capacity (CEC), five texture fractions (clay, coarse silt, fine silt, coarse sand and fine sand), iron, calcium carbonate and soil organic carbon (SOC) in the tilled horizon. Predictive abilities were studied according to R_cv2 and ratio of performance to deviation (RPD). Intermediate to near intermediate performances of prediction (R_cv2 and RPD between 0.28-0.70 and 1.19-1.85 respectively) were obtained for 6 topsoil properties: clay, iron, SOC, CEC, pH, coarse silt. In the Versailles Plain, 5 out of these properties could be predicted (by decreasing performance, CEC, SOC, pH, clay, coarse silt), while there were 4 predictable properties for La Peyne catchment (Iron, clay, CEC, coarse silt). The amount in coarse fragment content appeared to impact prediction error for iron content over La Peyne, while it influenced prediction error for SOC content over the Versailles Plain along with calcium carbonate content. A spatial structure of the estimated soil

In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors.

PubMed

Nongonierma, Alice B; Mooney, Catherine; Shields, Denis C; FitzGerald, Richard J

2014-07-01

Molecular docking of a library of all 8000 possible tripeptides to the active site of DPP-IV was used to determine their binding potential. A number of tripeptides were selected for experimental testing, however, there was no direct correlation between the Vina score and their in vitro DPP-IV inhibitory properties. While Trp-Trp-Trp, the peptide with the best docking score, was a moderate DPP-IV inhibitor (IC50 216μM), Lineweaver and Burk analysis revealed its action to be non-competitive. This suggested that it may not bind to the active site of DPP-IV as assumed in the docking prediction. Furthermore, there was no significant link between DPP-IV inhibition and the physicochemical properties of the peptides (molecular mass, hydrophobicity, hydrophobic moment (μH), isoelectric point (pI) and charge). LIGPLOTs indicated that competitive inhibitory peptides were predicted to have both hydrophobic and hydrogen bond interactions with the active site of DPP-IV. DPP-IV inhibitory peptides generally had a hydrophobic or aromatic amino acid at the N-terminus, preferentially a Trp for non-competitive inhibitors and a broader range of residues for competitive inhibitors (Ile, Leu, Val, Phe, Trp or Tyr). Two of the potent DPP-IV inhibitors, Ile-Pro-Ile and Trp-Pro (IC50 values of 3.5 and 44.2μM, respectively), were predicted to be gastrointestinally/intestinally stable. This work highlights the needs to test the assumptions (i.e. competitive binding) of any integrated strategy of computational and experimental screening, in optimizing screening. Future strategies targeting allosteric mechanisms may need to rely more on structure-activity relationship modeling, rather than on docking, in computationally selecting peptides for screening. Copyright © 2014 Elsevier Inc. All rights reserved.

Hemisphere Differences in Speech-Sound Event-Related Potentials in Intensive Care Neonates: Associations and Predictive Value for Development in Infancy

PubMed Central

Maitre, Nathalie L.; Slaughter, James C.; Aschner, Judy L.; Key, Alexandra P.

2014-01-01

Neurodevelopmental delays in intensive care neonates are common but difficult to predict. In children, hemisphere differences in cortical processing of speech are predictive of cognitive performance. We hypothesized that hemisphere differences in auditory event-related potentials in intensive care neonates are predictive of neurodevelopment in infancy, even in those born preterm. Event-related potentials to speech sounds were prospectively recorded in 57 infants (gestational age 24–40 weeks) prior to discharge. The Developmental Assessment of Young Children was performed at 6 and 12 months. Hemisphere differences in mean amplitudes increased with postnatal age (P < .01) but not with gestational age. Greater hemisphere differences were associated with improved communication and cognitive scores at 6 and 12 months, but decreased in significance at 12 months after adjusting for socioeconomic and clinical factors. Auditory cortical responses can be used in intensive care neonates to help identify infants at higher risk for delays in infancy. PMID:23864588

Oral administration of drugs with hypersensitivity potential induces germinal center hyperplasia in secondary lymphoid organ/tissue in Brown Norway rats, and this histological lesion is a promising candidate as a predictive biomarker for drug hypersensitivity occurrence in humans

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

Tamura, Akitoshi, E-mail: akitoshi-tamura@ds-pharma.co.jp; Miyawaki, Izuru; Yamada, Toru

It is important to evaluate the potential of drug hypersensitivity as well as other adverse effects during the preclinical stage of the drug development process, but validated methods are not available yet. In the present study we examined whether it would be possible to develop a new predictive model of drug hypersensitivity using Brown Norway (BN) rats. As representative drugs with hypersensitivity potential in humans, phenytoin (PHT), carbamazepine (CBZ), amoxicillin (AMX), and sulfamethoxazole (SMX) were orally administered to BN rats for 28 days to investigate their effects on these animals by examinations including observation of clinical signs, hematology, determination ofmore » serum IgE levels, histology, and flow cytometric analysis. Skin rashes were not observed in any animals treated with these drugs. Increases in the number of circulating inflammatory cells and serum IgE level did not necessarily occur in the animals treated with these drugs. However, histological examination revealed that germinal center hyperplasia was commonly induced in secondary lymphoid organs/tissues in the animals treated with these drugs. In cytometric analysis, changes in proportions of lymphocyte subsets were noted in the spleen of the animals treated with PHT or CBZ during the early period of administration. The results indicated that the potential of drug hypersensitivity was identified in BN rat by performing histological examination of secondary lymphoid organs/tissues. Data obtained herein suggested that drugs with hypersensitivity potential in humans gained immune reactivity in BN rat, and the germinal center hyperplasia induced by administration of these drugs may serve as a predictive biomarker for drug hypersensitivity occurrence. - Highlights: • We tested Brown Norway rats as a candidate model for predicting drug hypersensitivity. • The allergic drugs did not induce skin rash, whereas D-penicillamine did so in the rats. • Some of allergic drugs increased

The size prediction of potential inclusions embedded in the sub-surface of fused silica by damage morphology

NASA Astrophysics Data System (ADS)

Gao, Xiang; Qiu, Rong; Wang, Kunpeng; Zhang, Jiangmei; Zhou, Guorui; Yao, Ke; Jiang, Yong; Zhou, Qiang

2017-04-01

A model for predicting the size ranges of different potential inclusions initiating damage on the surface of fused silica has been presented. This accounts for the heating of nanometric inclusions whose absorptivity is described based on Mie Theory. The depth profile of impurities has been measured by ICP-OES. By the measured temporal pulse profile on the surface of fused silica, the temperature and thermal stress has been calculated. Furthermore, considering the limit conditions of temperature and thermal stress strength for different damage morphologies, the size range of potential inclusions for fused silica is discussed.

High invasion potential of Hydrilla verticillata in the Americas predicted using ecological niche modeling combined with genetic data.

PubMed

Zhu, Jinning; Xu, Xuan; Tao, Qing; Yi, Panpan; Yu, Dan; Xu, Xinwei

2017-07-01

Ecological niche modeling is an effective tool to characterize the spatial distribution of suitable areas for species, and it is especially useful for predicting the potential distribution of invasive species. The widespread submerged plant Hydrilla verticillata (hydrilla) has an obvious phylogeographical pattern: Four genetic lineages occupy distinct regions in native range, and only one lineage invades the Americas. Here, we aimed to evaluate climatic niche conservatism of hydrilla in North America at the intraspecific level and explore its invasion potential in the Americas by comparing climatic niches in a phylogenetic context. Niche shift was found in the invasion process of hydrilla in North America, which is probably mainly attributed to high levels of somatic mutation. Dramatic changes in range expansion in the Americas were predicted in the situation of all four genetic lineages invading the Americas or future climatic changes, especially in South America; this suggests that there is a high invasion potential of hydrilla in the Americas. Our findings provide useful information for the management of hydrilla in the Americas and give an example of exploring intraspecific climatic niche to better understand species invasion.

Prediction of the P-leaching potential of arable soils in areas with high livestock densities*

PubMed Central

Werner, Wilfried; Trimborn, Manfred; Pihl, Uwe

2006-01-01

Due to long-term positive P-balances many surface soils in areas with high livestock density in Germany are oversupplied with available P, creating a potential for vertical P losses by leaching. In extensive studies to characterize the endangering of ground water to P pollution by chemical soil parameters it is shown that the available P content and the P concentration of the soil solution in the deeper soil layers, as indicators of the P-leaching potential, cannot be satisfactorily predicted from the available P content of the topsoils. The P equilibrium concentration in the soil solution directly above ground water table or the pipe drainage system highly depends on the relative saturation of the P-sorption capacity in this layer. A saturation index of <20% normally corresponds with P equilibrium concentrations of <0.2 mg P/L. Phytoremediation may reduce the P leaching potential of P-enriched soils only over a very long period. PMID:16773724

Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models.

PubMed

Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme

2015-01-01

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sesame allergy: role of specific IgE and skin-prick testing in predicting food challenge results.

PubMed

Permaul, Perdita; Stutius, Lisa M; Sheehan, William J; Rangsithienchai, Pitud; Walter, Jolan E; Twarog, Frank J; Young, Michael C; Scott, Jordan E; Schneider, Lynda C; Phipatanakul, Wanda

2009-01-01

There are conflicting data regarding the diagnostic value of sesame-specific IgE and sesame skin test. Currently, there are no established thresholds that predict clinical reactivity. We examined the correlation of sesame ImmunoCAP and skin-prick test (SPT) results with oral challenge outcomes in children suspected of having a sesame food allergy. We conducted a retrospective chart review of children, aged 2-12 years, receiving a sesame ImmunoCAP level, SPT, and food challenge from January 2004 to August 2008 at Children's Hospital Boston and affiliated allergy clinics. Food challenges were conducted in cases of questionable clinical history or a negative ImmunoCAP and/or negative SPT despite a convincing history. Thirty-three oral sesame challenges were conducted. Of the 33 challenges performed, 21% (n = 7) failed and 79% (n = 26) passed. A sesame-specific IgE level of > or = 7 kU(A)/L showed specificity of >90%. An SPT wheal size of > or = 6 mm showed specificity of >90%. Receiver operator characteristic (ROC) curve analysis for sesame-specific IgE revealed an area under the curve (AUC) of 0.56. ROC curve analysis for SPT wheal size revealed an AUC of 0.67. To our knowledge, this study represents the largest number of sesame challenges performed to evaluate the diagnostic value of both sesame-specific IgE and SPT. Based on our sample, both tests are not good predictors of true sesame allergy as determined by an oral challenge. We were unable to establish a threshold with a 95% positive predictive value for both sesame-specific IgE and SPT.

gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.

