Profiled support vector machines for antisense oligonucleotide efficacy prediction

Directory of Open Access Journals (Sweden)

Martín-Guerrero José D

2004-09-01

Full Text Available Abstract Background This paper presents the use of Support Vector Machines (SVMs for prediction and analysis of antisense oligonucleotide (AO efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1 feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE, and (2 AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278 and predicted high (>75% inhibition of gene expression and low efficacy (http://aosvm.cgb.ki.se/. Conclusions The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.

Gaussian interaction profile kernels for predicting drug-target interaction.

Science.gov (United States)

van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

2011-11-01

The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

Comparison of predicted and measured pulsed-column profiles and inventories

International Nuclear Information System (INIS)

Ostenak, C.A.; Cermak, A.F.

1983-01-01

Nuclear materials accounting and process control in fuels reprocessing plants can be improved by near-real-time estimation of the in-process inventory in solvent-extraction contactors. Experimental studies were conducted on pilot- and plant-scale pulsed columns by Allied-General Nuclear Service (AGNS), and the extensive uranium concentration-profile and inventory data were analyzed by Los Alamos and AGNS to develop and evaluate different predictive inventory techniques. Preliminary comparisons of predicted and measured pulsed-column profiles and inventories show promise for using these predictive techniques to improve nuclear materials accounting and process control in fuels reprocessing plants

Comparison of Cluster Lensing Profiles with Lambda CDM Predictions

Energy Technology Data Exchange (ETDEWEB)

Broadhurst, Tom; /Tel Aviv U.; Umetsu, Keiichi; /Taipei, Inst. Astron. Astrophys.; Medezinski, Elinor; /Tel Aviv U.; Oguri, Masamune; /KIPAC, Menlo Park; Rephaeli, Yoel; /Tel Aviv U. /San Diego, CASS

2008-05-21

We derive lens distortion and magnification profiles of four well known clusters observed with Subaru. Each cluster is very well fitted by the general form predicted for Cold Dark Matter (CDM) dominated halos, with good consistency found between the independent distortion and magnification measurements. The inferred level of mass concentration is surprisingly high, 8 < c{sub vir} < 15 ( = 10.39 {+-} 0.91), compared to the relatively shallow profiles predicted by the {Lambda}CDM model, c{sub vir} = 5.06 {+-} 1.10 (for = 1.25 x 10{sup 15} M{sub {circle_dot}}/h). This represents a 4{sigma} discrepancy, and includes the relatively modest effects of projection bias and profile evolution derived from N-body simulations, which oppose each other with little residual effect. In the context of CDM based cosmologies, this discrepancy implies some modification of the widely assumed spectrum of initial density perturbations, so clusters collapse earlier (z {ge} 1) than predicted (z < 0.5) when the Universe was correspondingly denser.

The prediction of BRDFs from surface profile measurements

International Nuclear Information System (INIS)

Church, E.L.; Takacs, P.Z.; Leonard, T.A.

1989-01-01

This paper discusses methods of predicting the BRDF of smooth surfaces from profile measurements of their surface finish. The conversion of optical profile data to the BRDF at the same wavelength is essentially independent of scattering models, while the conversion of mechanical measurements, and wavelength scaling in general, are model dependent. Procedures are illustrated for several surfaces, including two from the recent HeNe BRDF round robin, and results are compared with measured data. Reasonable agreement is found except for surfaces which involve significant scattering from isolated surface defects which are poorly sampled in the profile data

Validity of a manual soft tissue profile prediction method following mandibular setback osteotomy.

Science.gov (United States)

Kolokitha, Olga-Elpis

2007-10-01

The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication.

A comparison on radar range profiles between in-flight measurements and RCS-predictions

NARCIS (Netherlands)

Heiden, R. van der; Ewijk, L.J. van; Groen, F.C.A.

1998-01-01

The validation of Radar Cross Section (RCS) prediction techniques against real measurements is crucial to acquire confidence in predictions when measurements are nut available. In this paper we present the results of a comparison on one-dimensional signatures, i.e. radar range profiles. The profiles

Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

Directory of Open Access Journals (Sweden)

Liying Yang

2016-01-01

Full Text Available Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio. Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM, K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance. Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.

Predicting Low Energy Dopant Implant Profiles in Semiconductors using Molecular Dynamics

Energy Technology Data Exchange (ETDEWEB)

Beardmore, K.M.; Gronbech-Jensen, N.

1999-05-02

The authors present a highly efficient molecular dynamics scheme for calculating dopant density profiles in group-IV alloy, and III-V zinc blende structure materials. Their scheme incorporates several necessary methods for reducing computational overhead, plus a rare event algorithm to give statistical accuracy over several orders of magnitude change in the dopant concentration. The code uses a molecular dynamics (MD) model to describe ion-target interactions. Atomic interactions are described by a combination of 'many-body' and pair specific screened Coulomb potentials. Accumulative damage is accounted for using a Kinchin-Pease type model, inelastic energy loss is represented by a Firsov expression, and electronic stopping is described by a modified Brandt-Kitagawa model which contains a single adjustable ion-target dependent parameter. Thus, the program is easily extensible beyond a given validation range, and is therefore truly predictive over a wide range of implant energies and angles. The scheme is especially suited for calculating profiles due to low energy and to situations where a predictive capability is required with the minimum of experimental validation. They give examples of using the code to calculate concentration profiles and 2D 'point response' profiles of dopants in crystalline silicon and gallium-arsenide. Here they can predict the experimental profile over five orders of magnitude for <100> and <110> channeling and for non-channeling implants at energies up to hundreds of keV.

Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling

DEFF Research Database (Denmark)

Simonsen, Martin; Maetschke, S.R.; Ragan, M.A.

2012-01-01

Motivation: Phylogenetic profiling methods can achieve good accuracy in predicting protein–protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly......: We present three novel methods for automating the selection of RT, using machine learning based on known protein–protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting...... phylogenetic profiles often require very different RT sets to support high prediction accuracy....

Geometrical theory to predict eccentric photorefraction intensity profiles in the human eye

Science.gov (United States)

Roorda, Austin; Campbell, Melanie C. W.; Bobier, W. R.

1995-08-01

In eccentric photorefraction, light returning from the retina of the eye is photographed by a camera focused on the eye's pupil. We use a geometrical model of eccentric photorefraction to generate intensity profiles across the pupil image. The intensity profiles for three different monochromatic aberration functions induced in a single eye are predicted and show good agreement with the measured eccentric photorefraction intensity profiles. A directional reflection from the retina is incorporated into the calculation. Intensity profiles for symmetric and asymmetric aberrations are generated and measured. The latter profile shows a dependency on the source position and the meridian. The magnitude of the effect of thresholding on measured pattern extents is predicted. Monochromatic aberrations in human eyes will cause deviations in the eccentric photorefraction measurements from traditional crescents caused by defocus and may cause misdiagnoses of ametropia or anisometropia. Our results suggest that measuring refraction along the vertical meridian is preferred for screening studies with the eccentric photorefractor.

A lifetime prediction method for LEDs considering mission profiles

DEFF Research Database (Denmark)

Qu, Xiaohui; Wang, Huai; Zhan, Xiaoqing

2016-01-01

and to benchmark the cost-competitiveness of different lighting technologies. The existing lifetime data released by LED manufacturers or standard organizations are usually applicable only for specific temperature and current levels. Significant lifetime discrepancies may be observed in field operations due...... to the varying operational and environmental conditions during the entire service time (i.e., mission profiles). To overcome the challenge, this paper proposes an advanced lifetime prediction method, which takes into account the field operation mission profiles and the statistical properties of the life data...

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

Science.gov (United States)

Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar

2017-01-01

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

Directory of Open Access Journals (Sweden)

Rianon Zaman

2017-01-01

Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

A new algorithm predicts pressure and temperature profiles of gas/gas-condensate transmission pipelines

Energy Technology Data Exchange (ETDEWEB)

Mokhatab, Saied [OIEC - Oil Industries' Engineering and Construction Group, Tehran (Iran, Islamic Republic of); Vatani, Ali [University of Tehran (Iran, Islamic Republic of)

2003-07-01

The main objective of the present study has been the development of a relatively simple analytical algorithm for predicting flow temperature and pressure profiles along the two-phase, gas/gas-condensate transmission pipelines. Results demonstrate the ability of the method to predict reasonably accurate pressure gradient and temperature gradient profiles under operating conditions. (author)

Prediction of metastasis from low-malignant breast cancer by gene expression profiling

DEFF Research Database (Denmark)

Thomassen, Mads; Tan, Qihua; Eiriksdottir, Freyja

2007-01-01

examined in these studies is the low-risk patients for whom outcome is very difficult to predict with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study, 26 tumors from low-risk patients...... with different characteristics and risk, expression-based classification specifically developed in low-risk patients have higher predictive power in this group.......Promising results for prediction of outcome in breast cancer have been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly...

A Lifetime Prediction Method for LEDs Considering Real Mission Profiles

DEFF Research Database (Denmark)

Qu, Xiaohui; Wang, Huai; Zhan, Xiaoqing

2017-01-01

operations due to the varying operational and environmental conditions during the entire service time (i.e., mission profiles). To overcome the challenge, this paper proposes an advanced lifetime prediction method, which takes into account the field operation mission profiles and also the statistical......The Light-Emitting Diode (LED) has become a very promising alternative lighting source with the advantages of longer lifetime and higher efficiency than traditional ones. The lifetime prediction of LEDs is important to guide the LED system designers to fulfill the design specifications...... properties of the life data available from accelerated degradation testing. The electrical and thermal characteristics of LEDs are measured by a T3Ster system, used for the electro-thermal modeling. It also identifies key variables (e.g., heat sink parameters) that can be designed to achieve a specified...

NESmapper: accurate prediction of leucine-rich nuclear export signals using activity-based profiles.

Directory of Open Access Journals (Sweden)

Shunichi Kosugi

2014-09-01

Full Text Available The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs in the cargo proteins, and modulates nuclear-cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS. Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at http://sourceforge.net/projects/nesmapper.

Understanding and Predicting Profile Structure and Parametric Scaling of Intrinsic Rotation

Science.gov (United States)

Wang, Weixing

2016-10-01

It is shown for the first time that turbulence-driven residual Reynolds stress can account for both the shape and magnitude of the observed intrinsic toroidal rotation profile. Nonlinear, global gyrokinetic simulations using GTS of DIII-D ECH plasmas indicate a substantial ITG fluctuation-induced non-diffusive momentum flux generated around a mid-radius-peaked intrinsic toroidal rotation profile. The non-diffusive momentum flux is dominated by the residual stress with a negligible contribution from the momentum pinch. The residual stress profile shows a robust anti-gradient, dipole structure in a set of ECH discharges with varying ECH power. Such interesting features of non-diffusive momentum fluxes, in connection with edge momentum sources and sinks, are found to be critical to drive the non-monotonic core rotation profiles in the experiments. Both turbulence intensity gradient and zonal flow ExB shear are identified as major contributors to the generation of the k∥-asymmetry needed for the residual stress generation. By balancing the residual stress and the momentum diffusion, a self-organized, steady-state rotation profile is calculated. The predicted core rotation profiles agree well with the experimentally measured main-ion toroidal rotation. The validated model is further used to investigate the characteristic dependence of global rotation profile structure in the multi-dimensional parametric space covering turbulence type, q-profile structure and collisionality with the goal of developing physics understanding needed for rotation profile control and optimization. Interesting results obtained include intrinsic rotation reversal induced by ITG-TEM transition in flat-q profile regime and by change in q-profile from weak to normal shear.. Fluctuation-generated poloidal Reynolds stress is also shown to significantly modify the neoclassical poloidal rotation in a way consistent with experimental observations. Finally, the first-principles-based model is applied

Comparisons of Crosswind Velocity Profile Estimates Used in Fast-Time Wake Vortex Prediction Models

Science.gov (United States)

Pruis, Mathew J.; Delisi, Donald P.; Ahmad, Nashat N.

2011-01-01

Five methods for estimating crosswind profiles used in fast-time wake vortex prediction models are compared in this study. Previous investigations have shown that temporal and spatial variations in the crosswind vertical profile have a large impact on the transport and time evolution of the trailing vortex pair. The most important crosswind parameters are the magnitude of the crosswind and the gradient in the crosswind shear. It is known that pulsed and continuous wave lidar measurements can provide good estimates of the wind profile in the vicinity of airports. In this study comparisons are made between estimates of the crosswind profiles from a priori information on the trajectory of the vortex pair as well as crosswind profiles derived from different sensors and a regional numerical weather prediction model.

Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

Science.gov (United States)

Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

2017-09-01

Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Validation of predicted exponential concentration profiles of chemicals in soils

International Nuclear Information System (INIS)

Hollander, Anne; Baijens, Iris; Ragas, Ad; Huijbregts, Mark; Meent, Dik van de

2007-01-01

Multimedia mass balance models assume well-mixed homogeneous compartments. Particularly for soils, this does not correspond to reality, which results in potentially large uncertainties in estimates of transport fluxes from soils. A theoretically expected exponential decrease model of chemical concentrations with depth has been proposed, but hardly tested against empirical data. In this paper, we explored the correspondence between theoretically predicted soil concentration profiles and 84 field measured profiles. In most cases, chemical concentrations in soils appear to decline exponentially with depth, and values for the chemical specific soil penetration depth (d p ) are predicted within one order of magnitude. Over all, the reliability of multimedia models will improve when they account for depth-dependent soil concentrations, so we recommend to take into account the described theoretical exponential decrease model of chemical concentrations with depth in chemical fate studies. In this model the d p -values should estimated be either based on local conditions or on a fixed d p -value, which we recommend to be 10 cm for chemicals with a log K ow > 3. - Multimedia mass model predictions will improve when taking into account depth dependent soil concentrations

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

Science.gov (United States)

Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

2017-12-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

Response-predictive gene expression profiling of glioma progenitor cells in vitro.

Directory of Open Access Journals (Sweden)

Sylvia Moeckel

Full Text Available High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist.In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a to enrich specimens for brain tumor initiating cells and (b to confront cells with a therapeutic agent before expression profiling.As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro.For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.