PubMed

Olejnik, Michael; Steuwer, Michel; Gorlatch, Sergei; Heider, Dominik

2014-11-15

Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics processing units (GPUs) in combination with a computational prediction model for HIV tropism. Our new model named gCUP, parallelized and optimized for GPU, is highly accurate and can classify >175 000 sequences per second on an NVIDIA GeForce GTX 460. The computational efficiency of our new model is the next step to enable NGS technologies to reach clinical significance in HIV diagnostics. Moreover, our approach is not limited to HIV tropism prediction, but can also be easily adapted to other settings, e.g. drug resistance prediction. The source code can be downloaded at http://www.heiderlab.de d.heider@wz-straubing.de. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The zoonotic potential of Giardia intestinalis assemblage E in rural settings.

PubMed

Abdel-Moein, Khaled A; Saeed, Hossam

2016-08-01

Giardiasis is a globally re-emerging protozoan disease with veterinary and public health implications. The current study was carried out to investigate the zoonotic potential of livestock-specific assemblage E in rural settings. For this purpose, a total of 40 microscopically positive Giardia stool samples from children with gastrointestinal complaints with or without diarrhea were enrolled in the study as well as fecal samples from 46 diarrheic cattle (18 dairy cows and 28 calves). Animal samples were examined by sedimentation method to identify Giardia spp., and then, all Giardia positive samples from human and animals were processed for molecular detection of livestock-specific assemblage E through amplification of assemblage-specific triosephosphate isomerase (tpi) gene using nested polymerase chain reaction (PCR). The results of the study revealed high unexpected occurrence of assemblage E among human samples (62.5 %), whereas the distribution among patients with diarrhea and those without was 42.1 and 81 %, respectively. On the other hand, the prevalence of Giardia spp. among diarrheic dairy cattle was (8.7 %), while only calves yielded positive results (14.3 %) and all bovine Giardia spp. were genetically classified as Giardia intestinalis assemblage E. Moreover, DNA sequencing of randomly selected one positive human sample and another bovine one revealed 100 and 99 % identity with assemblage E tpi gene sequences available at GenBank after BLAST analysis. In conclusion, the current study highlights the wide dissemination of livestock-specific assemblage E among humans in rural areas, and thus, zoonotic transmission cycle should not be discounted during the control of giardiasis in such settings.

The 13Carbon footprint of B[e] supergiants

NASA Astrophysics Data System (ADS)

Liermann, A.; Kraus, M.; Schnurr, O.; Fernandes, M. Borges

2010-10-01

We report on the first detection of 13C enhancement in two B[e] supergiants (B[e]SGs) in the Large Magellanic Cloud. Stellar evolution models predict the surface abundance in 13C to strongly increase during main-sequence and post-main-sequence evolution of massive stars. However, direct identification of chemically processed material on the surface of B[e]SGs is hampered by their dense, disc-forming winds, hiding the stars. Recent theoretical computations predict the detectability of enhanced 13C via the molecular emission in 13CO arising in the circumstellar discs of B[e]SGs. To test this potential method and to unambiguously identify a post-main-sequence B[e] SG by its 13CO emission, we have obtained high-quality K-band spectra of two known B[e] SGs in the Large Magellanic Cloud, using the Very Large Telescope's Spectrograph for INtegral Field Observation in the Near-Infrared (VLT/SINFONI). Both stars clearly show the 13CO band emission, whose strength implies a strong enhancement of 13C, in agreement with theoretical predictions. This first ever direct confirmation of the evolved nature of B[e]SGs thus paves the way to the first identification of a Galactic B[e]SG. Based on observations collected with the ESO VLT Paranal Observatory under programme 384.D-1078(A). E-mail: liermann@mpifr-bonn.mpg.de (AL); kraus@sunstel.asu.cas.cz (MK); oschnurr@aip.de (OS); borges@on.br (MBF)

A GIS model predicting potential distributions of a lineage: a test case on hermit spiders (Nephilidae: Nephilengys).

PubMed

Năpăruş, Magdalena; Kuntner, Matjaž

2012-01-01

Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics. Our model is a customizable GIS tool intended to predict current and future potential distributions of globally distributed terrestrial lineages. Its predictive

A GIS Model Predicting Potential Distributions of a Lineage: A Test Case on Hermit Spiders (Nephilidae: Nephilengys)

PubMed Central

Năpăruş, Magdalena; Kuntner, Matjaž

2012-01-01

Background Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters. Methodology/Principal Findings We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics. Conclusions Our model is a customizable GIS tool intended to predict current and future potential distributions of globally

A scientometric prediction of the discovery of the first potentially habitable planet with a mass similar to Earth.

PubMed

Arbesman, Samuel; Laughlin, Gregory

2010-10-04

The search for a habitable extrasolar planet has long interested scientists, but only recently have the tools become available to search for such planets. In the past decades, the number of known extrasolar planets has ballooned into the hundreds, and with it, the expectation that the discovery of the first Earth-like extrasolar planet is not far off. Here, we develop a novel metric of habitability for discovered planets and use this to arrive at a prediction for when the first habitable planet will be discovered. Using a bootstrap analysis of currently discovered exoplanets, we predict the discovery of the first Earth-like planet to be announced in the first half of 2011, with the likeliest date being early May 2011. Our predictions, using only the properties of previously discovered exoplanets, accord well with external estimates for the discovery of the first potentially habitable extrasolar planet and highlight the the usefulness of predictive scientometric techniques to understand the pace of scientific discovery in many fields.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

PubMed

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. http://datamining-iip.fudan.edu.cn/service/DrugE-Rank zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Biometric assessment of prostate cancer's metastatic potential.

PubMed

Cooper, C R; Emmett, N; Harris-Hooker, S; Patterson, R; Cooke, D B

1994-01-01

Currently, no protocol exists that can assess the metastatic potential of prostate adenocarcinoma. The reason for this is partly due to the lack of information on cellular changes that result in a tumor cell's becoming metastatic. In this investigation, attempts were made to devise a method that correlated with the metastatic potential of AT-1, Mat-Lu, and Mat-LyLu cell lines of the Dunning R-3327 rat prostatic adenocarcinoma system. To accomplish this, we applied BioQuant biometric parameters, i.e., area, shape factor, and cell motility. AT-1 had a lower shape factor and a greater area as compared with the more highly metastatic Mat-Lu subline. No significant difference in area or shape factor was detected between the AT-1 cell line and the highly metastatic Mat-LyLu line. However, the lowly metastatic AT-1 line had less motility as compared with the Mat-Lu and Mat-LyLu lines. This study revealed that metastatic potential could be partially predicted via area and shape factor and accurately predicted via cell motility.

Investigating the potential use of environmental DNA (eDNA) for genetic monitoring of marine mammals.

PubMed

Foote, Andrew D; Thomsen, Philip Francis; Sveegaard, Signe; Wahlberg, Magnus; Kielgast, Jos; Kyhn, Line A; Salling, Andreas B; Galatius, Anders; Orlando, Ludovic; Gilbert, M Thomas P

2012-01-01

The exploitation of non-invasive samples has been widely used in genetic monitoring of terrestrial species. In aquatic ecosystems, non-invasive samples such as feces, shed hair or skin, are less accessible. However, the use of environmental DNA (eDNA) has recently been shown to be an effective tool for genetic monitoring of species presence in freshwater ecosystems. Detecting species in the marine environment using eDNA potentially offers a greater challenge due to the greater dilution, amount of mixing and salinity compared with most freshwater ecosystems. To determine the potential use of eDNA for genetic monitoring we used specific primers that amplify short mitochondrial DNA sequences to detect the presence of a marine mammal, the harbor porpoise, Phocoena phocoena, in a controlled environment and in natural marine locations. The reliability of the genetic detections was investigated by comparing with detections of harbor porpoise echolocation clicks by static acoustic monitoring devices. While we were able to consistently genetically detect the target species under controlled conditions, the results from natural locations were less consistent and detection by eDNA was less successful than acoustic detections. However, at one site we detected long-finned pilot whale, Globicephala melas, a species rarely sighted in the Baltic. Therefore, with optimization aimed towards processing larger volumes of seawater this method has the potential to compliment current visual and acoustic methods of species detection of marine mammals.

A predictive model of iron oxide nanoparticles flocculation tuning Z-potential in aqueous environment for biological application

NASA Astrophysics Data System (ADS)

Baldassarre, Francesca; Cacciola, Matteo; Ciccarella, Giuseppe

2015-09-01

Iron oxide nanoparticles are the most used magnetic nanoparticles in biomedical and biotechnological field because of their nontoxicity respect to the other metals. The investigation of iron oxide nanoparticles behaviour in aqueous environment is important for the biological applications in terms of polydispersity, mobility, cellular uptake and response to the external magnetic field. Iron oxide nanoparticles tend to agglomerate in aqueous solutions; thus, the stabilisation and aggregation could be modified tuning the colloids physical proprieties. Surfactants or polymers are often used to avoid agglomeration and increase nanoparticles stability. We have modelled and synthesised iron oxide nanoparticles through a co-precipitation method, in order to study the influence of surfactants and coatings on the aggregation state. Thus, we compared experimental results to simulation model data. The change of Z-potential and the clusters size were determined by Dynamic Light Scattering. We developed a suitable numerical model to predict the flocculation. The effects of Volume Mean Diameter and fractal dimension were explored in the model. We obtained the trend of these parameters tuning the Z-potential. These curves matched with the experimental results and confirmed the goodness of the model. Subsequently, we exploited the model to study the influence of nanoparticles aggregation and stability by Z-potential and external magnetic field. The highest Z-potential is reached up with a small external magnetic influence, a small aggregation and then a high suspension stability. Thus, we obtained a predictive model of Iron oxide nanoparticles flocculation that will be exploited for the nanoparticles engineering and experimental setup of bioassays.

The VCS parameters: Potential hematological indicators for predicting antituberculosis drug-induced neutropenia.

PubMed

Shen, Tian; Gu, Delin; Zhu, Yihua; Shi, Junwei; Xu, Dongsheng; Cao, Xingjian

2016-08-01

The morphological changes in activated neutrophils associated with antituberculosis drugs can be measured by volume, conductivity, and scatter (VCS) technology on the Coulter LH750 hematology analyzer. We conducted the current study to further validate the clinical usefulness of the neutrophil VCS parameters in predicting drug-induced neutropenia. Peripheral blood samples were collected from 52 patients with drug-induced neutropenia, 309 patients without any abnormal CBC, and 237 healthy controls. The mean neutrophil volume (MNV) with its distribution width (NDW) and the mean neutrophil scatter (MNS) were studied. We observed a significant increase in the MNV and NDW as well as a significant decrease in the MNS in neutropenia patients approximately one week prior to development of neutropenia compared to healthy controls as well as to case controls. In addition, the delta MNV and delta MNS were respectively correlated well with delta absolute neutrophil counts when neutropenia occurred. The ROC curve analyses showed that the MNV、NDW and MNS had larger areas under curves compared to conventional parameters. With a cutoff of 150.15 for the MNV, a sensitivity of 84.4% and specificity of 75.7% were achieved prior to neutropenia. The neutrophil VCS parameters may be clinically useful as potential hematological indicators for predicting antituberculosis drug-induced neutropenia. Copyright © 2016 Elsevier B.V. All rights reserved.

Probing Higgs self-coupling of a classically scale invariant model in e+e- → Zhh: Evaluation at physical point

NASA Astrophysics Data System (ADS)

Fujitani, Y.; Sumino, Y.

2018-04-01

A classically scale invariant extension of the standard model predicts large anomalous Higgs self-interactions. We compute missing contributions in previous studies for probing the Higgs triple coupling of a minimal model using the process e+e- → Zhh. Employing a proper order counting, we compute the total and differential cross sections at the leading order, which incorporate the one-loop corrections between zero external momenta and their physical values. Discovery/exclusion potential of a future e+e- collider for this model is estimated. We also find a unique feature in the momentum dependence of the Higgs triple vertex for this class of models.

Development and formative evaluation of the e-Health Implementation Toolkit (e-HIT).

PubMed

Murray, Elizabeth; May, Carl; Mair, Frances

2010-10-18

The use of Information and Communication Technology (ICT) or e-Health is seen as essential for a modern, cost-effective health service. However, there are well documented problems with implementation of e-Health initiatives, despite the existence of a great deal of research into how best to implement e-Health (an example of the gap between research and practice). This paper reports on the development and formative evaluation of an e-Health Implementation Toolkit (e-HIT) which aims to summarise and synthesise new and existing research on implementation of e-Health initiatives, and present it to senior managers in a user-friendly format. The content of the e-HIT was derived by combining data from a systematic review of reviews of barriers and facilitators to implementation of e-Health initiatives with qualitative data derived from interviews of "implementers", that is people who had been charged with implementing an e-Health initiative. These data were summarised, synthesised and combined with the constructs from the Normalisation Process Model. The software for the toolkit was developed by a commercial company (RocketScience). Formative evaluation was undertaken by obtaining user feedback. There are three components to the toolkit--a section on background and instructions for use aimed at novice users; the toolkit itself; and the report generated by completing the toolkit. It is available to download from http://www.ucl.ac.uk/pcph/research/ehealth/documents/e-HIT.xls. The e-HIT shows potential as a tool for enhancing future e-Health implementations. Further work is needed to make it fully web-enabled, and to determine its predictive potential for future implementations.

Development and formative evaluation of the e-Health Implementation Toolkit (e-HIT)

PubMed Central

2010-01-01

Background The use of Information and Communication Technology (ICT) or e-Health is seen as essential for a modern, cost-effective health service. However, there are well documented problems with implementation of e-Health initiatives, despite the existence of a great deal of research into how best to implement e-Health (an example of the gap between research and practice). This paper reports on the development and formative evaluation of an e-Health Implementation Toolkit (e-HIT) which aims to summarise and synthesise new and existing research on implementation of e-Health initiatives, and present it to senior managers in a user-friendly format. Results The content of the e-HIT was derived by combining data from a systematic review of reviews of barriers and facilitators to implementation of e-Health initiatives with qualitative data derived from interviews of "implementers", that is people who had been charged with implementing an e-Health initiative. These data were summarised, synthesised and combined with the constructs from the Normalisation Process Model. The software for the toolkit was developed by a commercial company (RocketScience). Formative evaluation was undertaken by obtaining user feedback. There are three components to the toolkit - a section on background and instructions for use aimed at novice users; the toolkit itself; and the report generated by completing the toolkit. It is available to download from http://www.ucl.ac.uk/pcph/research/ehealth/documents/e-HIT.xls Conclusions The e-HIT shows potential as a tool for enhancing future e-Health implementations. Further work is needed to make it fully web-enabled, and to determine its predictive potential for future implementations. PMID:20955594

High accuracy operon prediction method based on STRING database scores.

PubMed

Taboada, Blanca; Verde, Cristina; Merino, Enrique

2010-07-01

We present a simple and highly accurate computational method for operon prediction, based on intergenic distances and functional relationships between the protein products of contiguous genes, as defined by STRING database (Jensen,L.J., Kuhn,M., Stark,M., Chaffron,S., Creevey,C., Muller,J., Doerks,T., Julien,P., Roth,A., Simonovic,M. et al. (2009) STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416). These two parameters were used to train a neural network on a subset of experimentally characterized Escherichia coli and Bacillus subtilis operons. Our predictive model was successfully tested on the set of experimentally defined operons in E. coli and B. subtilis, with accuracies of 94.6 and 93.3%, respectively. As far as we know, these are the highest accuracies ever obtained for predicting bacterial operons. Furthermore, in order to evaluate the predictable accuracy of our model when using an organism's data set for the training procedure, and a different organism's data set for testing, we repeated the E. coli operon prediction analysis using a neural network trained with B. subtilis data, and a B. subtilis analysis using a neural network trained with E. coli data. Even for these cases, the accuracies reached with our method were outstandingly high, 91.5 and 93%, respectively. These results show the potential use of our method for accurately predicting the operons of any other organism. Our operon predictions for fully-sequenced genomes are available at http://operons.ibt.unam.mx/OperonPredictor/.

Wave Energy Potential in the Eastern Mediterranean Levantine Basin. An Integrated 10-year Study

DTIC Science & Technology

2014-01-01

SUBTITLE Wave energy potential in the Eastern Mediterranean Levantine Basin. An integrated 10-year study 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c... Cardone CV, Ewing JA, et al. The WAM model e a third generation ocean wave prediction model. J Phys Oceanogr 1988;18(12):1775e810. [70] Varinou M

A Signal to Noise Paradox in Climate Predictions

NASA Astrophysics Data System (ADS)

Eade, R.; Scaife, A. A.; Smith, D.; Dunstone, N. J.; MacLachlan, C.; Hermanson, L.; Ruth, C.

2017-12-01

Recent advances in climate modelling have resulted in the achievement of skilful long-range prediction, particular that associated with the winter circulation over the north Atlantic (e.g. Scaife et al 2014, Stockdale et al 2015, Dunstone et al 2016) including impacts over Europe and North America, and further afield. However, while highly significant and potentially useful skill exists, the signal-to-noise ratio of the ensemble mean to total variability in these ensemble predictions is anomalously small (Scaife et al 2014) and the correlation between the ensemble mean and historical observations exceeds the proportion of predictable variance in the ensemble (Eade et al 2014). This means the real world is more predictable than our climate models. Here we discuss a series of hypothesis tests that have been carried out to assess issues with model mechanisms compared to the observed world, and present the latest findings in our attempt to determine the cause of the anomalously weak predicted signals in our seasonal-to-decadal hindcasts.

Localization and prediction of malignant potential in recurrent pheochromocytoma/paraganglioma (PCC/PGL) using 18F-FDG PET/CT.

PubMed

Fikri, Ahmad Saad Fathinul; Kroiss, A; Ahmad, A Z F; Zanariah, H; Lau, W F E; Uprimny, C; Donnemiller, E; Kendler, D; Nordin, A J; Virgolini, I J

2014-06-01

To our knowledge, data are lacking on the role of 18F-FDG PET/CT in the localization and prediction of neuroendocrine tumors, in particular the pheochromocytoma/paraganglioma (PCC/PGL) group. To evaluate the role of 18F-FDG PET/CT in localizing and predicting the malignant potential of PCC/PGL. Twenty-three consecutive patients with a history of PCC/PGL, presenting with symptoms related to catecholamine excess, underwent 18F-FDG PET/CT. Final confirmation of the diagnosis was made using the composite references. PET/CT findings were analyzed on a per-lesion basis and a per-patient basis. Tumor SUVmax was analyzed to predict the dichotomization of patient endpoints for the local disease and metastatic groups. We investigated 23 patients (10 men, 13 women) with a mean age of 46.43 ± 3.70 years. Serum catecholamine levels were elevated in 82.60% of these patients. There were 136 sites (mean SUVmax: 16.39 ± 3.47) of validated disease recurrence. The overall sensitivities for diagnostic CT, FDG PET, and FDG PET/CT were 86.02%, 87.50%, and 98.59%, respectively. Based on the composite references, 39.10% of patients had local disease. There were significant differences in the SUVmax distribution between the local disease and metastatic groups; a significant correlation was noted when a SUVmax cut-off was set at 9.2 (P<0.05). In recurrent PCC/PGL, diagnostic 18F-FDG PET/CT is a superior tool in the localization of recurrent tumors. Tumor SUVmax is a potentially useful predictor of malignant tumor potential. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Blood Cholinesterase as a Function of Physostigmine.

DTIC Science & Technology

1981-07-01

organophosphates to serum cholines - terase and brain homogenate are similar, indicating that the en- zymes are the same in blood and brain, although...organophosphate insult to the organism. Little research is available on the time course of cholin - esterase inactivation and/or reactivation after...administration. Nine animals received a .05, .07, .09, .11, or .13 mg/kg dose of physostigmine salicylate or a placebo injection on two occasions, and four

An E/e' ratio on echocardiography predicts the existence of left atrial low-voltage areas and poor outcomes after catheter ablation for atrial fibrillation.