Predicting Post-Editor Profiles from the Translation Process

DEFF Research Database (Denmark)

Singla, Karan; Orrego-Carmona, David; Gonzales, Ashleigh Rhea

2014-01-01

The purpose of the current investigation is to predict post-editor profiles based on user behaviour and demographics using machine learning techniques to gain a better understanding of post-editor styles. Our study extracts process unit features from the CasMaCat LS14 database from the CRITT...... of translation process features. The classification and clustering of participants resulting from our study suggest this type of exploration could be used as a tool to develop new translation tool features or customization possibilities....

Prediction of Clinically Relevant Safety Signals of Nephrotoxicity through Plasma Metabolite Profiling

Directory of Open Access Journals (Sweden)

W. B. Mattes

2013-01-01

Full Text Available Addressing safety concerns such as drug-induced kidney injury (DIKI early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC. The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound’s well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity, not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.

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

Science.gov (United States)

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.

Science.gov (United States)

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 .

A predictable Java profile

DEFF Research Database (Denmark)

Bøgholm, Thomas; Hansen, Rene Rydhof; Ravn, Anders Peter

2009-01-01

A Java profile suitable for development of high integrity embedded systems is presented. It is based on event handlers which are grouped in missions and equipped with respectively private handler memory and shared mission memory. This is a result of our previous work on developing a Java profile......, and is directly inspired by interactions with the Open Group on their on-going work on a safety critical Java profile (JSR-302). The main contribution is an arrangement of the class hierarchy such that the proposal is a generalization of Real-Time Specification for Java (RTSJ). A further contribution...

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

Science.gov (United States)

Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

2018-04-15

Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen

Prediction of Process-Induced Distortions in L-Shaped Composite Profiles Using Path-Dependent Constitutive Law

Science.gov (United States)

Ding, Anxin; Li, Shuxin; Wang, Jihui; Ni, Aiqing; Sun, Liangliang; Chang, Lei

2016-10-01

In this paper, the corner spring-in angles of AS4/8552 L-shaped composite profiles with different thicknesses are predicted using path-dependent constitutive law with the consideration of material properties variation due to phase change during curing. The prediction accuracy mainly depends on the properties in the rubbery and glassy states obtained by homogenization method rather than experimental measurements. Both analytical and finite element (FE) homogenization methods are applied to predict the overall properties of AS4/8552 composite. The effect of fiber volume fraction on the properties is investigated for both rubbery and glassy states using both methods. And the predicted results are compared with experimental measurements for the glassy state. Good agreement is achieved between the predicted results and available experimental data, showing the reliability of the homogenization method. Furthermore, the corner spring-in angles of L-shaped composite profiles are measured experimentally and the reliability of path-dependent constitutive law is validated as well as the properties prediction by FE homogenization method.

Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

Science.gov (United States)

Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

2013-01-01

The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors

Science.gov (United States)

2017-02-01

affecting the function of Fanconi Anemia (FA) genes ( FANCA /B/C/D2/E/F/G/I/J/L/M, PALB2) or DNA damage response genes involved in HR 5 (ATM, ATR...Award Number: W81XWH-10-1-0585 TITLE: A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors...To) 15 July 2010 – 2 Nov.2016 4. TITLE AND SUBTITLE A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP

Chemogenomic analysis of G-protein coupled receptors and their ligands deciphers locks and keys governing diverse aspects of signalling.

Directory of Open Access Journals (Sweden)

Jörg D Wichard

Full Text Available Understanding the molecular mechanism of signalling in the important super-family of G-protein-coupled receptors (GPCRs is causally related to questions of how and where these receptors can be activated or inhibited. In this context, it is of great interest to unravel the common molecular features of GPCRs as well as those related to an active or inactive state or to subtype specific G-protein coupling. In our underlying chemogenomics study, we analyse for the first time the statistical link between the properties of G-protein-coupled receptors and GPCR ligands. The technique of mutual information (MI is able to reveal statistical inter-dependence between variations in amino acid residues on the one hand and variations in ligand molecular descriptors on the other. Although this MI analysis uses novel information that differs from the results of known site-directed mutagenesis studies or published GPCR crystal structures, the method is capable of identifying the well-known common ligand binding region of GPCRs between the upper part of the seven transmembrane helices and the second extracellular loop. The analysis shows amino acid positions that are sensitive to either stimulating (agonistic or inhibitory (antagonistic ligand effects or both. It appears that amino acid positions for antagonistic and agonistic effects are both concentrated around the extracellular region, but selective agonistic effects are cumulated between transmembrane helices (TMHs 2, 3, and ECL2, while selective residues for antagonistic effects are located at the top of helices 5 and 6. Above all, the MI analysis provides detailed indications about amino acids located in the transmembrane region of these receptors that determine G-protein signalling pathway preferences.

Behavioral Profile Predicts Dominance Status in Mountain Chickadees.

Science.gov (United States)

Fox, Rebecca A; Ladage, Lara D; Roth, Timothy C; Pravosudov, Vladimir V

2009-06-01

Individual variation in stable behavioral traits may explain variation in ecologically-relevant behaviors such as foraging, dispersal, anti-predator behavior, and dominance. We investigated behavioral variation in mountain chickadees (Poecile gambeli), a North American parid that lives in dominance-structured winter flocks, using two common measures of behavioral profile: exploration of a novel room and novel object exploration. We related those behavioral traits to dominance status in male chickadees following brief, pair-wise encounters. Low-exploring birds (birds that visited less than four locations in the novel room) were significantly more likely to become dominant in brief, pairwise encounters with high-exploring birds (i.e., birds that visited all perching locations within a novel room). On the other hand, there was no relationship between novel object exploration and dominance. Interestingly, novel room exploration was also not correlated with novel object exploration. These results suggest that behavioral profile may predict the social status of group-living individuals. Moreover, our results contradict the idea that novel object exploration and novel room exploration are always interchangeable measures of individuals' sensitivity to environmental novelty.

Periodontal profile classes predict periodontal disease progression and tooth loss.

Science.gov (United States)

Morelli, Thiago; Moss, Kevin L; Preisser, John S; Beck, James D; Divaris, Kimon; Wu, Di; Offenbacher, Steven

2018-02-01

Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived

Longitudinal prediction and concurrent functioning of adolescent girls demonstrating various profiles of dating violence and victimization.

Science.gov (United States)

Chiodo, Debbie; Crooks, Claire V; Wolfe, David A; McIsaac, Caroline; Hughes, Ray; Jaffe, Peter G

2012-08-01

Adolescent girls are involved in physical dating violence as both perpetrators and victims, and there are negative consequences associated with each of these behaviors. This article used a prospective design with 519 girls dating in grade 9 to predict profiles of dating violence in grade 11 based on relationships with families of origin (child maltreatment experiences, harsh parenting), and peers (harassment, delinquency, relational aggression). In addition, dating violence profiles were compared on numerous indices of adjustment (school connectedness, grades, self-efficacy and community connectedness) and maladjustment (suicide attempts, distress, delinquency, sexual behavior) for descriptive purposes. The most common profile was no dating violence (n = 367) followed by mutual violence (n = 81). Smaller numbers of girls reported victimization or perpetration only (ns = 39 and 32, respectively). Predicting grade 11 dating violence profile membership from grade 9 relationships was limited, although delinquency, parental rejection, and sexual harassment perpetration predicted membership to the mutually violent group, and delinquency predicted the perpetrator-only group. Compared to the non-violent group, the mutually violent girls in grade 11 had lower grades, poorer self-efficacy, and lower school connectedness and community involvement. Furthermore, they had higher rates of peer aggression and delinquency, were less likely to use condoms and were much more likely to have considered suicide. There were fewer differences among the profiles for girls involved with dating violence. In addition, the victims-only group reported higher rates of sexual intercourse, comparable to the mutually violent group and those involved in nonviolent relationships. Implications for prevention and intervention are highlighted.

Predicting lower third molar eruption on panoramic radiographs after cephalometric comparison of profile and panoramic radiographs

DEFF Research Database (Denmark)

Begtrup, Anders; Grønastøð, Halldis Á; Christensen, Ib Jarle

2012-01-01

and to find a simple and reliable method for predicting the eruption of the mandibular third molar by measurements on panoramic radiographs. The material consisted of profile and panoramic radiographs, taken before orthodontic treatment, of 30 males and 23 females (median age 22, range 18-48 years......Previous studies have suggested methods for predicting third molar tooth eruption radiographically. Still, this prediction is associated with uncertainty. The aim of the present study was to elucidate the association between cephalometric measurements on profile and panoramic radiographs...... the length from the ramus to the incisors (olr-id) showed a statistically significant correlation. By combining this length with the mesiodistal width of the lower second molar, the prediction of eruption of the lower third molar was strengthened. A new formula for calculating the probability of eruption...

Slurry discharge management-beach profile prediction

Energy Technology Data Exchange (ETDEWEB)

Bravo, R.; Nawrot, J.R. [Southern Illinois University at Carbondale, Carbondale, IL (United States). Dept. of Civil Engineering

1996-11-01

Mine tailings dams are embankments used by the mining industry to retain the tailings products after the mineral preparation process. Based on the acid-waste stereotype that all coal slurry is acid producing, current reclamation requires a four foot soil cover for inactive slurry disposal areas. Compliance with this requirement is both difficult and costly and in some case unnecessary, as not all the slurry, or portions of slurry impoundments are acid producing. Reduced costs and recent popularity of wetland development has prompted many operators to request reclamation variances for slurry impoundments. Waiting to address slurry reclamation until after the impoundment is full, limits the flexibility of reclamation opportunities. This paper outlines a general methodology to predict the formation of the beach profile for mine tailings dams, by the discharge volume and location of the slurry into the impoundment. The review is presented under the perspective of geotechnical engineering and waste disposal management emphasizing the importance of pre-planning slurry disposal land reclamation. 4 refs., 5 figs.

Body composition indices and predicted cardiovascular disease risk profile among urban dwellers in Malaysia.

Science.gov (United States)

Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Dahlui, Maznah; Majid, Hazreen Abdul

2015-01-01

This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD) risk profile in an urban population in Kuala Lumpur, Malaysia. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

Body Composition Indices and Predicted Cardiovascular Disease Risk Profile among Urban Dwellers in Malaysia

Directory of Open Access Journals (Sweden)

Tin Tin Su

2015-01-01

Full Text Available Objectives. This study aims to compare various body composition indices and their association with a predicted cardiovascular disease (CVD risk profile in an urban population in Kuala Lumpur, Malaysia. Methods. A cross-sectional survey was conducted in metropolitan Kuala Lumpur, Malaysia, in 2012. Households were selected using a simple random-sampling method, and adult members were invited for medical screening. The Framingham Risk Scoring algorithm was used to predict CVD risk, which was then analyzed in association with body composition measurements, including waist circumference, waist-hip ratio, waist-height ratio, body fat percentage, and body mass index. Results. Altogether, 882 individuals were included in our analyses. Indices that included waist-related measurements had the strongest association with CVD risk in both genders. After adjusting for demographic and socioeconomic variables, waist-related measurements retained the strongest correlations with predicted CVD risk in males. However, body mass index, waist-height ratio, and waist circumference had the strongest correlation with CVD risk in females. Conclusions. The waist-related indicators of abdominal obesity are important components of CVD risk profiles. As waist-related parameters can quickly and easily be measured, they should be routinely obtained in primary care settings and population health screens in order to assess future CVD risk profiles and design appropriate interventions.

Validity of a Manual Soft Tissue Profile Prediction Method Following Mandibular Setback Osteotomy

OpenAIRE

Kolokitha, Olga-Elpis

2007-01-01

Objectives The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. Methods To test the validity of the manu...

Effects of DTM resolution on slope steepness and soil loss prediction on hillslope profiles

Science.gov (United States)

Eder Paulo Moreira; William J. Elliot; Andrew T. Hudak

2011-01-01

Topographic attributes play a critical role in predicting erosion in models such as the Water Erosion Prediction Project (WEPP). The effects of four different high resolution hillslope profiles were studied using four different DTM resolutions: 1-m, 3-m, 5-m and 10-m. The WEPP model used a common scenario encountered in the forest environment and the selected hillslope...

Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study

Science.gov (United States)

Mathew, J.; Moat, R. J.; Paddea, S.; Francis, J. A.; Fitzpatrick, M. E.; Bouchard, P. J.

2017-12-01

Economic and safe management of nuclear plant components relies on accurate prediction of welding-induced residual stresses. In this study, the distribution of residual stress through the thickness of austenitic stainless steel welds has been measured using neutron diffraction and the contour method. The measured data are used to validate residual stress profiles predicted by an artificial neural network approach (ANN) as a function of welding heat input and geometry. Maximum tensile stresses with magnitude close to the yield strength of the material were observed near the weld cap in both axial and hoop direction of the welds. Significant scatter of more than 200 MPa was found within the residual stress measurements at the weld center line and are associated with the geometry and welding conditions of individual weld passes. The ANN prediction is developed in an attempt to effectively quantify this phenomenon of `innate scatter' and to learn the non-linear patterns in the weld residual stress profiles. Furthermore, the efficacy of the ANN method for defining through-thickness residual stress profiles in welds for application in structural integrity assessments is evaluated.

Leading-Edge Noise Prediction of General Airfoil Profiles with Spanwise-Varying Inflow Conditions

NARCIS (Netherlands)

Miotto, Renato Fuzaro; Wolf, William Roberto; De Santana, Leandro Dantas

2018-01-01

This paper presents a study of the leading-edge noise radiated by an airfoil undergoing a turbulent inflow. The noise prediction of generic airfoil profiles subjected to spanwise-varying inflow conditions is performed with the support of Amiet’s theory and the inverse strip technique. In the

Leading-Edge Noise Prediction of General Airfoil Profiles with Spanwise-Varying Inflow Conditions

NARCIS (Netherlands)

Miotto, Renato Fuzaro; Wolf, William Roberto; De Santana, Leandro Dantas

This paper presents a study of the leading-edge noise radiated by an airfoil undergoing a turbulent inflow. The noise prediction of generic airfoil profiles subjected to spanwise-varying inflow conditions is performed with the support of Amiet’s theory and the inverse strip technique. In the

Modeling and prediction of extraction profile for microwave-assisted extraction based on absorbed microwave energy.