PubMed

Masuda, Masaharu; Fujita, Masashi; Iida, Osamu; Okamoto, Shin; Ishihara, Takayuki; Nanto, Kiyonori; Kanda, Takashi; Sunaga, Akihiro; Tsujimura, Takuya; Matsuda, Yasuhiro; Ohashi, Takuya; Uematsu, Masaaki

2018-05-01

An elevated left atrial pressure has been reported to play an important role in the development of atrial remodelling in atrial fibrillation (AF) patients. The study aimed at elucidating the association between the diastolic early transmitral flow velocity/mitral annular velocity (E/e', a non-invasive surrogate of left atrial pressure) and left atrial low-voltage-area existence, and the prognostic impact of the E/e' on procedural outcomes in patients undergoing AF ablation. Total of 215 consecutive patients were divided into 3 groups based on the estimated left atrial pressure: normal (E/e' < 8.0, n = 58), undetermined (E/e' = 8.0-14.0, n = 114), and elevated (E/e' > 14.0, n = 43). Left atrial endocardial voltage mapping was performed following pulmonary vein isolation. Patients with a high E/e' more frequently had low-voltage areas (E/e' < 8.0, 31%, E/e' = 8.0-14.0, 35%; E/e' > 14.0, 67%; P = 0.0001). After adjusting for other correlates, a high E/e' was an independent predictor of low-voltage-area existence (HR = 1.11, 95% CI = 1.02-1.21, P = 0.017). During a mean follow-up period of 12 ± 6 months, recurrent atrial tachyarrhythmias occurred in 22 (10%) patients after multiple (1.4 ± 0.5) procedures. Patients with an E/e' > 14 had more frequent recurrent atrial tachyarrhythmias after multiple ablation procedures than those with an E/e' ≤ 14 (23% vs. 7%, P = 0.001). A high E/e' obtained by pre-ablation echocardiography was associated with a left atrial arrhythmogenic substrate in patients undergoing AF ablation. Furthermore, a high E/e' predicted poor procedural outcomes after pulmonary vein isolation.

[Predictions of potential geographical distribution of Alhagi sparsifolia under climate change].

PubMed

Yang, Xia; Zheng, Jiang-Hua; Mu, Chen; Lin, Jun

2017-02-01

Specific information on geographic distribution of a species is important for its conservation. This study was conducted to determine the potential geographic distribution of Alhagi sparsifolia, which is a plant used in traditional Uighur medicine, and predict how climate change would affect its geographic range. The potential geographic distribution of A. sparsifolia under the current conditions in China was simulated with MaxEnt software based on species presence data at 42 locations and 19 climatic variables. The future distributions of A. sparsifolia were also projected in 2050 and 2070 under the climate change scenarios of RCP2.6 and RCP8.5 described in 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC).The result showed that mean temperature of the coldest quarter, annual mean temperature, precipitation of the coldest quarter, annual precipitation, precipitation of the wettest month, mean temperature of the wettest quarter and the temperature annual range were the seven climatic factors influencing the geographic distribution of A. sparsifolia under current climate, the suitable habitats are mainly located in the Xinjiang, in the middle and north of Gansu, in the west of Neimeng, in the north of Nei Monggol. From 2050 to 2070, the model simulations indicated that the suitable habitats of A. sparsifolia would decrease under the climate change scenarios of RCP2.6 and scenarios of RCP8.5 on the whole. Copyright© by the Chinese Pharmaceutical Association.

Predictive tests to evaluate oxidative potential of engineered nanomaterials

NASA Astrophysics Data System (ADS)

Ghiazza, Mara; Carella, Emanuele; Oliaro-Bosso, Simonetta; Corazzari, Ingrid; Viola, Franca; Fenoglio, Ivana

2013-04-01

Oxidative stress constitutes one of the principal injury mechanisms through which particulate toxicants (asbestos, crystalline silica, hard metals) and engineered nanomaterials can induce adverse health effects. ROS may be generated indirectly by activated cells and/or directly at the surface of the material. The occurrence of these processes depends upon the type of material. Many authors have recently demonstrated that metal oxides and carbon-based nanoparticles may influence (increasing or decreasing) the generation of oxygen radicals in a cell environment. Metal oxide, such as iron oxides, crystalline silica, and titanium dioxide are able to generate free radicals via different mechanisms causing an imbalance within oxidant species. The increase of ROS species may lead to inflammatory responses and in some cases to the development of cancer. On the other hand carbon-based nanomaterials, such as fullerene, carbon nanotubes, carbon black as well as cerium dioxide are able to scavenge the free radicals generated acting as antioxidant. The high numbers of new-engineered nanomaterials, which are introduced in the market, are exponentially increasing. Therefore the definition of toxicological strategies is urgently needed. The development of acellular screening tests will make possible the reduction of the number of in vitro and in vivo tests to be performed. An integrated protocol that may be used to predict the oxidant/antioxidant potential of engineered nanoparticles will be here presented.

CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

PubMed

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

Temporal and geographical external validation study and extension of the Mayo Clinic prediction model to predict eGFR in the younger population of Swiss ADPKD patients.

PubMed

Girardat-Rotar, Laura; Braun, Julia; Puhan, Milo A; Abraham, Alison G; Serra, Andreas L

2017-07-17

Prediction models in autosomal dominant polycystic kidney disease (ADPKD) are useful in clinical settings to identify patients with greater risk of a rapid disease progression in whom a treatment may have more benefits than harms. Mayo Clinic investigators developed a risk prediction tool for ADPKD patients using a single kidney value. Our aim was to perform an independent geographical and temporal external validation as well as evaluate the potential for improving the predictive performance by including additional information on total kidney volume. We used data from the on-going Swiss ADPKD study from 2006 to 2016. The main analysis included a sample size of 214 patients with Typical ADPKD (Class 1). We evaluated the Mayo Clinic model performance calibration and discrimination in our external sample and assessed whether predictive performance could be improved through the addition of subsequent kidney volume measurements beyond the baseline assessment. The calibration of both versions of the Mayo Clinic prediction model using continuous Height adjusted total kidney volume (HtTKV) and using risk subclasses was good, with R 2 of 78% and 70%, respectively. Accuracy was also good with 91.5% and 88.7% of the predicted within 30% of the observed, respectively. Additional information regarding kidney volume did not substantially improve the model performance. The Mayo Clinic prediction models are generalizable to other clinical settings and provide an accurate tool based on available predictors to identify patients at high risk for rapid disease progression.

THE EFFECTS OF CRACKING ON THE SURFACE POTENTIAL OF ICY GRAINS IN SATURN’S E-RING: LABORATORY STUDIES

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

Bu, Caixia; Bahr, David A.; Dukes, Catherine A.

2016-07-10

Within Saturn's E-ring, dust grains are coated by water vapor co-released with ice grains from the geyser-like eruptions of Enceladus. These ice-coated grains have intrinsic surface potential and interact synergistically with the ions and electrons of Saturn's magnetospheric plasmas. We perform laboratory experiments to investigate the effects of water-ice growth on the surface potential, using amorphous solid water (ASW) films. We estimate the growth of the surface potential to be ∼ 2.5 mV (Earth) yr{sup 1} and 112 mV yr{sup 1} for E-ring grains at ∼4.5 R {sub s} and 3.95 R {sub s} outside Enceladus’s plume, respectively. In addition,more » our measurements show that the linear relationship between the surface potential and the film thickness, as described in previous studies, has an upper limit, where the film spontaneously cracks above a porosity-dependent critical thickness. Heating of the cracked films with (and without) deposited charge shows that significant positive (and negative) surface potentials are retained at temperatures above 110 K, contrary to the minimal values (roughly zero) for thin, transparent ASW films. The significant surface potentials observed on micron-scale cracked ice films after thermal cycling, (5–20) V, are consistent with Cassini measurements, which indicate a negative charge of up to 5 V for E-ring dust particles at ∼5 R {sub s}. Therefore, the native grain surface potential resulting from water-vapor coating must be included in modeling studies of interactions between E-ring icy surfaces and Saturn's magnetospheric plasma.« less

Two-potential approach for electron-molecular collisions at intermediate and high energies - Application to e-N2 scatterings

NASA Technical Reports Server (NTRS)

Choi, B. H.; Poe, R. T.; Sun, J. C.; Shan, Y.

1979-01-01

A general theoretical approach is proposed for the calculation of elastic, vibrational, and rotational transitions for electron-molecule scattering at intermediate and high-electron-impact energies. In this formulation, contributions to the scattering process come from the incoherent sum of two dominant potentials: a short-range shielded nuclear Coulomb potential from individual atomic centers, and a permanent/induced long-range potential. Application to e-N2 scattering from 50-500 eV incident electron energies has yielded good agreement with absolutely calibrated experiments. Comparisons with other theoretical approaches are made. The physical picture as well as the general features of electron-molecule scattering process are discussed within the framework of the two-potential approach.

In vitro biological activities of the E6 and E7 genes vary among human papillomaviruses of different oncogenic potential.

PubMed Central

Barbosa, M S; Vass, W C; Lowy, D R; Schiller, J T

1991-01-01

Human papillomavirus type 16 (HPV-16) and HPV-18 are often detected in cervical carcinomas, while HPV-6, although frequently present in benign genital lesions, is only rarely present in cancers of the cervix. Therefore, infections with HPV-16 and HPV-18 are considered high risk and infection with HPV-6 is considered low risk. We found, by using a heterologous promoter system, that expression of the E7 transforming protein differs between high- and low-risk HPVs. In high-risk HPV-16, E7 is expressed from constructs containing the complete upstream E6 open reading frame. In contrast, HPV-6 E7 was efficiently translated only when E6 was deleted. By using clones in which the coding regions of HPV-6, HPV-16, and HPV-18 E7s were preceded by identical leader sequences, we found that the ability of the E7 gene products to induce anchorage-independent growth in rodent fibroblasts correlated directly with the oncogenic association of the HPV types. By using an immortalization assay of normal human keratinocytes that requires complementation of E6 and E7, we found that both E6 and E7 of HPV-18 could complement the corresponding gene from HPV-16. However, neither E6 nor E7 from HPV-6 was able to substitute for the corresponding gene of HPV-16 or HPV-18. Our results suggest that multiple factors, including lower intrinsic biological activity of E6 and E7 and differences in the regulation of their expression, account for the low activity of HPV-6, in comparison with HPV-16 and HPV-18, in in vitro assays. These same factors may, in part, account for the apparent difference in oncogenic potential between these viruses. Images PMID:1845889

The potential use of physical resilience to predict healthy aging.