Science.gov (United States)

Chan, Chung-Hung; Yusoff, Rozita; Ngoh, Gek-Cheng

2013-09-01

A modeling technique based on absorbed microwave energy was proposed to model microwave-assisted extraction (MAE) of antioxidant compounds from cocoa (Theobroma cacao L.) leaves. By adapting suitable extraction model at the basis of microwave energy absorbed during extraction, the model can be developed to predict extraction profile of MAE at various microwave irradiation power (100-600 W) and solvent loading (100-300 ml). Verification with experimental data confirmed that the prediction was accurate in capturing the extraction profile of MAE (R-square value greater than 0.87). Besides, the predicted yields from the model showed good agreement with the experimental results with less than 10% deviation observed. Furthermore, suitable extraction times to ensure high extraction yield at various MAE conditions can be estimated based on absorbed microwave energy. The estimation is feasible as more than 85% of active compounds can be extracted when compared with the conventional extraction technique. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Impact of Mission Profile Models on the Predicted Lifetime of IGBT Modules in the Modular Multilevel Converter

DEFF Research Database (Denmark)

Zhang, Yi; Wang, Huai; Wang, Zhongxu

2017-01-01

and electrical power modeling methods on the estimated lifetime of IGBT modules in an MMC for offshore wind power application. In a 30 MW MMC case study, an annual wind speed profile with a resolution of 1 s/data, 10 minute/data, and 1 hour/data are considered, respectively. A method to re-generate higher......The reliability aspect study of Modular Multilevel Converter (MMC) is of great interest in industry applications, such as offshore wind. Lifetime prediction of key components is an important tool to design MMC with fulfilled reliability specifications. While many efforts have been made...... to the lifetime prediction of IGBT modules in renewable energy applications by considering long-term varying operation conditions (i.e., mission profile), the justifications of using the associated mission profiles are still missed. This paper investigates the impact of mission profile data resolutions...

Prediction of fracture profile using digital image correlation

Science.gov (United States)

Chaitanya, G. M. S. K.; Sasi, B.; Kumar, Anish; Babu Rao, C.; Purnachandra Rao, B.; Jayakumar, T.

2015-04-01

Digital Image Correlation (DIC) based full field strain mapping methodology is used for mapping strain on an aluminum sample subjected to tensile deformation. The local strains on the surface of the specimen are calculated at different strain intervals. Early localization of strain is observed at a total strain of 0.050ɛ; itself, whereas a visually apparent localization of strain is observed at a total strain of 0.088ɛ;. Orientation of the line of fracture (12.0°) is very close to the orientation of locus of strain maxima (11.6°) computed from the strain mapping at 0.063ɛ itself. These results show the efficacy of the DIC based method to predict the location as well as the profile of the fracture, at an early stage.

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling.

Science.gov (United States)

Wen, Shi; Zhan, Bohan; Feng, Jianghua; Hu, Weize; Lin, Xianchao; Bai, Jianxi; Huang, Heguang

2017-11-02

The differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy. By implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1 H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight. An obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation. The NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

Science.gov (United States)

Razmara, Jafar; Zaboli, Mohammad Hassan; Hassankhani, Hadi

2016-11-01

Falls play a critical role in older people's life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively. Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls.

Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy.

Science.gov (United States)

Honeyborne, Isobella; McHugh, Timothy D; Kuittinen, Iitu; Cichonska, Anna; Evangelopoulos, Dimitrios; Ronacher, Katharina; van Helden, Paul D; Gillespie, Stephen H; Fernandez-Reyes, Delmiro; Walzl, Gerhard; Rousu, Juho; Butcher, Philip D; Waddell, Simon J

2016-04-07

New treatment options are needed to maintain and improve therapy for tuberculosis, which caused the death of 1.5 million people in 2013 despite potential for an 86 % treatment success rate. A greater understanding of Mycobacterium tuberculosis (M.tb) bacilli that persist through drug therapy will aid drug development programs. Predictive biomarkers for treatment efficacy are also a research priority. Genome-wide transcriptional profiling was used to map the mRNA signatures of M.tb from the sputa of 15 patients before and 3, 7 and 14 days after the start of standard regimen drug treatment. The mRNA profiles of bacilli through the first 2 weeks of therapy reflected drug activity at 3 days with transcriptional signatures at days 7 and 14 consistent with reduced M.tb metabolic activity similar to the profile of pre-chemotherapy bacilli. These results suggest that a pre-existing drug-tolerant M.tb population dominates sputum before and after early drug treatment, and that the mRNA signature at day 3 marks the killing of a drug-sensitive sub-population of bacilli. Modelling patient indices of disease severity with bacterial gene expression patterns demonstrated that both microbiological and clinical parameters were reflected in the divergent M.tb responses and provided evidence that factors such as bacterial load and disease pathology influence the host-pathogen interplay and the phenotypic state of bacilli. Transcriptional signatures were also defined that predicted measures of early treatment success (rate of decline in bacterial load over 3 days, TB test positivity at 2 months, and bacterial load at 2 months). This study defines the transcriptional signature of M.tb bacilli that have been expectorated in sputum after two weeks of drug therapy, characterizing the phenotypic state of bacilli that persist through treatment. We demonstrate that variability in clinical manifestations of disease are detectable in bacterial sputa signatures, and that the changing M.tb m

HPV and high-risk gene expression profiles predict response to chemoradiotherapy in head and neck cancer, independent of clinical factors

International Nuclear Information System (INIS)

Jong, Monique C. de; Pramana, Jimmy; Knegjens, Joost L.; Balm, Alfons J.M.; Brekel, Michiel W.M. van den; Hauptmann, Michael; Begg, Adrian C.; Rasch, Coen R.N.

2010-01-01

Purpose: The purpose of this study was to combine gene expression profiles and clinical factors to provide a better prediction model of local control after chemoradiotherapy for advanced head and neck cancer. Material and methods: Gene expression data were available for a series of 92 advanced stage head and neck cancer patients treated with primary chemoradiotherapy. The effect of the Chung high-risk and Slebos HPV expression profiles on local control was analyzed in a model with age at diagnosis, gender, tumor site, tumor volume, T-stage and N-stage and HPV profile status. Results: Among 75 patients included in the study, the only factors significantly predicting local control were tumor site (oral cavity vs. Pharynx, hazard ratio 4.2 [95% CI 1.4-12.5]), Chung gene expression status (high vs. Low risk profile, hazard ratio 4.4 [95% CI 1.5-13.3]) and HPV profile (negative vs. Positive profile, hazard ratio 6.2 [95% CI 1.7-22.5]). Conclusions: Chung high-risk expression profile and a negative HPV expression profile were significantly associated with increased risk of local recurrence after chemoradiotherapy in advanced pharynx and oral cavity tumors, independent of clinical factors.

Acylcarnitines profile best predicts survival in horses with atypical myopathy.

Directory of Open Access Journals (Sweden)

François Boemer

Full Text Available Equine atypical myopathy (AM is caused by hypoglycin A intoxication and is characterized by a high fatality rate. Predictive estimation of survival in AM horses is necessary to prevent unnecessary suffering of animals that are unlikely to survive and to focus supportive therapy on horses with a possible favourable prognosis of survival. We hypothesized that outcome may be predicted early in the course of disease based on the assumption that the acylcarnitine profile reflects the derangement of muscle energetics. We developed a statistical model to prognosticate the risk of death of diseased animals and found that estimation of outcome may be drawn from three acylcarnitines (C2, C10:2 and C18 -carnitines with a high sensitivity and specificity. The calculation of the prognosis of survival makes it possible to distinguish the horses that will survive from those that will die despite severe signs of acute rhabdomyolysis in both groups.

Acylcarnitines profile best predicts survival in horses with atypical myopathy

Science.gov (United States)

Detilleux, Johann; Cello, Christophe; Amory, Hélène; Marcillaud-Pitel, Christel; Richard, Eric; van Galen, Gaby; van Loon, Gunther; Lefère, Laurence; Votion, Dominique-Marie

2017-01-01

Equine atypical myopathy (AM) is caused by hypoglycin A intoxication and is characterized by a high fatality rate. Predictive estimation of survival in AM horses is necessary to prevent unnecessary suffering of animals that are unlikely to survive and to focus supportive therapy on horses with a possible favourable prognosis of survival. We hypothesized that outcome may be predicted early in the course of disease based on the assumption that the acylcarnitine profile reflects the derangement of muscle energetics. We developed a statistical model to prognosticate the risk of death of diseased animals and found that estimation of outcome may be drawn from three acylcarnitines (C2, C10:2 and C18 -carnitines) with a high sensitivity and specificity. The calculation of the prognosis of survival makes it possible to distinguish the horses that will survive from those that will die despite severe signs of acute rhabdomyolysis in both groups. PMID:28846683

Profile control simulations and experiments on TCV : A controller test environment and results using a model-based predictive controller

NARCIS (Netherlands)

Maljaars, E.; Felici, F.; Blanken, T.C.; Galperti, C.; Sauter, O.; de Baar, M.R.; Carpanese, F.; Goodman, T.P.; Kim, D.; Kim, S.H.; Kong, M.G.; Mavkov, B.; Merle, A.; Moret, J.M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.A.; Vu, N.M.T.

2017-01-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

NARCIS (Netherlands)

Maljaars, B.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.; Vu, T.

2017-01-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

Identification of drug targets by chemogenomic and metabolomic profiling in yeast

KAUST Repository

Wu, Manhong; Zheng, Ming; Zhang, Weiruo; Suresh, Sundari; Schlecht, Ulrich; Fitch, William L.; Aronova, Sofia; Baumann, Stephan; Davis, Ronald; St.Onge, Robert; Dill, David L.; Peltz, Gary

2012-01-01

OBJECTIVE: To advance our understanding of disease biology, the characterization of the molecular target for clinically proven or new drugs is very important. Because of its simplicity and the availability of strains with individual deletions in all

Predicting multi-class customer profiles based on transactions : a case study in food sales

NARCIS (Netherlands)

Apeh, E.; Zliobaite, I.; Pechenizkiy, M.; Gabrys, B.; Bramer, M.; Petridis, M.

2012-01-01

Predicting the class of customer profiles is a key task in marketing, which enables businesses to approach the customers in a right way to satisfy the customer’s evolving needs. However, due to costs, privacy and/or data protection, only the business’ owned transactional data is typically available

Relationship between the prognostic and predictive value of the intrinsic subtypes and a validated gene profile predictive of loco-regional control and benefit from post-mastectomy radiotherapy in patients with high-risk breast cancer

DEFF Research Database (Denmark)

Tramm, Trine; Kyndi, Marianne; Myhre, Simen

2014-01-01

, and has shown prognostic impact in terms of loco-regional failure and predictive impact for PMRT. Reports have also shown predictive value in terms of benefit of PMRT from intrinsic subtypes and derived approximations. The aim of this study was to examine: 1) the agreement between various methods...... for determining the intrinsic subtypes; and 2) the relationship between the prognostic and predictive impact of the DBCG-RT profile and the intrinsic subtypes. MATERIAL AND METHODS: Intrinsic subtypes and the DBCG-RT profile was determined from microarray analysis based on fresh frozen tissue from 191 patients...... and predictive information obtained from the DBCG-RT profile cannot be substituted by any approximation of the tumors intrinsic subtype. The predictive value of the intrinsic subtypes in terms of PMRT was influenced by the method used for assignment to the intrinsic subtypes....

In-flight measurements and RCS-predictions: A comparison on broad-side radar range profiles of a Boeing 737

NARCIS (Netherlands)

Heiden, R. van der; Ewijk, L.J. van; Groen, F.C.A.

1997-01-01

The validation of Radar Cross Section (RCS) prediction techniques against real measurements is crucial to acquire confidence in predictions when measurements are not available. In this paper we present the first results of a comparison on one dimensional images, i.e., radar range profiles. The

The prediction of concentration profiles for a NIMCIX column absorbing uranium from aqueous solution

International Nuclear Information System (INIS)

Wright, R.S.

1979-01-01

A procedure is proposed for the prediction of concentration profiles for a countercurrent ion-exchange absorption column, use being made of equilibrium and kinetic data derived from small-scale batch tests. A comparison is presented between the predictions and the measured performance of a column (2,5 m in diameter) absorbing uranium from solution. The method is shown to be adequate for design purposes provided that the data used are from tests in which the solution and resin conditions approximate those for which the plant is being designed [af

A statistical approach for predicting thermal diffusivity profiles in fusion plasmas as a transport model

International Nuclear Information System (INIS)

Yokoyama, Masayuki

2014-01-01

A statistical approach is proposed to predict thermal diffusivity profiles as a transport “model” in fusion plasmas. It can provide regression expressions for the ion and electron heat diffusivities (χ i and χ e ), separately, to construct their radial profiles. An approach that this letter is proposing outstrips the conventional scaling laws for the global confinement time (τ E ) since it also deals with profiles (temperature, density, heating depositions etc.). This approach has become possible with the analysis database accumulated by the extensive application of the integrated transport analysis suite to experiment data. In this letter, TASK3D-a analysis database for high-ion-temperature (high-T i ) plasmas in the LHD (Large Helical Device) is used as an example to describe an approach. (author)

Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

Directory of Open Access Journals (Sweden)

Banjanovic-Mehmedovic Lejla

2016-01-01

Full Text Available Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS, since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS. The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error, coefficient of determination (R2 and Pearson coefficient (r, were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes.

Directory of Open Access Journals (Sweden)

Shinji Kume

Full Text Available Prevention of cardiovascular disease (CVD is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke. Using the PFAA profiles and clinical information, an index (CVD-AI consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI: 0.64-0.79] showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62-0.77] on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57-5.19]. This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria.

Predictive Properties of Plasma Amino Acid Profile for Cardiovascular Disease in Patients with Type 2 Diabetes

Science.gov (United States)

Kume, Shinji; Araki, Shin-ichi; Ono, Nobukazu; Shinhara, Atsuko; Muramatsu, Takahiko; Araki, Hisazumi; Isshiki, Keiji; Nakamura, Kazuki; Miyano, Hiroshi; Koya, Daisuke; Haneda, Masakazu; Ugi, Satoshi; Kawai, Hiromichi; Kashiwagi, Atsunori; Uzu, Takashi; Maegawa, Hiroshi

2014-01-01

Prevention of cardiovascular disease (CVD) is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke). Using the PFAA profiles and clinical information, an index (CVD-AI) consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI): 0.64–0.79]) showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62–0.77]) on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57–5.19]). This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria. PMID:24971671

ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles.

Science.gov (United States)

Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G; Gelly, Jean-Christophe

2016-06-20

Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation -with Protein Blocks-, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the 'Hard' category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/.

Predicting fiber refractive index from a measured preform index profile

Science.gov (United States)

Kiiveri, P.; Koponen, J.; Harra, J.; Novotny, S.; Husu, H.; Ihalainen, H.; Kokki, T.; Aallos, V.; Kimmelma, O.; Paul, J.

2018-02-01

When producing fiber lasers and amplifiers, silica glass compositions consisting of three to six different materials are needed. Due to the varying needs of different applications, substantial number of different glass compositions are used in the active fiber structures. Often it is not possible to find material parameters for theoretical models to estimate thermal and mechanical properties of those glass compositions. This makes it challenging to predict accurately fiber core refractive index values, even if the preform index profile is measured. Usually the desired fiber refractive index value is achieved experimentally, which is expensive. To overcome this problem, we analyzed statistically the changes between the measured preform and fiber index values. We searched for correlations that would help to predict the Δn-value change from preform to fiber in a situation where we don't know the values of the glass material parameters that define the change. Our index change models were built using the data collected from preforms and fibers made by the Direct Nanoparticle Deposition (DND) technology.

The Reliability and Predictive Validity of the Stalking Risk Profile.

Science.gov (United States)

McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

2018-03-01

This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

Prediction of lymphatic metastasis based on gene expression profile analysis after brachytherapy for early-stage oral tongue carcinoma

International Nuclear Information System (INIS)

Watanabe, Hiroshi; Mogushi, Kaoru; Miura, Masahiko; Yoshimura, Ryo-ichi; Kurabayashi, Tohru; Shibuya, Hitoshi; Tanaka, Hiroshi; Noda, Shuhei; Iwakawa, Mayumi; Imai, Takashi

2008-01-01

Background and purpose: The management of lymphatic metastasis of early-stage oral tongue carcinoma patients is crucial for its prognosis. The purpose of this study was to evaluate the predictive ability of lymphatic metastasis after brachytherapy (BRT) for early-stage tongue carcinoma based on gene expression profiling. Patients and methods: Pre-therapeutic biopsies from 39 patients with T1 or T2 tongue cancer were analyzed for gene expression signatures using Codelink Uniset Human 20K Bioarray. All patients were treated with low dose-rate BRT for their primary lesions and underwent strict follow-up under a wait-and-see policy for cervical lymphatic metastasis. Candidate genes were selected for predicting lymph-node status in the reference group by the permutation test. Predictive accuracy was further evaluated by the prediction strength (PS) scoring system using an independent validation group. Results: We selected a set of 19 genes whose expression differed significantly between classes with or without lymphatic metastasis in the reference group. The lymph-node status in the validation group was predicted by the PS scoring system with an accuracy of 76%. Conclusions: Gene expression profiling using 19 genes in primary tumor tissues may allow prediction of lymphatic metastasis after BRT for early-stage oral tongue carcinoma

Predicting the profile of nutrients available for absorption: from nutrient requirement to animal response and environmental impact.

Science.gov (United States)

Dijkstra, J; Kebreab, E; Mills, J A N; Pellikaan, W F; López, S; Bannink, A; France, J

2007-02-01

Current feed evaluation systems for dairy cattle aim to match nutrient requirements with nutrient intake at pre-defined production levels. These systems were not developed to address, and are not suitable to predict, the responses to dietary changes in terms of production level and product composition, excretion of nutrients to the environment, and nutrition related disorders. The change from a requirement to a response system to meet the needs of various stakeholders requires prediction of the profile of absorbed nutrients and its subsequent utilisation for various purposes. This contribution examines the challenges to predicting the profile of nutrients available for absorption in dairy cattle and provides guidelines for further improved prediction with regard to animal production responses and environmental pollution.The profile of nutrients available for absorption comprises volatile fatty acids, long-chain fatty acids, amino acids and glucose. Thus the importance of processes in the reticulo-rumen is obvious. Much research into rumen fermentation is aimed at determination of substrate degradation rates. Quantitative knowledge on rates of passage of nutrients out of the rumen is rather limited compared with that on degradation rates, and thus should be an important theme in future research. Current systems largely ignore microbial metabolic variation, and extant mechanistic models of rumen fermentation give only limited attention to explicit representation of microbial metabolic activity. Recent molecular techniques indicate that knowledge on the presence and activity of various microbial species is far from complete. Such techniques may give a wealth of information, but to include such findings in systems predicting the nutrient profile requires close collaboration between molecular scientists and mathematical modellers on interpreting and evaluating quantitative data. Protozoal metabolism is of particular interest here given the paucity of quantitative data

Prediction of the Inlet Nozzle Velocity Profiles for the CANDU-6 Moderator Analysis

International Nuclear Information System (INIS)

Yoon, Churl; Park, Joo Hwan

2006-01-01

For the moderator analysis of the CANDU reactors in Korea, predicting local moderator subcooling in the Calandria vessels is one of the main concerns for the estimation of heat sink capability of moderator under LOCA transients. The moderator circulation pattern is determined by the combined forces of the inlet jet momentum and the buoyancy flow. Even though the inlet boundary condition plays an important role in determining the moderator circulations, no measured data of detailed inlet velocity profiles is available. The purpose of this study is to produce the velocity profiles at the inlet nozzles by a CFD simulation. To produce the velocity vector fields at the inlet nozzle surfaces, the internal flows in the nozzle assembly were simulated by using a commercial CFD code, CFX-5.7. In the reference, the analytical capability of CFX-5.7 had been estimated by a validation of the CFD code against available experimental data for separate flow phenomena. Various turbulence models and grid spacing had been also tested. In the following section, the interface treatment between the computational domains would be explained. In section 3, the inlet nozzle flow through the CANDU moderator nozzle assembly was predicted by using the obtained technology of the CFD simulation

Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.

Directory of Open Access Journals (Sweden)

Camille Jeunet

Full Text Available Mental-Imagery based Brain-Computer Interfaces (MI-BCIs allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG, which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants' BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants' performance with a mean error of less than 3 points. This study determined how users' profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.

Prediction of incidence and stability of alcohol use disorders by latent internalizing psychopathology risk profiles in adolescence and young adulthood.

Science.gov (United States)

Behrendt, Silke; Bühringer, Gerhard; Höfler, Michael; Lieb, Roselind; Beesdo-Baum, Katja

2017-10-01

Comorbid internalizing mental disorders in alcohol use disorders (AUD) can be understood as putative independent risk factors for AUD or as expressions of underlying shared psychopathology vulnerabilities. However, it remains unclear whether: 1) specific latent internalizing psychopathology risk-profiles predict AUD-incidence and 2) specific latent internalizing comorbidity-profiles in AUD predict AUD-stability. To investigate baseline latent internalizing psychopathology risk profiles as predictors of subsequent AUD-incidence and -stability in adolescents and young adults. Data from the prospective-longitudinal EDSP study (baseline age 14-24 years) were used. The study-design included up to three follow-up assessments in up to ten years. DSM-IV mental disorders were assessed with the DIA-X/M-CIDI. To investigate risk-profiles and their associations with AUD-outcomes, latent class analysis with auxiliary outcome variables was applied. AUD-incidence: a 4-class model (N=1683) was identified (classes: normative-male [45.9%], normative-female [44.2%], internalizing [5.3%], nicotine dependence [4.5%]). Compared to the normative-female class, all other classes were associated with a higher risk of subsequent incident alcohol dependence (p<0.05). AUD-stability: a 3-class model (N=1940) was identified with only one class (11.6%) with high probabilities for baseline AUD. This class was further characterized by elevated substance use disorder (SUD) probabilities and predicted any subsequent AUD (OR 8.5, 95% CI 5.4-13.3). An internalizing vulnerability may constitute a pathway to AUD incidence in adolescence and young adulthood. In contrast, no indication for a role of internalizing comorbidity profiles in AUD-stability was found, which may indicate a limited importance of such profiles - in contrast to SUD-related profiles - in AUD stability. Copyright © 2017 Elsevier B.V. All rights reserved.

TEMA and Dot Enumeration Profiles Predict Mental Addition Problem Solving Speed Longitudinally.

Science.gov (United States)

Major, Clare S; Paul, Jacob M; Reeve, Robert A

2017-01-01

Different math indices can be used to assess math potential at school entry. We evaluated whether standardized math achievement (TEMA-2 performance), core number abilities (dot enumeration, symbolic magnitude comparison), non-verbal intelligence (NVIQ) and visuo-spatial working memory (VSWM), in combination or separately, predicted mental addition problem solving speed over time. We assessed 267 children's TEMA-2, magnitude comparison, dot enumeration, and VSWM abilities at school entry (5 years) and NVIQ at 8 years. Mental addition problem solving speed was assessed at 6, 8, and 10 years. Longitudinal path analysis supported a model in which dot enumeration performance ability profiles and previous mental addition speed predicted future mental addition speed on all occasions, supporting a componential account of math ability. Standardized math achievement and NVIQ predicted mental addition speed at specific time points, while VSWM and symbolic magnitude comparison did not contribute unique variance to the model. The implications of using standardized math achievement and dot enumeration ability to index math learning potential at school entry are discussed.

Technical player profiles related to the physical fitness of young female volleyball players predict team performance.

Science.gov (United States)

Dávila-Romero, C; Hernández-Mocholí, M A; García-Hermoso, A

2015-03-01

This study is divided into three sequential stages: identification of fitness and game performance profiles (individual player performance), an assessment of the relationship between these profiles, and an assessment of the relationship between individual player profiles and team performance during play (in championship performance). The overall study sample comprised 525 (19 teams) female volleyball players aged 12-16 years and a subsample (N.=43) used to examine study aims one and two was selected from overall sample. Anthropometric, fitness and individual player performance (actual game) data were collected in the subsample. These data were analyzed through clustering methods, ANOVA and independence chi-square test. Then, we investigated whether the proportion of players with the highest individual player performance profile might predict a team's results in the championship. Cluster analysis identified three volleyball fitness profiles (high, medium, and low) and two individual player performance profiles (high and low). The results showed a relationship between both types of profile (fitness and individual player performance). Then, linear regression revealed a moderate relationship between the number of players with a high volleyball fitness profile and a team's results in the championship (R2=0.23). The current study findings may enable coaches and trainers to manage training programs more efficiently in order to obtain tailor-made training, identify volleyball-specific physical fitness training requirements and reach better results during competitions.

Prediction of the hardness profile of an AISI 4340 steel cylinder heat-treated by laser - 3D and artificial neural networks modelling and experimental validation

Energy Technology Data Exchange (ETDEWEB)

Hadhri, Mahdi; Ouafi, Abderazzak El; Barka, Noureddine [University of Quebec, Rimouski (Canada)

2017-02-15

This paper presents a comprehensive approach developed to design an effective prediction model for hardness profile in laser surface transformation hardening process. Based on finite element method and Artificial neural networks, the proposed approach is built progressively by (i) examining the laser hardening parameters and conditions known to have an influence on the hardened surface attributes through a structured experimental investigation, (ii) investigating the laser hardening parameters effects on the hardness profile through extensive 3D modeling and simulation efforts and (ii) integrating the hardening process parameters via neural network model for hardness profile prediction. The experimental validation conducted on AISI4340 steel using a commercial 3 kW Nd:Yag laser, confirm the feasibility and efficiency of the proposed approach leading to an accurate and reliable hardness profile prediction model. With a maximum relative error of about 10 % under various practical conditions, the predictive model can be considered as effective especially in the case of a relatively complex system such as laser surface transformation hardening process.

Prediction of the hardness profile of an AISI 4340 steel cylinder heat-treated by laser - 3D and artificial neural networks modelling and experimental validation

International Nuclear Information System (INIS)

Hadhri, Mahdi; Ouafi, Abderazzak El; Barka, Noureddine

2017-01-01

This paper presents a comprehensive approach developed to design an effective prediction model for hardness profile in laser surface transformation hardening process. Based on finite element method and Artificial neural networks, the proposed approach is built progressively by (i) examining the laser hardening parameters and conditions known to have an influence on the hardened surface attributes through a structured experimental investigation, (ii) investigating the laser hardening parameters effects on the hardness profile through extensive 3D modeling and simulation efforts and (ii) integrating the hardening process parameters via neural network model for hardness profile prediction. The experimental validation conducted on AISI4340 steel using a commercial 3 kW Nd:Yag laser, confirm the feasibility and efficiency of the proposed approach leading to an accurate and reliable hardness profile prediction model. With a maximum relative error of about 10 % under various practical conditions, the predictive model can be considered as effective especially in the case of a relatively complex system such as laser surface transformation hardening process

Insulin Resistance Predicts Atherogenic Lipoprotein Profile in Nondiabetic Subjects

Directory of Open Access Journals (Sweden)

Flávia De C. Cartolano

2017-01-01

Full Text Available Background. Atherogenic diabetes is associated with an increased cardiovascular risk and mortality in diabetic individuals; however, the impact of insulin resistance (IR in lipid metabolism in preclinical stages is generally underreported. For that, we evaluated the capacity of IR to predict an atherogenic lipid subfraction profile. Methods. Complete clinical evaluation and biochemical analysis (lipid, glucose profile, LDL, and HDL subfractions and LDL phenotype and size were performed in 181 patients. The impact of IR as a predictor of atherogenic lipoproteins was tested by logistic regression analysis in raw and adjusted models. Results. HDL-C and Apo AI were significantly lower in individuals with IR. Individuals with IR had a higher percentage of small HDL particles, lower percentage in the larger ones, and reduced frequency of phenotype A (IR = 62%; non-IR = 83%. IR individuals had reduced probability to have large HDL (OR = 0.213; CI = 0.999–0.457 and had twice more chances to show increased small HDL (OR = 2.486; CI = 1.341–7.051. IR was a significant predictor of small LDL (OR = 3.075; CI = 1.341–7.051 and atherogenic phenotype (OR = 3.176; CI = 1.469–6.867. Conclusion. IR, previously DM2 diagnosis, is a strong predictor of quantitative and qualitative features of lipoproteins directly associated with an increased atherogenic risk.