PubMed

Schorr, Anna; Carter, Christy; Ladiges, Warren

2018-01-01

Physical resilience is the ability of an organism to respond to stressors that acutely disrupt normal physiological homeostasis. By definition, resilience decreases with increasing age, while frailty, defined as a decline in tissue function, increases with increasing age. Assessment of resilience could therefore be an informative early paradigm to predict healthy aging compared to frailty, which measures late life dysfunction. Parameters for resilience in the laboratory mouse are not yet well defined, and no single standardized stress test exists. Since aging involves multiple genetic pathways, integrative responses involving multiple tissues, organs, and activities need to be measured to reveal the overall resilience status, suggesting a battery of stress tests, rather than a single all-encompassing one, would be most informative. Three simple, reliable, and inexpensive stressors are described in this review that could be used as a panel to determine levels of resilience. Brief cold water immersion allows a recovery time to normothermia as an indicator of resilience to hypothermia, i.e. the quicker the return to normal body temperature, the more robust the resilience. Sleep deprivation (SD) impairs remote memory in aged mice, and has detrimental effects on glucose metabolism. Cyclophosphamide (CYP) targets white blood cells, especially myeloid cells resulting in neutropenia with a rebound neutrophilia in an age-dependent manner. Thus a strong neutrophilic response indicates resilience. In conclusion, resilience promises to be an especially useful measurement of biological age, i.e. how fast a particular organ or tissue ages. The three stressors, cold, SD, and CYP, are applicable to human medicine and aging because they represent clinically relevant stress conditions that have effects in an age-dependent manner. They are thus an attractive perturbation for resilience testing in mice to measure the effectiveness of interventions that target basic aging processes.

On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs.

PubMed

Bastin, C; Théron, L; Lainé, A; Gengler, N

2016-05-01

Fertility and health traits are of prime importance in dairy breeding programs. However, these traits are generally complex, difficult to record, and lowly heritable (<0.10), thereby hampering genetic improvement in disease resistance and fertility. Hence, indicators are useful in the prediction of genetic merit for fertility and health traits as long as they are easier to measure than direct fitness traits, heritable, and genetically correlated. Considering that changes in (fine) milk composition over a lactation reflect the physiological status of the cow, mid-infrared (MIR) analysis of milk opens the door to a wide range of potential indicator traits of fertility and health. Previous studies investigated the phenotypic and genetic relationships between fertility and MIR-predicted phenotypes, most being related to negative postpartum energy balance and body fat mobilization (e.g., fat:protein ratio, urea, fatty acids profile). Results showed that a combination of various fatty acid traits (e.g., C18:1 cis-9 and C10:0) could be used to improve fertility. Furthermore, occurrence of (sub)clinical ketosis has been related to milk-based phenotypes such as fat:protein ratio, fatty acids, and ketone bodies. Hence, MIR-predicted acetone and β-hydroxybutyrate contents in milk could be useful for breeding cows less susceptible to ketosis. Although studies investigating the genetic association among mastitis and MIR-predicted phenotypes are scarce, a wide range of traits, potentially predicted by MIR spectrometry, are worthy of consideration. These include traits related to the disease response of the cow (e.g., lactoferrin), reduced secretory activity (e.g., casein), and the alteration of the blood-milk barrier (e.g., minerals). Moreover, direct MIR prediction of fertility and health traits should be further considered. To conclude, MIR-predicted phenotypes have a role to play in the improvement of dairy cow fertility and health. However, further studies are warranted to

Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets.

PubMed

Prakash, Pravin; Rajakani, Raja; Gupta, Vikrant

2016-04-01

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 19-24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be -0.815 kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. Copyright © 2015 Elsevier Ltd. All rights reserved.

Lanthanide complex coordination polyhedron geometry prediction accuracies of ab initio effective core potential calculations.

PubMed

Freire, Ricardo O; Rocha, Gerd B; Simas, Alfredo M

2006-03-01

lanthanide coordination compounds efficiently and accurately is central for the design of new ligands capable of forming stable and highly luminescent complexes. Accordingly, we present in this paper a report on the capability of various ab initio effective core potential calculations in reproducing the coordination polyhedron geometries of lanthanide complexes. Starting with all combinations of HF, B3LYP and MP2(Full) with STO-3G, 3-21G, 6-31G, 6-31G* and 6-31+G basis sets for [Eu(H2O)9]3+ and closing with more manageable calculations for the larger complexes, we computed the fully predicted ab initio geometries for a total of 80 calculations on 52 complexes of Sm(III), Eu(III), Gd(III), Tb(III), Dy(III), Ho(III), Er(III) and Tm(III), the largest containing 164 atoms. Our results indicate that RHF/STO-3G/ECP appears to be the most efficient model chemistry in terms of coordination polyhedron crystallographic geometry predictions from isolated lanthanide complex ion calculations. Moreover, both augmenting the basis set and/or including electron correlation generally enlarged the deviations and aggravated the quality of the predicted coordination polyhedron crystallographic geometry. Our results further indicate that Cosentino et al.'s suggestion of using RHF/3-21G/ECP geometries appears to be indeed a more robust, but not necessarily, more accurate recommendation to be adopted for the general lanthanide complex case. [Figure: see text].

A Scientometric Prediction of the Discovery of the First Potentially Habitable Planet with a Mass Similar to Earth

PubMed Central

Arbesman, Samuel; Laughlin, Gregory

2010-01-01

Background The search for a habitable extrasolar planet has long interested scientists, but only recently have the tools become available to search for such planets. In the past decades, the number of known extrasolar planets has ballooned into the hundreds, and with it, the expectation that the discovery of the first Earth-like extrasolar planet is not far off. Methodology/Principal Findings Here, we develop a novel metric of habitability for discovered planets and use this to arrive at a prediction for when the first habitable planet will be discovered. Using a bootstrap analysis of currently discovered exoplanets, we predict the discovery of the first Earth-like planet to be announced in the first half of 2011, with the likeliest date being early May 2011. Conclusions/Significance Our predictions, using only the properties of previously discovered exoplanets, accord well with external estimates for the discovery of the first potentially habitable extrasolar planet and highlight the the usefulness of predictive scientometric techniques to understand the pace of scientific discovery in many fields. PMID:20957226

eShadow: A tool for comparing closely related sequences

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

Ovcharenko, Ivan; Boffelli, Dario; Loots, Gabriela G.

2004-01-15

Primate sequence comparisons are difficult to interpret due to the high degree of sequence similarity shared between such closely related species. Recently, a novel method, phylogenetic shadowing, has been pioneered for predicting functional elements in the human genome through the analysis of multiple primate sequence alignments. We have expanded this theoretical approach to create a computational tool, eShadow, for the identification of elements under selective pressure in multiple sequence alignments of closely related genomes, such as in comparisons of human to primate or mouse to rat DNA. This tool integrates two different statistical methods and allows for the dynamic visualizationmore » of the resulting conservation profile. eShadow also includes a versatile optimization module capable of training the underlying Hidden Markov Model to differentially predict functional sequences. This module grants the tool high flexibility in the analysis of multiple sequence alignments and in comparing sequences with different divergence rates. Here, we describe the eShadow comparative tool and its potential uses for analyzing both multiple nucleotide and protein alignments to predict putative functional elements. The eShadow tool is publicly available at http://eshadow.dcode.org/« less

Impact Assessment of Mikania Micrantha on Land Cover and Maxent Modeling to Predict its Potential Invasion Sites

NASA Astrophysics Data System (ADS)

Baidar, T.; Shrestha, A. B.; Ranjit, R.; Adhikari, R.; Ghimire, S.; Shrestha, N.

2017-05-01

Mikania micrantha is one of the major invasive alien plant species in tropical moist forest regions of Asia including Nepal. Recently, this weed is spreading at an alarming rate in Chitwan National Park (CNP) and threatening biodiversity. This paper aims to assess the impacts of Mikania micrantha on different land cover and to predict potential invasion sites in CNP using Maxent model. Primary data for this were presence point coordinates and perceived Mikania micrantha cover collected through systematic random sampling technique. Rapideye image, Shuttle Radar Topographic Mission data and bioclimatic variables were acquired as secondary data. Mikania micrantha distribution maps were prepared by overlaying the presence points on image classified by object based image analysis. The overall accuracy of classification was 90 % with Kappa coefficient 0.848. A table depicting the number of sample points in each land cover with respective Mikania micrantha coverage was extracted from the distribution maps to show the impact. The riverine forest was found to be the most affected land cover with 85.98 % presence points and sal forest was found to be very less affected with only 17.02 % presence points. Maxent modeling predicted the areas near the river valley as the potential invasion sites with statistically significant Area Under the Receiver Operating Curve (AUC) value of 0.969. Maximum temperature of warmest month and annual precipitation were identified as the predictor variables that contribute the most to Mikania micrantha's potential distribution.

Potential Seasonal Predictability for Winter Storms over Europe

NASA Astrophysics Data System (ADS)

Wild, Simon; Befort, Daniel J.; Leckebusch, Gregor C.

2017-04-01

Reliable seasonal forecasts of strong extra-tropical cyclones and windstorms would have great social and economical benefits, as these events are the most costly natural hazards over Europe. In a previous study we have shown good agreement of spatial climatological distributions of extra-tropical cyclones and wind storms in state-of-the-art multi-member seasonal prediction systems with reanalysis. We also found significant seasonal prediction skill of extra-tropical cyclones and windstorms affecting numerous European countries. We continue this research by investigating the mechanisms and precursor conditions (primarily over the North Atlantic) on a seasonal time scale leading to enhanced extra-tropical cyclone activity and winter storm frequency over Europe. Our results regarding mechanisms show that an increased surface temperature gradient at the western edge of the North Atlantic can be related to enhanced winter storm frequency further downstream causing for example a greater number of storms over the British Isles, as observed in winter 2013-14.The so-called "Horseshoe Index", a SST tripole anomaly pattern over the North Atlantic in the summer months can also cause a higher number of winter storms over Europe in the subsequent winter. We will show results of AMIP-type sensitivity experiments using an AGCM (ECHAM5), supporting this hypothesis. Finally we will analyse whether existing seasonal forecast systems are able to capture these identified mechanisms and precursor conditions affecting the models' seasonal prediction skill.

Potential of the Thermal Infrared Wavelength Region to predict semi-arid Soil Surface Properties for Remote Sensing Monitoring

NASA Astrophysics Data System (ADS)

Eisele, Andreas; Chabrillat, Sabine; Lau, Ian; Hecker, Christoph; Hewson, Robert; Carter, Dan; Wheaton, Buddy; Ong, Cindy; Cudahy, Thomas John; Kaufmann, Hermann

2014-05-01

Digital soil mapping with the means of passive remote sensing basically relies on the soils' spectral characteristics and an appropriate atmospheric window, where electromagnetic radiation transmits without significant attenuation. Traditionally the atmospheric window in the solar-reflective wavelength region (visible, VIS: 0.4 - 0.7 μm; near infrared, NIR: 0.7 - 1.1 μm; shortwave infrared, SWIR: 1.1 - 2.5 μm) has been used to quantify soil surface properties. However, spectral characteristics of semi-arid soils, typically have a coarse quartz rich texture and iron coatings that can limit the prediction of soil surface properties. In this study we investigated the potential of the atmospheric window in the thermal wavelength region (long wave infrared, LWIR: 8 - 14 μm) to predict soil surface properties such as the grain size distribution (texture) and the organic carbon content (SOC) for coarse-textured soils from the Australian wheat belt region. This region suffers soil loss due to wind erosion processes and large scale monitoring techniques, such as remote sensing, is urgently required to observe the dynamic changes of such soil properties. The coarse textured sandy soils of the investigated area require methods, which can measure the special spectral response of the quartz dominated mineralogy with iron oxide enriched grain coatings. By comparison, the spectroscopy using the solar-reflective region has limitations to discriminate such arid soil mineralogy and associated coatings. Such monitoring is important for observing potential desertification trends associated with coarsening of topsoil texture and reduction in SOC. In this laboratory study we identified the relevant LWIR wavelengths to predict these soil surface properties. The results showed the ability of multivariate analyses methods (PLSR) to predict these soil properties from the soil's spectral signature, where the texture parameters (clay and sand content) could be predicted well in the models

The role of zeta potential in the adhesion of E. coli to suspended intertidal sediments.

PubMed

Wyness, Adam J; Paterson, David M; Defew, Emma C; Stutter, Marc I; Avery, Lisa M

2018-05-29

The extent of pathogen transport to and within aquatic systems depends heavily on whether the bacterial cells are freely suspended or in association with suspended particles. The surface charge of both bacterial cells and suspended particles affects cell-particle adhesion and subsequent transport and exposure pathways through settling and resuspension cycles. This study investigated the adhesion of Faecal Indicator Organisms (FIOs) to natural suspended intertidal sediments over the salinity gradient encountered at the transition zone from freshwater to marine environments. Phenotypic characteristics of three E. coli strains, and the zeta potential (surface charge) of the E. coli strains and 3 physically different types of intertidal sediments was measured over a salinity gradient from 0 to 5 Practical Salinity Units (PSU). A batch adhesion microcosm experiment was constructed with each combination of E. coli strain, intertidal sediment and 0, 2, 3.5 and 5 PSU. The zeta potential profile of one E. coli strain had a low negative charge and did not change in response to an increase in salinity, and the remaining E. coli strains and the sediments exhibited a more negative charge that decreased with an increase in salinity. Strain type was the most important factor in explaining cell-particle adhesion, however adhesion was also dependant on sediment type and salinity (2, 3.5 PSU > 0, 5 PSU). Contrary to traditional colloidal (Derjaguin, Landau, Vervey, and Overbeek (DLVO)) theory, zeta potential of strain or sediment did not correlate with cell-particle adhesion. E. coli strain characteristics were the defining factor in cell-particle adhesion, implying that diverse strain-specific transport and exposure pathways may exist. Further research applying these findings on a catchment scale is necessary to elucidate these pathways in order to improve accuracy of FIO fate and transport models. Copyright © 2018 Elsevier Ltd. All rights reserved.

The accuracy of new wheelchair users' predictions about their future wheelchair use.

PubMed

Hoenig, Helen; Griffiths, Patricia; Ganesh, Shanti; Caves, Kevin; Harris, Frances

2012-06-01

This study examined the accuracy of new wheelchair user predictions about their future wheelchair use. This was a prospective cohort study of 84 community-dwelling veterans provided a new manual wheelchair. The association between predicted and actual wheelchair use was strong at 3 mos (ϕ coefficient = 0.56), with 90% of those who anticipated using the wheelchair at 3 mos still using it (i.e., positive predictive value = 0.96) and 60% of those who anticipated not using it indeed no longer using the wheelchair (i.e., negative predictive value = 0.60, overall accuracy = 0.92). Predictive accuracy diminished over time, with overall accuracy declining from 0.92 at 3 mos to 0.66 at 6 mos. At all time points, and for all types of use, patients better predicted use as opposed to disuse, with correspondingly higher positive than negative predictive values. Accuracy of prediction of use in specific indoor and outdoor locations varied according to location. This study demonstrates the importance of better understanding the potential mismatch between the anticipated and actual patterns of wheelchair use. The findings suggest that users can be relied upon to accurately predict their basic wheelchair-related needs in the short-term. Further exploration is needed to identify characteristics that will aid users and their providers in more accurately predicting mobility needs for the long-term.

The changes of HRV in refractory epilepsy: The potential index to predict the onset of epilepsy in children.

PubMed

Gong, Xuehao; Mao, Xuhua; Chen, Yan; Huang, Leidan; Liu, Weizong; Huang, Xian; Tan, Zheng; Wang, Xianming; Wu, Wanqing; Chen, Qian; Li, Rong

2016-01-01

In this study, we examine the potential of heart rate variability (HRV) as an efficient tool for predicting the onset of epilepsy in children. We totally collected 53 seizures EEG and ECG data using Video - EEG - ECG monitoring system. We then separated the ECG data into three segments: ten-minute before onset of each seizure, five-minute before onset of each seizure, and five-minute from the onset of each seizure. After the HRV parameters in all segments were calculated, we compared the differences between pre-ictal period and ictal period. We found that the values of meanHR, LF and LF/HF were greater in onset period. And the values of meanRR and the HF were less in ictal period. And it presented the similar changes when seizures occurred in the daytime and seizures occurred in the nighttime. In brief, we found that the sympathetic nervous system was under a more active status during onset period. We speculated that the HRV parameters such as the LF, HF or LF/HF could have potential to predict the seizures in children with epilepsy.

The Role of Nicotine Dependence in E-Cigarettes' Potential for Smoking Reduction.

PubMed

Selya, Arielle S; Dierker, Lisa; Rose, Jennifer S; Hedeker, Donald; Mermelstein, Robin J

2017-07-07

E-cigarettes (Electronic Nicotine Delivery Systems, or ENDS) are an increasingly popular tobacco product among youth. Some evidence suggests that e-cigarettes may be effective for harm reduction and smoking cessation, although these claims remain controversial. Little is known about how nicotine dependence may contribute to e-cigarettes' effectiveness in reducing or quitting conventional smoking. A cohort of young adults were surveyed over 4 years (approximately ages 19-23). Varying-coefficient models (VCMs) were used to examine the relationship between e-cigarette use and conventional smoking frequency, and how this relationship varies across users with different nicotine dependence levels. Lifetime, but not recent, e-cigarette use was associated with less frequent concurrent smoking of conventional cigarettes among those with high levels of nicotine dependence. However, nondependent e-cigarette users smoked conventional cigarettes slightly more frequently than those who had never used e-cigarettes. Nearly half of ever e-cigarette users reported using them to quit smoking at the last measurement wave. For those who used e-cigarettes in a cessation attempt, the frequency of e-cigarette use was not associated with reductions in future conventional smoking frequency. These findings offer possible support that e-cigarettes may act as a smoking reduction method among highly nicotine-dependent young adult cigarette smokers. However, the opposite was found in non-dependent smokers, suggesting that e-cigarette use should be discouraged among novice tobacco users. Additionally, although a substantial proportion of young adults used e-cigarettes to help them quit smoking, these self-initiated quit attempts with e-cigarettes were not associated with future smoking reduction or cessation. This study offers potential support for e-cigarettes as a smoking reduction tool among highly nicotine-dependent young adult conventional smokers, although the extent and nature of this

Propensity scores-potential outcomes framework to incorporate severity probabilities in the highway safety manual crash prediction algorithm.

PubMed

Sasidharan, Lekshmi; Donnell, Eric T

2014-10-01

Accurate estimation of the expected number of crashes at different severity levels for entities with and without countermeasures plays a vital role in selecting countermeasures in the framework of the safety management process. The current practice is to use the American Association of State Highway and Transportation Officials' Highway Safety Manual crash prediction algorithms, which combine safety performance functions and crash modification factors, to estimate the effects of safety countermeasures on different highway and street facility types. Many of these crash prediction algorithms are based solely on crash frequency, or assume that severity outcomes are unchanged when planning for, or implementing, safety countermeasures. Failing to account for the uncertainty associated with crash severity outcomes, and assuming crash severity distributions remain unchanged in safety performance evaluations, limits the utility of the Highway Safety Manual crash prediction algorithms in assessing the effect of safety countermeasures on crash severity. This study demonstrates the application of a propensity scores-potential outcomes framework to estimate the probability distribution for the occurrence of different crash severity levels by accounting for the uncertainties associated with them. The probability of fatal and severe injury crash occurrence at lighted and unlighted intersections is estimated in this paper using data from Minnesota. The results show that the expected probability of occurrence of fatal and severe injury crashes at a lighted intersection was 1 in 35 crashes and the estimated risk ratio indicates that the respective probabilities at an unlighted intersection was 1.14 times higher compared to lighted intersections. The results from the potential outcomes-propensity scores framework are compared to results obtained from traditional binary logit models, without application of propensity scores matching. Traditional binary logit analysis suggests that

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

PubMed

van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

2018-06-01

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Delirium prediction in the intensive care unit: comparison of two delirium prediction models.

PubMed

Wassenaar, Annelies; Schoonhoven, Lisette; Devlin, John W; van Haren, Frank M P; Slooter, Arjen J C; Jorens, Philippe G; van der Jagt, Mathieu; Simons, Koen S; Egerod, Ingrid; Burry, Lisa D; Beishuizen, Albertus; Matos, Joaquim; Donders, A Rogier T; Pickkers, Peter; van den Boogaard, Mark

2018-05-05

Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction  model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of - 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible. While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni

PubMed Central

Yang, Ya; Cheng, Wanting; Wu, Xiaoying; Huang, Shaoyu; Deng, Zhuohui; Zeng, Xin; Yang, Yu; Wu, Zhongdao; Chen, Yue; Zhou, Yibiao; Jiang, Qingwu

2018-01-01

Background Schistosomiasis is a snail-borne parasitic disease and is endemic in many tropical and subtropical countries. Biomphalaria straminea, an intermediate host for Schistosoma mansoni, is native to the southeastern part of South America and has established in other regions of South America, Central America and southern China during the last decades. S. mansoni is endemic in Africa, the Middle East, South America and the Caribbean. Knowledge of the potential global distribution of this snail is essential for risk assessment, monitoring, disease prevention and control. Methods and findings A comprehensive database of cross-continental occurrence for B. straminea was compiled to construct ecological models. We used several approaches to investigate the distribution of B. straminea, including direct comparison of climatic conditions, principal component analysis and niche overlap analyses to detect niche shifts. We also investigated the impacts of bioclimatic and human factors, and then used the bioclimatic and footprint layers to predict the potential distribution of B. straminea at global scale. We detected niche shifts accompanying the invasions of B. straminea in the Americas and China. The introduced populations had enlarged its habitats to subtropical regions where annual mean temperature is relatively low. Annual mean temperature, isothermality and temperature seasonality were identified as most important climatic features for the occurrence of B. straminea. Additionally, human factors improved the model prediction (P<0.001). Our model showed that under current climate conditions the snail should mostly be confined to the tropic and subtropic regions, including South America, Central America, Sub-Saharan Africa and Southeast Asia. Conclusions Our results confirmed that niche shifts took place in the invasions of B. straminea, in which bioclimatic and human factors played an important role. Our model predicted the global distribution of B. straminea based

Predictions of future ephemeral springtime waterbird stopover habitat availability under global change

USGS Publications Warehouse

Uden, Daniel R.; Allen, Craig R.; Bishop, Andrew A.; Grosse, Roger; Jorgensen, Christopher F.; LaGrange, Theodore G.; Stutheit, Randy G.; Vrtiska, Mark P.