A predictable Java profile - rationale and implementations

DEFF Research Database (Denmark)

Søndergaard, Hans; Bøgholm, Thomas; Hansen, Rene Rydhof

A Java profile suitable for development of high integrity embedded systems is presented. It is based on event handlers which are grouped in missions and equipped with respectively private handler memory and shared mission memory. This is a result of our previous work on developing a Java profile......, and is directly inspired by interactions with the Open Group on their on-going work on a safety critical Java profile (JSR-302). The main contribution is an arrangement of the class hierarchy such that the proposal is a generalization of Real-Time Specification for Java (RTSJ). A further contribution...

Predicting Recurrence and Progression of Noninvasive Papillary Bladder Cancer at Initial Presentation Based on Quantitative Gene Expression Profiles

DEFF Research Database (Denmark)

Birkhahn, M.; Mitra, A.P.; Williams, Johan

2010-01-01

% specificity. Since this is a small retrospective study using medium-throughput profiling, larger confirmatory studies are needed. Conclusions: Gene expression profiling across relevant cancer pathways appears to be a promising approach for Ta bladder tumor outcome prediction at initial diagnosis......Background: Currently, tumor grade is the best predictor of outcome at first presentation of noninvasive papillary (Ta) bladder cancer. However, reliable predictors of Ta tumor recurrence and progression for individual patients, which could optimize treatment and follow-up schedules based...... on specific tumor biology, are yet to be identified. Objective: To identify genes predictive for recurrence and progression in Ta bladder cancer at first presentation using a quantitative, pathway-specific approach. Design, setting, and participants: Retrospective study of patients with Ta G2/3 bladder tumors...

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness.

Science.gov (United States)

Verikas, Antanas; Vaiciukynas, Evaldas; Gelzinis, Adas; Parker, James; Olsson, M Charlotte

2016-04-23

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

Directory of Open Access Journals (Sweden)

Antanas Verikas

2016-04-01

Full Text Available This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each. The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG

Identifying and Predicting Profiles of Medical Noncompliance: Pediatric Caregivers' Antibiotic Stewardship.

Science.gov (United States)

Smith, Rachel A; Kim, Youllee; M'Ikanatha, Nkuchia M

2018-05-14

Sometimes compliance with medical recommendations is problematic. We investigated pediatric caregivers' (N = 606) patterns of noncompliance with antibiotic stewardship based on the obstacle hypothesis. We tested predictors of noncompliance framed by the obstacle hypothesis, dissonance theory, and psychological reactance. The results revealed four profiles of caregivers' stewardship: one marked by compliance (Stewards) and three marked by types of noncompliance (Stockers, Persuaders, and Dissenters). The covariate analysis showed that, although psychological reactance predicted being noncompliant, it was types of obstacles and discrepant experiences that predicted caregivers' patterns of noncompliance with antibiotic stewardship. Campaign planning often focuses on identifying the belief most associated with the targeted outcome, such as compliance. Noncompliance research, however, points out that persuaders may be successful to the extent to which they anticipate obstacles to compliance and address them in their influence attempts. A shift from medical noncompliance to patient engagement also affords an opportunity to consider how some recommendations create obstacles for others and to find positive ways to embrace conflicting needs, tensions, and reasons for refusal in order to promote collective goals.

How good are publicly available web services that predict bioactivity profiles for drug repurposing?

Science.gov (United States)

Murtazalieva, K A; Druzhilovskiy, D S; Goel, R K; Sastry, G N; Poroikov, V V

2017-10-01

Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.

Multigene expression profile for predicting efficacy of cisplatin and vinorelbine in non-small cell lung cancer

DEFF Research Database (Denmark)

Buhl, I. K.; Christensen, I. J.; Santoni-Rugiu, E.

2016-01-01

Background: There is a need for biomarkers to predict efficacy of adjuvant chemotherapy in resected non-small cell lung cancer (NSCLC). Presented is a combined cisplatin and vinorelbine marker from a previously validated model system [1] tested in two cohorts. Methods: The profiles consist...... and vinorelbine (ACT) and 62 patients who had no adjuvant treatment (OBS) [2] and 2) 95 stage Ib-IIIb completely resected NSCLC patients who all received adjuvant cisplatin and vinorelbine [3]. Endpoint is cancer specific survival. Results: The combined cisplatin and vinorelbine profiles scored as a continuous...... of correlated in vitro cytotoxicity of cisplatin and vinorelbine and mRNA expressions. Then each profile is correlated to mRNA expression of 3500 tumors. The cohorts are 1) a publically available dataset with 133 completely resected stage Ib-II NSCLC patients, 71 of whom received adjuvant cisplatin...

Hierarchical Status Predicts Behavioral Vulnerability and Nucleus Accumbens Metabolic Profile Following Chronic Social Defeat Stress.

Science.gov (United States)

Larrieu, Thomas; Cherix, Antoine; Duque, Aranzazu; Rodrigues, João; Lei, Hongxia; Gruetter, Rolf; Sandi, Carmen

2017-07-24

Extensive data highlight the existence of major differences in individuals' susceptibility to stress [1-4]. While genetic factors [5, 6] and exposure to early life stress [7, 8] are key components for such neurobehavioral diversity, intriguing observations revealed individual differences in response to stress in inbred mice [9-12]. This raised the possibility that other factors might be critical in stress vulnerability. A key challenge in the field is to identify non-invasively risk factors for vulnerability to stress. Here, we investigated whether behavioral factors, emerging from preexisting dominance hierarchies, could predict vulnerability to chronic stress [9, 13-16]. We applied a chronic social defeat stress (CSDS) model of depression in C57BL/6J mice to investigate the predictive power of hierarchical status to pinpoint which individuals will exhibit susceptibility to CSDS. Given that the high social status of dominant mice would be the one particularly challenged by CSDS, we predicted and found that dominant individuals were the ones showing a strong susceptibility profile as indicated by strong social avoidance following CSDS, while subordinate mice were not affected. Data from 1 H-NMR spectroscopy revealed that the metabolic profile in the nucleus accumbens (NAc) relates to social status and vulnerability to stress. Under basal conditions, subordinates show lower levels of energy-related metabolites compared to dominants. In subordinates, but not dominants, levels of these metabolites were increased after exposure to CSDS. To the best of our knowledge, this is the first study that identifies non-invasively the origin of behavioral risk factors predictive of stress-induced depression-like behaviors associated with metabolic changes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Vacuum-assisted breast biopsy of suspected mammographic breast diagnoses: predictive value of serum proteomic profile

International Nuclear Information System (INIS)

Schittulli, F.; Ventrella, V.

2009-01-01

The project planned a series of actions oriented to different scientific questions: to complete the prospective collection of serum samples for serum proteomic analysis according to SOPs needed for the Italy-USA program; the identification of different mammographic signs for prediction of histological diagnosis of breast lesions through mammotone; the analysis of relationship between serum proteomic profile and micro histology characteristics of breast lesions

A supply-demand model of fetal energy sufficiency predicts lipid profiles in male but not female Filipino adolescents.

Science.gov (United States)

Kuzawa, C W; Adair, L S

2004-03-01

To test the hypothesis that the balance between fetal nutritional demand and maternal nutritional supply during pregnancy will predict lipid profiles in offspring measured in adolescence. A total of 296 male and 307 female Filipino offspring (aged 14-16 y) and mothers enrolled in a longitudinal birth cohort study (begun in 1983-84) had lipid profiles measured. Data on maternal height (as a proxy for offspring growth potential and thus fetal nutritional demand) and third trimester maternal arm fat area (as a proxy for maternal supply) were used to create four groups hypothesized to reflect a gradient of fetal energy sufficiency. As fetal energy sufficiency increased among males, there was a decrease in total cholesterol (TC) (Psupply-demand model did not predict any lipid outcome or clinical risk criteria. Our findings in males support the hypothesis that the balance between fetal nutritional demand and maternal nutritional supply has implications for future lipid profiles. The lack of significant associations in females adds to mounting evidence for sex differences in lipid metabolism programming, and may reflect sex differences in fetal nutritional demand. The National Science Foundation, the Mellon Foundation, the Nestle Foundation, and the Emory University Internationalization Program.

Cannabis use in children with individualized risk profiles: Predicting the effect of universal prevention intervention.

Science.gov (United States)

Miovský, Michal; Vonkova, Hana; Čablová, Lenka; Gabrhelík, Roman

2015-11-01

To study the effect of a universal prevention intervention targeting cannabis use in individual children with different risk profiles. A school-based randomized controlled prevention trial was conducted over a period of 33 months (n=1874 sixth-graders, baseline mean age 11.82). We used a two-level random intercept logistic model for panel data to predict the probabilities of cannabis use for each child. Specifically, we used eight risk/protective factors to characterize each child and then predicted two probabilities of cannabis use for each child if the child had the intervention or not. Using the two probabilities, we calculated the absolute and relative effect of the intervention for each child. According to the two probabilities, we also divided the sample into a low-risk group (the quarter of the children with the lowest probabilities), a moderate-risk group, and a high-risk group (the quarter of the children with the highest probabilities) and showed the average effect of the intervention on these groups. The differences between the intervention group and the control group were statistically significant in each risk group. The average predicted probabilities of cannabis use for a child from the low-risk group were 4.3% if the child had the intervention and 6.53% if no intervention was provided. The corresponding probabilities for a child from the moderate-risk group were 10.91% and 15.34% and for a child from the high-risk group 25.51% and 32.61%. School grades, thoughts of hurting oneself, and breaking the rules were the three most important factors distinguishing high-risk and low-risk children. We predicted the effect of the intervention on individual children, characterized by their risk/protective factors. The predicted absolute effect and relative effect of any intervention for any selected risk/protective profile of a given child may be utilized in both prevention practice and research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of Castration Resistant Prostate Cancer can be Predicted by a DNA Hypermethylation Profile.

Science.gov (United States)

Angulo, Javier C; Andrés, Guillermo; Ashour, Nadia; Sánchez-Chapado, Manuel; López, Jose I; Ropero, Santiago

2016-03-01

Detection of DNA hypermethylation has emerged as a novel molecular biomarker for prostate cancer diagnosis and evaluation of prognosis. We sought to define whether a hypermethylation profile of patients with prostate cancer on androgen deprivation would predict castrate resistant prostate cancer. Genome-wide methylation analysis was performed using a methylation cancer panel in 10 normal prostates and 45 tumor samples from patients placed on androgen deprivation who were followed until castrate resistant disease developed. Castrate resistant disease was defined according to EAU (European Association of Urology) guideline criteria. Two pathologists reviewed the Gleason score, Ki-67 index and neuroendocrine differentiation. Hierarchical clustering analysis was performed and relationships with outcome were investigated by Cox regression and log rank analysis. We found 61 genes that were significantly hypermethylated in greater than 20% of tumors analyzed. Three clusters of patients were characterized by a DNA methylation profile, including 1 at risk for earlier castrate resistant disease (log rank p = 0.019) and specific mortality (log rank p = 0.002). Hypermethylation of ETV1 (HR 3.75) and ZNF215 (HR 2.89) predicted disease progression despite androgen deprivation. Hypermethylation of IRAK3 (HR 13.72), ZNF215 (HR 4.81) and SEPT9 (HR 7.64) were independent markers of prognosis. Prostate specific antigen greater than 25 ng/ml, Gleason pattern 5, Ki-67 index greater than 12% and metastasis at diagnosis also predicted a negative response to androgen deprivation. Study limitations included the retrospective design and limited number of cases. Epigenetic silencing of the mentioned genes could be novel molecular markers for the prognosis of advanced prostate cancer. It might predict castrate resistance during hormone deprivation and, thus, disease specific mortality. Gene hypermethylation is associated with disease progression in patients who receive hormone therapy. It

Latent profiles of nonresidential father engagement six years after divorce predict long-term offspring outcomes.

Science.gov (United States)

Modecki, Kathryn Lynn; Hagan, Melissa J; Sandler, Irwin; Wolchik, Sharlene A

2015-01-01

This study examined profiles of nonresidential father engagement (i.e., support to the adolescent, contact frequency, remarriage, relocation, and interparental conflict) with their adolescent children (N = 156) 6 to 8 years following divorce and the prospective relation between these profiles and the psychosocial functioning of their offspring, 9 years later. Parental divorce occurred during late childhood to early adolescence; indicators of nonresidential father engagement were assessed during adolescence, and mental health problems and academic achievement of offspring were assessed 9 years later in young adulthood. Three profiles of father engagement were identified in our sample of mainly White, non-Hispanic divorced fathers: Moderate Involvement/Low Conflict, Low Involvement/Moderate Conflict, and High Involvement/High Conflict. Profiles differentially predicted offspring outcomes 9 years later when they were young adults, controlling for quality of the mother-adolescent relationship, mother's remarriage, mother's income, and gender, age, and offspring mental health problems in adolescence. Offspring of fathers characterized as Moderate Involvement/Low Conflict had the highest academic achievement and the lowest number of externalizing problems 9 years later compared to offspring whose fathers had profiles indicating either the highest or lowest levels of involvement but higher levels of conflict. Results indicate that greater paternal psychosocial support and more frequent father-adolescent contact do not outweigh the negative impact of interparental conflict on youth outcomes in the long term. Implications of findings for policy and intervention are discussed.

Gas chromatography/mass spectrometry based component profiling and quality prediction for Japanese sake.

Science.gov (United States)

Mimura, Natsuki; Isogai, Atsuko; Iwashita, Kazuhiro; Bamba, Takeshi; Fukusaki, Eiichiro

2014-10-01

Sake is a Japanese traditional alcoholic beverage, which is produced by simultaneous saccharification and alcohol fermentation of polished and steamed rice by Aspergillus oryzae and Saccharomyces cerevisiae. About 300 compounds have been identified in sake, and the contribution of individual components to the sake flavor has been examined at the same time. However, only a few compounds could explain the characteristics alone and most of the attributes still remain unclear. The purpose of this study was to examine the relationship between the component profile and the attributes of sake. Gas chromatography coupled with mass spectrometry (GC/MS)-based non-targeted analysis was employed to obtain the low molecular weight component profile of Japanese sake including both nonvolatile and volatile compounds. Sake attributes and overall quality were assessed by analytical descriptive sensory test and the prediction model of the sensory score from the component profile was constructed by means of orthogonal projections to latent structures (OPLS) regression analysis. Our results showed that 12 sake attributes [ginjo-ka (aroma of premium ginjo sake), grassy/aldehydic odor, sweet aroma/caramel/burnt odor, sulfury odor, sour taste, umami, bitter taste, body, amakara (dryness), aftertaste, pungent/smoothness and appearance] and overall quality were accurately explained by component profiles. In addition, we were able to select statistically significant components according to variable importance on projection (VIP). Our methodology clarified the correlation between sake attribute and 200 low molecular components and presented the importance of each component thus, providing new insights to the flavor study of sake. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

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

Energy Technology Data Exchange (ETDEWEB)

Kaye, S. M., E-mail: skaye@pppl.gov; Guttenfelder, W.; Bell, R. E.; Gerhardt, S. P.; LeBlanc, B. P.; Maingi, R. [Princeton Plasma Physics Laboratory, Princeton University, Princeton, New Jersey 08543 (United States)

2014-08-15

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 stability 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.

Professional choice self-efficacy: predicting traits and personality profiles in high school students

Directory of Open Access Journals (Sweden)

Rodolfo Augusto Matteo Ambiel

2016-01-01

Full Text Available Abstract This study aimed to verify the predictive capacity of the Big Five personality factors related to professional choice self-efficacy, as well as to draw a personality profile of people with diverse self-efficacy levels. There were 308 high school students participating, from three different grades (57.5 % women, from public and private schools, average 26.64 years of age. Students completed two instruments, Escala de Autoeficácia para Escolha Profissional (Professional Choice Self-efficacy Scale and Bateria Fatorial de Personalidade (Factorial Personality Battery. Results were obtained using multiple regression analysis, analysis of variance with repeated measures profile and Cohen’s d to estimate the effect size of differences. Results showed that Extraversion, Agreeableness and Conscientiousness were the main predictors of self-efficacy. Differences from medium to large were observed between extreme groups, and Extraversion and Conscientiousness were the personality factors that better distinguish people with low and high levels of self-efficacy. Theses results partially corroborate with the hypothesis. Results were discussed based on literature and on the practical implications of the results. New studies are proposed.

DEFINING THE CHEMICAL SPACE OF PUBLIC GENOMIC ...

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The current project aims to chemically index the genomics content of public genomic databases to make these data accessible in relation to other publicly available, chemically-indexed toxicological information. By defining the chemical space of public genomic data, it is possible to identify classes of chemicals on which to develop methodologies for the integration of chemogenomic data into predictive toxicology. The chemical space of public genomic data will be presented as well as the methodologies and tools developed to identify this chemical space.

Mathematical model to predict temperature profile and air–fuel equivalence ratio of a downdraft gasification process

International Nuclear Information System (INIS)

Jaojaruek, Kitipong

2014-01-01

Highlights: • A mathematical model based on finite computation analysis was developed. • Model covers all zones of gasification process which will be useful to improve gasifier design. • Model can predict temperature profile, feedstock consumption rate and reaction equivalent ratio (ϕ). • Model-predicted parameters fitted well with experimental values. - Abstract: A mathematical model for the entire length of a downdraft gasifier was developed using thermochemical principles to derive energy and mass conversion equations. Analysis of heat transfer (conduction, convection and radiation) and chemical kinetic technique were applied to predict the temperature profile, feedstock consumption rate (FCR) and reaction equivalence ratio (RER). The model will be useful for designing gasifiers, estimating output gas composition and gas production rate (GPR). Implicit finite difference method solved the equations on the considered reactor length (50 cm) and diameter (20 cm). Conversion criteria for calculation of temperature and feedstock consumption rate were 1 × 10 −6 °C and 1 × 10 −6 kg/h, respectively. Experimental validation showed that model outputs fitted well with experimental data. Maximum deviation between model and experimental data of temperature, FCR and RER were 52 °C at combustion temperature 663 °C, 0.7 kg/h at the rate 8.1 kg/h and 0.03 at the RER 0.42, respectively. Experimental uncertainty of temperature, FCR and RER were 24.4 °C, 0.71 kg/h and 0.04, respectively, on confidence level of 95%

FUNCTIONAL SUBCLONE PROFILING FOR PREDICTION OF TREATMENT-INDUCED INTRA-TUMOR POPULATION SHIFTS AND DISCOVERY OF RATIONAL DRUG COMBINATIONS IN HUMAN GLIOBLASTOMA

Science.gov (United States)

Reinartz, Roman; Wang, Shanshan; Kebir, Sied; Silver, Daniel J.; Wieland, Anja; Zheng, Tong; Küpper, Marius; Rauschenbach, Laurèl; Fimmers, Rolf; Shepherd, Timothy M.; Trageser, Daniel; Till, Andreas; Schäfer, Niklas; Glas, Martin; Hillmer, Axel M.; Cichon, Sven; Smith, Amy A.; Pietsch, Torsten; Liu, Ying; Reynolds, Brent A.; Yachnis, Anthony; Pincus, David W.; Simon, Matthias; Brüstle, Oliver; Steindler, Dennis A.; Scheffler, Björn

2016-01-01

Purpose Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design Here, we used 33 single cell-derived subclones generated from five clinical glioblastoma specimens for exploring intra- and inter-individual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacological sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential anti-glioblastoma compounds. A more comprehensive intra-tumoral analysis revealed that stable genetic and phenotypic characteristics of co-existing subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacological profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions Our data provide a previously unrecognized strategy for revealing functional consequences of intra-tumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma. PMID:27521447

Kozeny-Carman permeability relationship with disintegration process predicted from early dissolution profiles of immediate release tablets.

Science.gov (United States)

Kumari, Parveen; Rathi, Pooja; Kumar, Virender; Lal, Jatin; Kaur, Harmeet; Singh, Jasbir

2017-07-01

This study was oriented toward the disintegration profiling of the diclofenac sodium (DS) immediate-release (IR) tablets and development of its relationship with medium permeability k perm based on Kozeny-Carman equation. Batches (L1-L9) of DS IR tablets with different porosities and specific surface area were prepared at different compression forces and evaluated for porosity, in vitro dissolution and particle-size analysis of the disintegrated mass. The k perm was calculated from porosities and specific surface area, and disintegration profiles were predicted from the dissolution profiles of IR tablets by stripping/residual method. The disintegration profiles were subjected to exponential regression to find out the respective disintegration equations and rate constants k d . Batches L1 and L2 showed the fastest disintegration rates as evident from their bi-exponential equations while the rest of the batches L3-L9 exhibited the first order or mono-exponential disintegration kinetics. The 95% confidence interval (CI 95% ) revealed significant differences between k d values of different batches except L4 and L6. Similar results were also spotted for dissolution profiles of IR tablets by similarity (f 2 ) test. The final relationship between k d and k perm was found to be hyperbolic, signifying the initial effect of k perm on the disintegration rate. The results showed that disintegration profiling is possible because a relationship exists between k d and k perm . The later being relatable with porosity and specific surface area can be determined by nondestructive tests.

Does Enjoying Friendship Help or Impede Academic Achievement? Academic and Social Intrinsic Value Profiles Predict Academic Achievement

Science.gov (United States)

Seo, Eunjin; Lee, You-kyung

2018-01-01

We examine the intrinsic value students placed on schoolwork (i.e. academic intrinsic value) and social relationships (i.e. social intrinsic value). We then look at how these values predict middle and high school achievement. To do this, we came up with four profiles based on cluster analyses of 6,562 South Korean middle school students. The four…

Predicting fatty acid profiles in blood based on food intake and the FADS1 rs174546 SNP.

Science.gov (United States)

Hallmann, Jacqueline; Kolossa, Silvia; Gedrich, Kurt; Celis-Morales, Carlos; Forster, Hannah; O'Donovan, Clare B; Woolhead, Clara; Macready, Anna L; Fallaize, Rosalind; Marsaux, Cyril F M; Lambrinou, Christina-Paulina; Mavrogianni, Christina; Moschonis, George; Navas-Carretero, Santiago; San-Cristobal, Rodrigo; Godlewska, Magdalena; Surwiłło, Agnieszka; Mathers, John C; Gibney, Eileen R; Brennan, Lorraine; Walsh, Marianne C; Lovegrove, Julie A; Saris, Wim H M; Manios, Yannis; Martinez, Jose Alfredo; Traczyk, Iwona; Gibney, Michael J; Daniel, Hannelore

2015-12-01

A high intake of n-3 PUFA provides health benefits via changes in the n-6/n-3 ratio in blood. In addition to such dietary PUFAs, variants in the fatty acid desaturase 1 (FADS1) gene are also associated with altered PUFA profiles. We used mathematical modeling to predict levels of PUFA in whole blood, based on multiple hypothesis testing and bootstrapped LASSO selected food items, anthropometric and lifestyle factors, and the rs174546 genotypes in FADS1 from 1607 participants (Food4Me Study). The models were developed using data from the first reported time point (training set) and their predictive power was evaluated using data from the last reported time point (test set). Among other food items, fish, pizza, chicken, and cereals were identified as being associated with the PUFA profiles. Using these food items and the rs174546 genotypes as predictors, models explained 26-43% of the variability in PUFA concentrations in the training set and 22-33% in the test set. Selecting food items using multiple hypothesis testing is a valuable contribution to determine predictors, as our models' predictive power is higher compared to analogue studies. As unique feature, we additionally confirmed our models' power based on a test set. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting the oral pharmacokinetic profiles of multiple-unit (pellet) dosage forms using a modeling and simulation approach coupled with biorelevant dissolution testing: case example diclofenac sodium.

Science.gov (United States)

Kambayashi, Atsushi; Blume, Henning; Dressman, Jennifer B

2014-07-01

The objective of this research was to characterize the dissolution profile of a poorly soluble drug, diclofenac, from a commercially available multiple-unit enteric coated dosage form, Diclo-Puren® capsules, and to develop a predictive model for its oral pharmacokinetic profile. The paddle method was used to obtain the dissolution profiles of this dosage form in biorelevant media, with the exposure to simulated gastric conditions being varied in order to simulate the gastric emptying behavior of pellets. A modified Noyes-Whitney theory was subsequently fitted to the dissolution data. A physiologically-based pharmacokinetic (PBPK) model for multiple-unit dosage forms was designed using STELLA® software and coupled with the biorelevant dissolution profiles in order to simulate the plasma concentration profiles of diclofenac from Diclo-Puren® capsule in both the fasted and fed state in humans. Gastric emptying kinetics relevant to multiple-units pellets were incorporated into the PBPK model by setting up a virtual patient population to account for physiological variations in emptying kinetics. Using in vitro biorelevant dissolution coupled with in silico PBPK modeling and simulation it was possible to predict the plasma profile of this multiple-unit formulation of diclofenac after oral administration in both the fasted and fed state. This approach might be useful to predict variability in the plasma profiles for other drugs housed in multiple-unit dosage forms. Copyright © 2014 Elsevier B.V. All rights reserved.

Higher schizotypy predicts better metabolic profile in unaffected siblings of patients with schizophrenia.

Science.gov (United States)

Atbasoglu, E Cem; Gumus-Akay, Guvem; Guloksuz, Sinan; Saka, Meram Can; Ucok, Alp; Alptekin, Koksal; Gullu, Sevim; van Os, Jim

2018-04-01

Type 2 diabetes (T2D) is more frequent in schizophrenia (Sz) than in the general population. This association is partly accounted for by shared susceptibility genetic variants. We tested the hypotheses that a genetic predisposition to Sz would be associated with higher likelihood of insulin resistance (IR), and that IR would be predicted by subthreshold psychosis phenotypes. Unaffected siblings of Sz patients (n = 101) were compared with a nonclinical sample (n = 305) in terms of IR, schizotypy (SzTy), and a behavioural experiment of "jumping to conclusions". The measures, respectively, were the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Structured Interview for Schizotypy-Revised (SIS-R), and the Beads Task (BT). The likelihood of IR was examined in multiple regression models that included sociodemographic, metabolic, and cognitive parameters alongside group status, SIS-R scores, and BT performance. Insulin resistance was less frequent in siblings (31.7%) compared to controls (43.3%) (p model that examined all relevant parameters included the tSzTy tertiles, TG and HDL-C levels, and BMI, as significant predictors of IR. Lack of IR was predicted by the highest as compared to the lowest SzTy tertile [OR (95%CI): 0.43 (0.21-0.85), p = 0.015]. Higher dopaminergic activity may contribute to both schizotypal features and a favourable metabolic profile in the same individual. This is compatible with dopamine's regulatory role in glucose metabolism via indirect central actions and a direct action on pancreatic insulin secretion. The relationship between dopaminergic activity and metabolic profile in Sz must be examined in longitudinal studies with younger unaffected siblings.