2015-01-01

In the present period of rapid, worldwide change in climate and landuse (i.e., global change), successful biodiversity conservation warrants proactive management responses, especially for long-distance migratory species. However, the development and implementation of management strategies can be impeded by high levels of uncertainty and low levels of control over potentially impactful future events and their effects. Scenario planning and modeling are useful tools for expanding perspectives and informing decisions under these conditions. We coupled scenario planning and statistical modeling to explain and predict playa wetland inundation (i.e., presence/absence of water) and ponded area (i.e., extent of water) in the Rainwater Basin, an anthropogenically altered landscape that provides critical stopover habitat for migratory waterbirds. Inundation and ponded area models for total wetlands, those embedded in rowcrop fields, and those not embedded in rowcrop fields were trained and tested with wetland ponding data from 2004 and 2006–2009, and then used to make additional predictions under two alternative climate change scenarios for the year 2050, yielding a total of six predictive models and 18 prediction sets. Model performance ranged from moderate to good, with inundation models outperforming ponded area models, and models for non-rowcrop-embedded wetlands outperforming models for total wetlands and rowcrop-embedded wetlands. Model predictions indicate that if the temperature and precipitation changes assumed under our climate change scenarios occur, wetland stopover habitat availability in the Rainwater Basin could decrease in the future. The results of this and similar studies could be aggregated to increase knowledge about the potential spatial and temporal distributions of future stopover habitat along migration corridors, and to develop and prioritize multi-scale management actions aimed at mitigating the detrimental effects of global change on migratory

Alpha Decay Potential Barriers and Half-Lives and Analytical Formula Predictions for Superheavy Nuclei

NASA Astrophysics Data System (ADS)

Royer, Guy; Zhang, Hongfei

The α decay potential barriers are determined in the cluster-like shape path within a generalized liquid drop model including the proximity effects between the α particle and the daughter nucleus and adjusted to reproduce the experimental Qα. The α emission half-lives are determined within the WKB penetration probability. Calculations using previously proposed formulae depending only on the mass and charge of the alpha emitter and Qα are also compared with new experimental alpha-decay half-lives. The agreement allows to provide predictions for the α decay half-lives of other still unknown superheavy nuclei using the Qα determined from the 2003 atomic mass evaluation of Audi, Wapstra and Thibault.

Using the Personality Assessment Inventory Antisocial and Borderline Features Scales to Predict Behavior Change.

PubMed

Penson, Brittany N; Ruchensky, Jared R; Morey, Leslie C; Edens, John F

2016-11-01

A substantial amount of research has examined the developmental trajectory of antisocial behavior and, in particular, the relationship between antisocial behavior and maladaptive personality traits. However, research typically has not controlled for previous behavior (e.g., past violence) when examining the utility of personality measures, such as self-report scales of antisocial and borderline traits, in predicting future behavior (e.g., subsequent violence). Examination of the potential interactive effects of measures of both antisocial and borderline traits also is relatively rare in longitudinal research predicting adverse outcomes. The current study utilizes a large sample of youthful offenders ( N = 1,354) from the Pathways to Desistance project to examine the separate effects of the Personality Assessment Inventory Antisocial Features (ANT) and Borderline Features (BOR) scales in predicting future offending behavior as well as trends in other negative outcomes (e.g., substance abuse, violence, employment difficulties) over a 1-year follow-up period. In addition, an ANT × BOR interaction term was created to explore the predictive effects of secondary psychopathy. ANT and BOR both explained unique variance in the prediction of various negative outcomes even after controlling for past indicators of those same behaviors during the preceding year.

Manipulating time and space: Collision prediction in peripersonal and extrapersonal space.

PubMed

Iachini, Tina; Ruotolo, Francesco; Vinciguerra, Michela; Ruggiero, Gennaro

2017-09-01

Being able to predict potential collisions is a necessary survival prerequisite for all moving species. Temporal and spatial information is fundamental for this purpose. However, it is not clear yet if the peripersonal (i.e. near) and extrapersonal (i.e. far) distance between our body and the moving objects affects the way in which we can predict possible collisions. In order to assess this, we manipulated independently velocity and path of two balls moving one towards the other in such a way as to collide or not in peripersonal and extrapersonal space. In two experiments, participants had to judge if these balls were to collide or not. The results consistently showed a lower discrimination capacity and a more liberal tendency to predict collisions when the moving balls were in peripersonal space and their velocity was different rather than equal. This did not happen in extrapersonal space. Therefore, peripersonal space was particularly affected by temporal information. The possible link between the motor and anticipatory adaptive function of peripersonal space and collision prediction mechanisms is discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Predictive values of egg-specific IgE by two commonly used assay systems for the diagnosis of egg allergy in young children: a prospective multicenter study.

PubMed

Furuya, K; Nagao, M; Sato, Y; Ito, S; Fujisawa, T

2016-10-01

Specific IgE (sIgE) is often used to predict oral food challenge (OFC) outcomes in food allergy, but interpretation of the results may vary depending on the assay method employed and the patient population tested. The aim of this study was to use two commercial assay systems to determine egg-sIgE values predictive of allergy within the most common populations treated at pediatric clinics. In a multicenter prospective study, 433 children with suspected or confirmed egg allergy underwent oral challenge (OFC) using cooked egg (CE) and raw egg (RE) powders to diagnose either true allergy in 1-year-old (group A, n = 220) or tolerance in 2- to 6-year-old (group B, n = 213). Egg white (EW)- and ovomucoid (OM)-sIgE values were measured using the ImmunoCAP(®) sIgE (ImmunoCAP) and the IMMULITE(®) 2000 3 gAllergy(™) (3gAllergy) systems. Children were recruited from six primary care clinics and 18 hospitals in Japan. Receiver-operating characteristic (ROC) curve analysis yielded similar areas under the curve (AUC) for the two assays (0.7-0.8). The optimal cutoff values and the probability curves (PCs) of the sIgE by the two assays to predict CE and RE OFC outcomes were determined for both groups. Values for 3gAllergy were higher than for ImmunoCAP; however, correlation of sIgE and predicted probability calculated by PCs were strong between the two methods. Cutoff values and PCs for egg-sIgE established using both ImmunoCAP and 3gAllergy may be useful for predicting egg allergy in early childhood patient populations. © 2016 The Authors. Allergy Published by John Wiley & Sons Ltd.

Gene expression analysis in zebrafish embryos: a potential approach to predict effect concentrations in the fish early life stage test.

PubMed

Weil, Mirco; Scholz, Stefan; Zimmer, Michaela; Sacher, Frank; Duis, Karen

2009-09-01

Based on the hypothesis that analysis of gene expression could be used to predict chronic fish toxicity, the zebrafish (Danio rerio) embryo test (DarT), developed as a replacement method for the acute fish test, was expanded to a gene expression D. rerio embryo test (Gene-DarT). The effects of 14 substances on lethal and sublethal endpoints of the DarT and on expression of potential marker genes were investigated: the aryl hydrocarbon receptor 2, cytochrome P450 1A (cypla), heat shock protein 70, fizzy-related protein 1, the transcription factors v-maf musculoaponeurotic fibrosarcoma oncogene family protein g (avian) 1 and NF-E2-p45-related factor, and heme oxygenase 1 (hmox1). After exposure of zebrafish embryos for 48 h, differential gene expression was evaluated using reverse transcriptase-polymerase chain reaction, gel electrophoresis, and densitometric analysis of the gels. All tested compounds significantly affected the expression of at least one potential marker gene, with cyp1a and hmox1 being most sensitive. Lowest-observed-effect concentrations (LOECs) for gene expression were below concentrations resulting in 10% lethal effects in the DarT. For 10 (3,4- and 3,5-dichloroaniline, 1,4-dichlorobenzene, 2,4-dinitrophenol, atrazine, parathion-ethyl, chlorotoluron, genistein, 4-nitroquinoline-1-oxide, and cadmium) out of the 14 tested substances, LOEC values derived with the Gene-DarT differ by a factor of less than 10 from LOEC values of fish early life stage tests with zebrafish. For pentachloroaniline and pentachlorobenzene, the Gene-DarT showed a 23- and 153-fold higher sensitivity, respectively, while for lindane, it showed a 13-fold lower sensitivity. For ivermectin, the Gene-DarT was by a factor of more than 1,000 less sensitive than the acute fish test. The results of the present study indicate that gene expression analysis in zebrafish embryos could principally be used to predict effect concentrations in the fish early life stage test.

Applicability of effective fragment potential version 2 - Molecular dynamics (EFP2-MD) simulations for predicting excess properties of mixed solvents

NASA Astrophysics Data System (ADS)

Kuroki, Nahoko; Mori, Hirotoshi

2018-02-01

Effective fragment potential version 2 - molecular dynamics (EFP2-MD) simulations, where the EFP2 is a polarizable force field based on ab initio electronic structure calculations were applied to water-methanol binary mixture. Comparing EFP2s defined with (aug-)cc-pVXZ (X = D,T) basis sets, it was found that large sets are necessary to generate sufficiently accurate EFP2 for predicting mixture properties. It was shown that EFP2-MD could predict the excess molar volume. Since the computational cost of EFP2-MD are far less than ab initio MD, the results presented herein demonstrate that EFP2-MD is promising for predicting physicochemical properties of novel mixed solvents.