Latent profiles of non-residential father engagement six years after divorce predict long term offspring outcomes

Science.gov (United States)

Modecki, Kathryn Lynn; Hagan, Melissa; Sandler, Irwin; Wolchik, Sharlene

2014-01-01

This study examined profiles of non-residential father engagement (i.e., support to the adolescent, contact frequency, remarriage, relocation, and interparental conflict) with their adolescent children (N = 156) six to eight years following divorce and the prospective relation between these profiles and the psychosocial functioning of their offspring, nine years later. Parental divorce occurred during late childhood to early adolescence; indicators of non-residential father engagement were assessed during adolescence, and mental health problems and academic achievement of offspring were assessed nine years later in young adulthood. Three profiles of father engagement were identified in our sample of mainly White, non-Hispanic divorced fathers: Moderate Involvement/Low Conflict, Low Involvement/Moderate Conflict, and High Involvement/High Conflict. Profiles differentially predicted offspring outcomes nine years later when they were young adults, controlling for quality of the mother-adolescent relationship, mother’s remarriage, mother’s income, and gender, age and offspring mental health problems in adolescence. Offspring of fathers characterized as Moderate Involvement/Low Conflict had the highest academic achievement and the lowest number of externalizing problems nine years later compared to offspring whose fathers had profiles indicating either the highest or lowest levels of involvement but higher levels of conflict. Results indicate that greater paternal psychosocial support and more frequent father-adolescent contact do not outweigh the negative impact of interparental conflict on youth outcomes in the long-term. Implications of findings for policy and intervention are discussed. PMID:24484456

Psoriasis prediction from genome-wide SNP profiles

Directory of Open Access Journals (Sweden)

Fang Xiangzhong

2011-01-01

Full Text Available Abstract Background With the availability of large-scale genome-wide association study (GWAS data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs to predict psoriasis from searching GWAS data. Methods Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB method was compared with classical linear discriminant analysis(LDA for classification performance. Results The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698, while only 0.520(95% CI: 0.472-0.524 was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study. Conclusions The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.

Assist feature printability prediction by 3-D resist profile reconstruction

Science.gov (United States)

Zheng, Xin; Huang, Jensheng; Chin, Fook; Kazarian, Aram; Kuo, Chun-Chieh

2012-06-01

properties may then be used to optimize the printability vs. efficacy of an SRAF either prior to or during an Optical Proximity Correction (OPC) run. The process models that are used during OPC have never been able to reliably predict which SRAFs will print. This appears to be due to the fact that OPC process models are generally created using data that does not include printed subresolution patterns. An enhancement to compact modeling capability to predict Assist Features (AF) printability is developed and discussed. A hypsometric map representing 3-D resist profile was built by applying a first principle approximation to estimate the "energy loss" from the resist top to bottom. Such a 3-D resist profile is an extrapolation of a well calibrated traditional OPC model without any additional information. Assist features are detected at either top of resist (dark field) or bottom of resist (bright field). Such detection can be done by just extracting top or bottom resist models from our 3-D resist model. There is no measurement of assist features needed when we build AF but it can be included if interested but focusing on resist calibration to account for both exposure dosage and focus change sensitivities. This approach significantly increases resist model's capability for predicting printed SRAF accuracy. And we don't need to calibrate an SRAF model in addition to the OPC model. Without increase in computation time, this compact model can draw assist feature contour with real placement and size at any vertical plane. The result is compared and validated with 3-D rigorous modeling as well as SEM images. Since this method does not change any form of compact modeling, it can be integrated into current MBAF solutions without any additional work.

Magnetic resonance metabolic profiling of breast cancer tissue obtained with core needle biopsy for predicting pathologic response to neoadjuvant chemotherapy.

Directory of Open Access Journals (Sweden)

Ji Soo Choi

Full Text Available The purpose of this study was to determine whether metabolic profiling of core needle biopsy (CNB samples using high-resolution magic angle spinning (HR-MAS magnetic resonance spectroscopy (MRS could be used for predicting pathologic response to neoadjuvant chemotherapy (NAC in patients with locally advanced breast cancer. After institutional review board approval and informed consent were obtained, CNB tissue samples were collected from 37 malignant lesions in 37 patients before NAC treatment. The metabolic profiling of CNB samples were performed by HR-MAS MRS. Metabolic profiles were compared according to pathologic response to NAC using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA. Various metabolites including choline-containing compounds were identified and quantified by HR-MAS MRS in all 37 breast cancer tissue samples obtained by CNB. In univariate analysis, the metabolite concentrations and metabolic ratios of CNB samples obtained with HR-MAS MRS were not significantly different between different pathologic response groups. However, there was a trend of lower levels of phosphocholine/creatine ratio and choline-containing metabolite concentrations in the pathologic complete response group compared to the non-pathologic complete response group. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the pathologic response groups. This study showed OPLS-DA multivariate analysis using metabolic profiles of pretreatment CNB samples assessed by HR- MAS MRS may be used to predict pathologic response before NAC, although we did not identify the metabolite showing statistical significance in univariate analysis. Therefore, our preliminary results raise the necessity of further study on HR-MAS MR metabolic profiling of CNB samples for a large number of cancers.

Profiles of verbal working memory growth predict speech and language development in children with cochlear implants.

Science.gov (United States)

Kronenberger, William G; Pisoni, David B; Harris, Michael S; Hoen, Helena M; Xu, Huiping; Miyamoto, Richard T

2013-06-01

Verbal short-term memory (STM) and working memory (WM) skills predict speech and language outcomes in children with cochlear implants (CIs) even after conventional demographic, device, and medical factors are taken into account. However, prior research has focused on single end point outcomes as opposed to the longitudinal process of development of verbal STM/WM and speech-language skills. In this study, the authors investigated relations between profiles of verbal STM/WM development and speech-language development over time. Profiles of verbal STM/WM development were identified through the use of group-based trajectory analysis of repeated digit span measures over at least a 2-year time period in a sample of 66 children (ages 6-16 years) with CIs. Subjects also completed repeated assessments of speech and language skills during the same time period. Clusters representing different patterns of development of verbal STM (digit span forward scores) were related to the growth rate of vocabulary and language comprehension skills over time. Clusters representing different patterns of development of verbal WM (digit span backward scores) were related to the growth rate of vocabulary and spoken word recognition skills over time. Different patterns of development of verbal STM/WM capacity predict the dynamic process of development of speech and language skills in this clinical population.

SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

Science.gov (United States)

Suresh, V; Parthasarathy, S

2014-01-01

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

Science.gov (United States)

Yabu, Julie M; Siebert, Janet C; Maecker, Holden T

2016-01-01

Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize

Scaling laws for TEXT plasma profiles

International Nuclear Information System (INIS)

McCool, S.C.; Bravenec, R.V.; Chen, J.Y.; Foster, M.S.; Li, W.L.; Ouroura, A.; Phillips, P.E.; Richards, B.; Wenzel, K.W.; Zhang, Z.M.

1994-01-01

Regression analysis has been performed on a number of measured profiles including temperature and density vs. nominal macroscopic operating parameters for TEXT tokamak (pre-upgrade) ohmic plasmas. The resulting simple empirical model has enabled the authors to quickly approximate profiles of electron temperature and density, ion temperature, and soft x-ray brightness, as well as the scalar quantities: total radiated power, q=1 radius, sawtooth period and amplitude, and energy confinement time as a power law of toroidal field, plasma current, chord average density, and fueling gas atomic weight. The model profiles are only applicable to the plasma interior, i.e. within the limiter radius. In most cases the predicted model profiles are within the experimental error bars of measured profiles and are more accurate at predicting profile variation for small operating parameter changes than the measured profiles

Draft forces prediction model for standard single tines by using principles of soil mechanics and soil profile evaluation

Directory of Open Access Journals (Sweden)

Amer Khalid Ahmed Al-Neama

2017-06-01

Full Text Available This paper explains a model to predict the draft force acting on varying standard single tines by using principles of soil mechanics and soil profile evaluation. Draft force (Fd measurements were made with four standard single tines comprising Heavy Duty, Double Heart, Double Heart with Wings and Duck Foot. Tine widths were 6.5, 13.5, 45 and 40 cm, respectively. The test was conducted in a soil bin with sandy loam soil. The effects of forward speeds and working depths on draft forces were investigated under controlled lab conditions. Results were evaluated based on a prediction model. A good correlation between measured and predicted Fd values for all tines with an average absolute variation less than 15 % was found.

Predictive modelling of grain size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

Science.gov (United States)

Baasch, B.; M"uller, H.; von Dobeneck, T.

2018-04-01

In this work we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine learning techniques. Nonnegative matrix factorisation is used to determine grain-size end-members from sediment surface samples. Four end-members were found which well represent the variety of sediments in the study area. A radial-basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.

Prediction of Phenotypic Antimicrobial Resistance Profiles From Whole Genome Sequences of Non-typhoidal Salmonella enterica.

Science.gov (United States)

Neuert, Saskia; Nair, Satheesh; Day, Martin R; Doumith, Michel; Ashton, Philip M; Mellor, Kate C; Jenkins, Claire; Hopkins, Katie L; Woodford, Neil; de Pinna, Elizabeth; Godbole, Gauri; Dallman, Timothy J

2018-01-01

Surveillance of antimicrobial resistance (AMR) in non-typhoidal Salmonella enterica (NTS), is essential for monitoring transmission of resistance from the food chain to humans, and for establishing effective treatment protocols. We evaluated the prediction of phenotypic resistance in NTS from genotypic profiles derived from whole genome sequencing (WGS). Genes and chromosomal mutations responsible for phenotypic resistance were sought in WGS data from 3,491 NTS isolates received by Public Health England's Gastrointestinal Bacteria Reference Unit between April 2014 and March 2015. Inferred genotypic AMR profiles were compared with phenotypic susceptibilities determined for fifteen antimicrobials using EUCAST guidelines. Discrepancies between phenotypic and genotypic profiles for one or more antimicrobials were detected for 76 isolates (2.18%) although only 88/52,365 (0.17%) isolate/antimicrobial combinations were discordant. Of the discrepant results, the largest number were associated with streptomycin (67.05%, n = 59). Pan-susceptibility was observed in 2,190 isolates (62.73%). Overall, resistance to tetracyclines was most common (26.27% of isolates, n = 917) followed by sulphonamides (23.72%, n = 828) and ampicillin (21.43%, n = 748). Multidrug resistance (MDR), i.e., resistance to three or more antimicrobial classes, was detected in 848 isolates (24.29%) with resistance to ampicillin, streptomycin, sulphonamides and tetracyclines being the most common MDR profile ( n = 231; 27.24%). For isolates with this profile, all but one were S . Typhimurium and 94.81% ( n = 219) had the resistance determinants bla TEM-1, strA-strB, sul2 and tet (A). Extended-spectrum β-lactamase genes were identified in 41 isolates (1.17%) and multiple mutations in chromosomal genes associated with ciprofloxacin resistance in 82 isolates (2.35%). This study showed that WGS is suitable as a rapid means of determining AMR patterns of NTS for public health surveillance.

Computational Analysis of Epidermal Growth Factor Receptor Mutations Predicts Differential Drug Sensitivity Profiles toward Kinase Inhibitors.

Science.gov (United States)

Akula, Sravani; Kamasani, Swapna; Sivan, Sree Kanth; Manga, Vijjulatha; Vudem, Dashavantha Reddy; Kancha, Rama Krishna

2018-05-01

A significant proportion of patients with lung cancer carry mutations in the EGFR kinase domain. The presence of a deletion mutation in exon 19 or L858R point mutation in the EGFR kinase domain has been shown to cause enhanced efficacy of inhibitor treatment in patients with NSCLC. Several less frequent (uncommon) mutations in the EGFR kinase domain with potential implications in treatment response have also been reported. The role of a limited number of uncommon mutations in drug sensitivity was experimentally verified. However, a huge number of these mutations remain uncharacterized for inhibitor sensitivity or resistance. A large-scale computational analysis of clinically reported 298 point mutants of EGFR kinase domain has been performed, and drug sensitivity profiles for each mutant toward seven kinase inhibitors has been determined by molecular docking. In addition, the relative inhibitor binding affinity toward each drug as compared with that of adenosine triphosphate was calculated for each mutant. The inhibitor sensitivity profiles predicted in this study for a set of previously characterized mutants correlated well with the published clinical, experimental, and computational data. Both the single and compound mutations displayed differential inhibitor sensitivity toward first- and next-generation kinase inhibitors. The present study provides predicted drug sensitivity profiles for a large panel of uncommon EGFR mutations toward multiple inhibitors, which may help clinicians in deciding mutant-specific treatment strategies. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot.

Science.gov (United States)

Robin, Marie-Hélène; Colbach, Nathalie; Lucas, Philippe; Montfort, Françoise; Cholez, Célia; Debaeke, Philippe; Aubertot, Jean-Noël

2013-01-01

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

Profiling cancer

DEFF Research Database (Denmark)

Ciro, Marco; Bracken, Adrian P; Helin, Kristian

2003-01-01

In the past couple of years, several very exciting studies have demonstrated the enormous power of gene-expression profiling for cancer classification and prediction of patient survival. In addition to promising a more accurate classification of cancer and therefore better treatment of patients......, gene-expression profiling can result in the identification of novel potential targets for cancer therapy and a better understanding of the molecular mechanisms leading to cancer....

Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis.

Directory of Open Access Journals (Sweden)

Robert W Chapman

Full Text Available Inherited gene transcripts deposited in oocytes direct early embryonic development in all vertebrates, but transcript profiles indicative of embryo developmental competence have not previously been identified. We employed artificial intelligence to model profiles of maternal ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in the striped bass (Morone saxatilis, a farmed species with serious egg quality problems. In models developed using artificial neural networks (ANNs and supervised machine learning, collective changes in the expression of a limited suite of genes (233 representing 90% of the eventual variance in embryo survival. Egg quality related to minor changes in gene expression (<0.2-fold, with most individual transcripts making a small contribution (<1% to the overall prediction of egg quality. These findings indicate that the predictive power of the transcriptome as regards egg quality resides not in levels of individual genes, but rather in the collective, coordinated expression of a suite of transcripts constituting a transcriptomic "fingerprint". Correlation analyses of the corresponding candidate genes indicated that dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence. The affected gene networks are centrally involved in regulation of early development in all vertebrates, including humans. By assessing collective levels of the relevant ovarian transcripts via ANNs we were able, for the first time in any vertebrate, to accurately predict the subsequent embryo developmental potential of eggs from individual females. Our results show that the transcriptomic fingerprint evidencing developmental dysfunction is highly predictive of, and therefore likely to regulate, egg quality, a biologically complex trait crucial to reproductive fitness.

Applying geographic profiling used in the field of criminology for predicting the nest locations of bumble bees.