Multiple e-pharmacophore modelling pooled with high-throughput virtual screening, docking and molecular dynamics simulations to discover potential inhibitors of Plasmodium falciparum lactate dehydrogenase (PfLDH).

PubMed

Saxena, Shalini; Durgam, Laxman; Guruprasad, Lalitha

2018-05-14

Development of new antimalarial drugs continues to be of huge importance because of the resistance of malarial parasite towards currently used drugs. Due to the reliance of parasite on glycolysis for energy generation, glycolytic enzymes have played important role as potential targets for the development of new drugs. Plasmodium falciparum lactate dehydrogenase (PfLDH) is a key enzyme for energy generation of malarial parasites and is considered to be a potential antimalarial target. Presently, there are nearly 15 crystal structures bound with inhibitors and substrate that are available in the protein data bank (PDB). In the present work, we attempted to consider multiple crystal structures with bound inhibitors showing affinity in the range of 1.4 × 10 2 -1.3 × 10 6  nM efficacy and optimized the pharmacophore based on the energy involved in binding termed as e-pharmacophore mapping. A high throughput virtual screening (HTVS) combined with molecular docking, ADME predictions and molecular dynamics simulation led to the identification of 20 potential compounds which could be further developed as novel inhibitors for PfLDH.

Inhibitors of ubiquitin E3 ligase as potential new antimalarial drug leads.

PubMed

Jain, Jagrati; Jain, Surendra K; Walker, Larry A; Tekwani, Babu L

2017-06-02

inhibitors shall provide better understanding regarding the importance of E3 ligase functions in the malaria parasite as a potential new antimalarial drug target and a new class of antimalarial drug leads.

Human Papillomavirus E6/E7-Specific siRNA Potentiates the Effect of Radiotherapy for Cervical Cancer in Vitro and in Vivo

PubMed Central

Jung, Hun Soon; Rajasekaran, Nirmal; Song, Sang Yong; Kim, Young Deug; Hong, Sungyoul; Choi, Hyuck Jae; Kim, Young Seok; Choi, Jong-Sun; Choi, Yoon-La; Shin, Young Kee

2015-01-01

The functional inactivation of TP53 and Rb tumor suppressor proteins by the HPV-derived E6 and E7 oncoproteins is likely an important step in cervical carcinogenesis. We have previously shown siRNA technology to selectively silence both E6/E7 oncogenes and demonstrated that the synthetic siRNAs could specifically block its expression in HPV-positive cervical cancer cells. Herein, we investigated the potentiality of E6/E7 siRNA candidates as radiosensitizers of radiotherapy for the human cervical carcinomas. HeLa and SiHa cells were transfected with HPV E6/E7 siRNA; the combined cytotoxic effect of E6/E7 siRNA and radiation was assessed by using the cell viability assay, flow cytometric analysis and the senescence-associated β-galactosidase (SA-β-Gal) assay. In addition, we also investigated the effect of combined therapy with irradiation and E6/E7 siRNA intravenous injection in an in vivo xenograft model. Combination therapy with siRNA and irradiation efficiently retarded tumor growth in established tumors of human cervical cancer cell xenografted mice. In addition, the chemically-modified HPV16 and 18 E6/E7 pooled siRNA in combination with irradiation strongly inhibited the growth of cervical cancer cells. Our results indicated that simultaneous inhibition of HPV E6/E7 oncogene expression with radiotherapy can promote potent antitumor activity and radiosensitizing activity in human cervical carcinomas. PMID:26035754

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

USGS Publications Warehouse

Magarey, Roger; Newton, Leslie; Hong, Seung C.; Takeuchi, Yu; Christie, Dave; Jarnevich, Catherine S.; Kohl, Lisa; Damus, Martin; Higgins, Steven I.; Miller, Leah; Castro, Karen; West, Amanda; Hastings, John; Cook, Gericke; Kartesz, John; Koop, Anthony

2018-01-01

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

Adsorption of metal atoms at a buckled graphene grain boundary using model potentials

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

Helgee, Edit E.; Isacsson, Andreas

Two model potentials have been evaluated with regard to their ability to model adsorption of single metal atoms on a buckled graphene grain boundary. One of the potentials is a Lennard-Jones potential parametrized for gold and carbon, while the other is a bond-order potential parametrized for the interaction between carbon and platinum. Metals are expected to adsorb more strongly to grain boundaries than to pristine graphene due to their enhanced adsorption at point defects resembling those that constitute the grain boundary. Of the two potentials considered here, only the bond-order potential reproduces this behavior and predicts the energy of themore » adsorbate to be about 0.8 eV lower at the grain boundary than on pristine graphene. The Lennard-Jones potential predicts no significant difference in energy between adsorbates at the boundary and on pristine graphene. These results indicate that the Lennard-Jones potential is not suitable for studies of metal adsorption on defects in graphene, and that bond-order potentials are preferable.« less

Embedded-atom-method interatomic potentials from lattice inversion.

PubMed

Yuan, Xiao-Jian; Chen, Nan-Xian; Shen, Jiang; Hu, Wangyu

2010-09-22

The present work develops a physically reliable procedure for building the embedded-atom-method (EAM) interatomic potentials for the metals with fcc, bcc and hcp structures. This is mainly based on Chen-Möbius lattice inversion (Chen et al 1997 Phys. Rev. E 55 R5) and first-principles calculations. Following Baskes (Baskes et al 2007 Phys. Rev. B 75 094113), this new version of the EAM eliminates all of the prior arbitrary choices in the determination of the atomic electron density and pair potential functions. Parameterizing the universal form deduced from the calculations within the density-functional scheme for homogeneous electron gas as the embedding function, the new-type EAM potentials for Cu, Fe and Ti metals have successfully been constructed by considering interatomic interactions up to the fifth neighbor, the third neighbor and the seventh neighbor, respectively. The predictions of elastic constants, structural energy difference, vacancy formation energy and migration energy, activation energy of vacancy diffusion, latent heat of melting and relative volume change on melting all satisfactorily agree with the experimental results available or first-principles calculations. The predicted surface energies for low-index crystal faces and the melting point are in agreement with the experimental data to the same extent as those calculated by other EAM-type potentials such as the FBD-EAM, 2NN MEAM and MS-EAM. In addition, the order among the predicted low-index surface energies is also consistent with the experimental information.

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

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

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Cross, Kevin P.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describemore » the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.« less

Semimicroscopic, Lane-consistent nucleon-nucleus optical model potential up to 200 MeV

NASA Astrophysics Data System (ADS)

Bauge, Eric; Delaroche, Jean-Paul; Girod, Michel

2000-10-01

Our semimicroscopic optical model potential (E. Bauge et al., Phys. Rev. C 58), 1118 (1998). is re-evaluated in order to obtain a Lane-consistent description of (p,p), (n,n) and (p,n IAS) elastic scattering and reaction observables. The re-assessed nuclear matter interaction (which includes sizable renormalizations of the isovector potentials) is folded with microscopic HFB nuclear densities, producing OMPs that are free of adjustable parameters for nuclei with A >= 40. With Lane-consistency of the interaction, and the predictive nature of our HFB calculations, this scheme can be used to calculate observables for nuclei far from the stability line with good predictivity.

Predicting Seawater Intrusion in Coastal Groundwater Boreholes Using Self-Potential Data

NASA Astrophysics Data System (ADS)

Graham, M.; MacAllister, D. J.; Jackson, M.; Vinogradov, J.; Butler, A. P.

2017-12-01

Many coastal groundwater abstraction wells are under threat from seawater intrusion: this is exacerbated in summer by low water tables and increased abstraction. Existing hydrochemistry or geophysical techniques often fail to predict the timing of intrusion events. We investigate whether the presence and transport of seawater can influence self-potentials (SPs) measured within groundwater boreholes, with the aim of using SP monitoring to provide early warning of saline intrusion. SP data collection: SP data were collected from a coastal groundwater borehole and an inland borehole (> 60 km from the coast) in the Seaford Chalk of southern England. The SP gradient in the inland borehole was approximately 0.05 mV/m, while that in the coastal borehole varied from 0.16-0.26 mV/m throughout the monitoring period. Spectral analysis showed that semi-diurnal fluctuations in the SP gradient were several orders of magnitude higher at the coast than inland, indicating a strong influence from oceanic tides. A characteristic decrease in the gradient, or precursor, was observed in the coastal borehole several days prior to seawater intrusion. Modelling results: Hydrodynamic transport and geoelectric modelling suggest that observed pressure changes (associated with the streaming potential) are insufficient to explain either the magnitude of the coastal SP gradient or the semi-diurnal SP fluctuations. By contrast, a model of the exclusion-diffusion potential closely matches these observations and produces a precursor similar to that observed in the field. Sensitivity analysis suggests that both a sharp saline front and spatial variations in the exclusion efficiency arising from aquifer heterogeneities are necessary to explain the SP gradient observed in the coastal borehole. The presence of the precursor in the model depends also on the presence and depth of fractures near the base of the borehole. Conclusions: Our results indicate that SP monitoring, combined with hydrodynamic

Influence of surface conductivity on the apparent zeta potential of calcite.

PubMed

Li, Shuai; Leroy, Philippe; Heberling, Frank; Devau, Nicolas; Jougnot, Damien; Chiaberge, Christophe

2016-04-15

Zeta potential is a physicochemical parameter of particular importance in describing the surface electrical properties of charged porous media. However, the zeta potential of calcite is still poorly known because of the difficulty to interpret streaming potential experiments. The Helmholtz-Smoluchowski (HS) equation is widely used to estimate the apparent zeta potential from these experiments. However, this equation neglects the influence of surface conductivity on streaming potential. We present streaming potential and electrical conductivity measurements on a calcite powder in contact with an aqueous NaCl electrolyte. Our streaming potential model corrects the apparent zeta potential of calcite by accounting for the influence of surface conductivity and flow regime. We show that the HS equation seriously underestimates the zeta potential of calcite, particularly when the electrolyte is diluted (ionic strength ⩽ 0.01 M) because of calcite surface conductivity. The basic Stern model successfully predicted the corrected zeta potential by assuming that the zeta potential is located at the outer Helmholtz plane, i.e. without considering a stagnant diffuse layer at the calcite-water interface. The surface conductivity of calcite crystals was inferred from electrical conductivity measurements and computed using our basic Stern model. Surface conductivity was also successfully predicted by our surface complexation model. Copyright © 2016 Elsevier Inc. All rights reserved.