Science.gov (United States)

Suzuki-Ohno, Yukari; Inoue, Maki N; Ohno, Kazunori

2010-07-21

We tested whether geographic profiling (GP) can predict multiple nest locations of bumble bees. GP was originally developed in the field of criminology for predicting the area where an offender most likely resides on the basis of the actual crime sites and the predefined probability of crime interaction. The predefined probability of crime interaction in the GP model depends on the distance of a site from an offender's residence. We applied GP for predicting nest locations, assuming that foraging and nest sites were the crime sites and the offenders' residences, respectively. We identified the foraging and nest sites of the invasive species Bombus terrestris in 2004, 2005, and 2006. We fitted GP model coefficients to the field data of the foraging and nest sites, and used GP with the fitting coefficients. GP succeeded in predicting about 10-30% of actual nests. Sensitivity analysis showed that the predictability of the GP model mainly depended on the coefficient value of buffer zone, the distance at the mode of the foraging probability. GP will be able to predict the nest locations of bumble bees in other area by using the fitting coefficient values measured in this study. It will be possible to further improve the predictability of the GP model by considering food site preference and nest density. (c) 2010 Elsevier Ltd. All rights reserved.

Quantitative prediction of shrimp disease incidence via the profiles of gut eukaryotic microbiota.

Science.gov (United States)

Xiong, Jinbo; Yu, Weina; Dai, Wenfang; Zhang, Jinjie; Qiu, Qiongfen; Ou, Changrong

2018-04-01

One common notion is emerging that gut eukaryotes are commensal or beneficial, rather than detrimental. To date, however, surprisingly few studies have been taken to discern the factors that govern the assembly of gut eukaryotes, despite growing interest in the dysbiosis of gut microbiota-disease relationship. Herein, we firstly explored how the gut eukaryotic microbiotas were assembled over shrimp postlarval to adult stages and a disease progression. The gut eukaryotic communities changed markedly as healthy shrimp aged, and converged toward an adult-microbiota configuration. However, the adult-like stability was distorted by disease exacerbation. A null model untangled that the deterministic processes that governed the gut eukaryotic assembly tended to be more important over healthy shrimp development, whereas this trend was inverted as the disease progressed. After ruling out the baseline of gut eukaryotes over shrimp ages, we identified disease-discriminatory taxa (species level afforded the highest accuracy of prediction) that characteristic of shrimp health status. The profiles of these taxa contributed an overall 92.4% accuracy in predicting shrimp health status. Notably, this model can accurately diagnose the onset of shrimp disease. Interspecies interaction analysis depicted how the disease-discriminatory taxa interacted with one another in sustaining shrimp health. Taken together, our findings offer novel insights into the underlying ecological processes that govern the assembly of gut eukaryotes over shrimp postlarval to adult stages and a disease progression. Intriguingly, the established model can quantitatively and accurately predict the incidences of shrimp disease.

The affective profiles, psychological well-being, and harmony: environmental mastery and self-acceptance predict the sense of a harmonious life

Directory of Open Access Journals (Sweden)

Danilo Garcia

2014-02-01

explained by the dimensions of psychological well-being within the four affective profiles. Specifically, harmony in life was significantly predicted by environmental mastery and self-acceptance across all affective profiles. However, for the low affective group high purpose in life predicted low levels of harmony in life.Conclusions. The results demonstrated that affective profiles systematically relate to psychological well-being and harmony in life. Notably, individuals categorised as self-fulfilling tended to report higher levels of both psychological well-being and harmony in life when compared with the other profiles. Meanwhile individuals in the self-destructive group reported the lowest levels of psychological well-being and harmony when compared with the three other profiles. It is proposed that self-acceptance and environmental acceptance might enable individuals to go from self-destructive to a self-fulfilling state that also involves harmony in life.

Serum protein profiling using an aptamer array predicts clinical outcomes of stage IIA colon cancer: A leave-one-out crossvalidation

Science.gov (United States)

Huh, Jung Wook; Kim, Sung Chun; Sohn, Insuk; Jung, Sin-Ho; Kim, Hee Cheol

2016-01-01

Background In this study, we established and validated a model for predicting prognosis of stage IIA colon cancer patients based on expression profiles of aptamers in serum. Methods Bloods samples were collected from 227 consecutive patients with pathologic T3N0M0 (stage IIA) colon cancer. We incubated 1,149 serum molecule-binding aptamer pools of clinical significance with serum from patients to obtain aptamers bound to serum molecules, which were then amplified and marked. Oligonucleotide arrays were constructed with the base sequences of the 1,149 aptamers, and the marked products identified above were reacted with one another to produce profiles of the aptamers bound to serum molecules. These profiles were organized into low- and high-risk groups of colon cancer patients based on clinical information for the serum samples. Cox proportional hazards model and leave-one-out cross-validation (LOOCV) were used to evaluate predictive performance. Results During a median follow-up period of 5 years, 29 of the 227 patients (11.9%) experienced recurrence. There were 212 patients (93.4%) in the low-risk group and 15 patients (6.6%) in the high-risk group in our aptamer prognosis model. Postoperative recurrence significantly correlated with age and aptamer risk stratification (p = 0.046 and p = 0.001, respectively). In multivariate analysis, aptamer risk stratification (p recurrence. Disease-free survival curves calculated according to aptamer risk level predicted through a LOOCV procedure and age showed significant differences (p < 0.001 from permutations). Conclusion Aptamer risk stratification can be a valuable prognostic factor in stage II colon cancer patients. PMID:26908450

PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches

Science.gov (United States)

Fujibuchi, Wataru; Anderson, John S. J.; Landsman, David

2001-01-01

Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data. PMID:11574681

A proposal of predictive methods of crack propagation life and remaining life of structural metal under creep-fatigue interacted conditions by use of X-ray profile analysis

International Nuclear Information System (INIS)

Ohnami, M.; Sakane, M.; Nishino, S.

1987-01-01

The following two series of studies are described: One is crack propagation life prediction in high-temperature low-cycle fatigue tests under triangular and trapezoidal strain or stress waves for austenitic stainless steel by X-ray fractography. Another is remaining life prediction of the steel under creep-fatigue interacted conditions by applying the concept of the remaining life diagram and X-ray profile analysis. Particle size and microstrain obtained by X-ray profile analysis were effective nondestructive parameters for estimating crack propagation life and remaining life in creep-fatigue interaction

Ohmic ion temperature and thermal diffusivity profiles from the JET neutron emission profile monitor

Energy Technology Data Exchange (ETDEWEB)

Esposito, B. (ENEA, Frascati (Italy). Centro Ricerche Energia); Marcus, F.B.; Conroy, S.; Jarvis, O.N.; Loughlin, M.J.; Sadler, G.; Belle, P. van (Commission of the European Communities, Abingdon (United Kingdom). JET Joint Undertaking); Adams, J.M.; Watkins, N. (AEA Industrial Technology, Harwell (United Kingdom))

1993-10-01

The JET neutron emission profile monitor was used to study ohmically heated deuterium discharges. The radial profile of the neutron emissivity is deduced from the line-integral data. The profiles of ion temperature, T[sub i], and ion thermal diffusivity, [chi][sub i], are derived under steady-state conditions. The ion thermal diffusivity is higher than, and its scaling with plasma current opposite to, that predicted by neoclassical theory. (author).

Ohmic ion temperature and thermal diffusivity profiles from the JET neutron emission profile monitor

International Nuclear Information System (INIS)

Esposito, B.

1993-01-01

The JET neutron emission profile monitor was used to study ohmically heated deuterium discharges. The radial profile of the neutron emissivity is deduced from the line-integral data. The profiles of ion temperature, T i , and ion thermal diffusivity, χ i , are derived under steady-state conditions. The ion thermal diffusivity is higher than, and its scaling with plasma current opposite to, that predicted by neoclassical theory. (author)

Gene Expression Profiling to Predict Clinical Outcome of Breast Cancer: reproducing, analyzing and extending the Nature publication by vhVeer et al

NARCIS (Netherlands)

Li R.; Visser, H.M.

2010-01-01

Chemotherapy and hormonal therapy as adjuvant systemic therapies to inhibit breast cancer recurrence are not necessary for each patient. In Veer's paper "Gene expression profiling predicts clinical outcome of breast cancer" (Nature 2002, PMID: 11823860), they introduced a method based on DNA

Profiling Occupant Behaviour in Danish Dwellings using Time Use Survey Data - Part I: Data Description and Activity Profiling

DEFF Research Database (Denmark)

Barthelmes, V.M.; Li, R.; Andersen, R.K.

2018-01-01

Occupant behaviour has been shown to be one of the key driving factors of uncertainty in prediction of energy consumption in buildings. Building occupants affect building energy use directly and indirectly by interacting with building energy systems such as adjusting temperature set...... occupant profiles for prediction of energy use to reduce the gap between predicted and real building energy consumptions. To generate accurate occupant profiles for the residential sector in Denmark, the Danish time use surveys are considered an essential data source. The latest Danish diarybased time use......-points, switching lights on/off, using electrical devices and opening/closing windows. Furthermore, building inhabitants’ daily activity profiles clearly shape the timing of energy demand in households. Modelling energy-related human activities throughout the day, therefore, is crucial to defining more realistic...

Non-invasive metabolomic profiling of embryo culture media and morphology grading to predict implantation outcome in frozen-thawed embryo transfer cycles.

Science.gov (United States)

Li, Xiong; Xu, Yan; Fu, Jing; Zhang, Wen-Bi; Liu, Su-Ying; Sun, Xiao-Xi

2015-11-01

Assessment of embryo viability is a crucial component of in vitro fertilization and currently relies largely on embryo morphology and cleavage rate. Because morphological assessment remains highly subjective, it can be unreliable in predicting embryo viability. This study investigated the metabolomic profiling of embryo culture media using near-infrared (NIR) spectroscopy for predicting the implantation potential of human embryos in frozen-thawed embryo transfer (FET) cycles. Spent embryo culture media was collected on day 4 after thawed embryo transfer (n = 621) and analysed using NIR spectroscopy. Viability scores were calculated using a predictive multivariate algorithm of fresh embryos with known pregnancy outcomes. The mean viability indices of embryos resulting in clinical pregnancy following FET were significantly higher than those of non-implanted embryos and differed between the 0, 50, and 100 % implantation groups. Notably, the 0 % group index was significantly lower than the 100 % implantation group index (-0.787 ± 0.382 vs. 1.064 ± 0.331, P  0.05). NIR metabolomic profiling of thawed embryo culture media is independent of morphology and correlates with embryo implantation potential in FET cycles. The viability score alone or in conjunction with morphologic grading is a more objective marker for implantation outcome in FET cycles than morphology alone.

ChemProt: A disease chemical biology database

DEFF Research Database (Denmark)

Taboureau, Olivier; Oprea, Tudor I.

2013-01-01

The integration of chemistry, biology, and informatics to study drug actions across multiple biological targets, pathways, and biological systems is an emerging paradigm in drug discovery. Rather than reducing a complex system to simplistic models, fields such as chemogenomics and translational...... informatics are seeking to build a holistic model for a better understanding of the drug pharmacology and clinical effects. Here we will present a webserver called ChemProt that can assist, in silico, the drug actions in the context of cellular and disease networks and contribute in the field of disease...... chemical biology, drug repurposing, and off-target effects prediction....

Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor.

Science.gov (United States)

Vasselli, James R; Shih, Joanna H; Iyengar, Shuba R; Maranchie, Jodi; Riss, Joseph; Worrell, Robert; Torres-Cabala, Carlos; Tabios, Ray; Mariotti, Andra; Stearman, Robert; Merino, Maria; Walther, McClellan M; Simon, Richard; Klausner, Richard D; Linehan, W Marston

2003-06-10

To identify potential molecular determinants of tumor biology and possible clinical outcomes, global gene-expression patterns were analyzed in the primary tumors of patients with metastatic renal cell cancer by using cDNA microarrays. We used grossly dissected tumor masses that included tumor, blood vessels, connective tissue, and infiltrating immune cells to obtain a gene-expression "profile" from each primary tumor. Two patterns of gene expression were found within this uniformly staged patient population, which correlated with a significant difference in overall survival between the two patient groups. Subsets of genes most significantly associated with survival were defined, and vascular cell adhesion molecule-1 (VCAM-1) was the gene most predictive for survival. Therefore, despite the complex biological nature of metastatic cancer, basic clinical behavior as defined by survival may be determined by the gene-expression patterns expressed within the compilation of primary gross tumor cells. We conclude that survival in patients with metastatic renal cell cancer can be correlated with the expression of various genes based solely on the expression profile in the primary kidney tumor.

Effects of historical and predictive information on ability of transport pilot to predict an alert

Science.gov (United States)

Trujillo, Anna C.

1994-01-01

In the aviation community, the early detection of the development of a possible subsystem problem during a flight is potentially useful for increasing the safety of the flight. Commercial airlines are currently using twin-engine aircraft for extended transport operations over water, and the early detection of a possible problem might increase the flight crew's options for safely landing the aircraft. One method for decreasing the severity of a developing problem is to predict the behavior of the problem so that appropriate corrective actions can be taken. To investigate the pilots' ability to predict long-term events, a computer workstation experiment was conducted in which 18 airline pilots predicted the alert time (the time to an alert) using 3 different dial displays and 3 different parameter behavior complexity levels. The three dial displays were as follows: standard (resembling current aircraft round dial presentations); history (indicating the current value plus the value of the parameter 5 sec in the past); and predictive (indicating the current value plus the value of the parameter 5 sec into the future). The time profiles describing the behavior of the parameter consisted of constant rate-of-change profiles, decelerating profiles, and accelerating-then-decelerating profiles. Although the pilots indicated that they preferred the near term predictive dial, the objective data did not support its use. The objective data did show that the time profiles had the most significant effect on performance in estimating the time to an alert.

Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience.

Science.gov (United States)

Bellavista, Paolo; Corradi, Antonio; Foschini, Luca; Ianniello, Raffaele

2015-07-30

Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of the challenging technical issues related to the efficient assignment of Mobile Crowd Sensing (MCS) data collection tasks to volunteers in a crowdsensing campaign. In particular, the paper originally describes how to increase the effectiveness of the proposed sensing campaigns through the inclusion of several new facilities, including accurate participant selection algorithms able to profile and predict user mobility patterns, gaming techniques, and timely geo-notification. The reported results show the feasibility of exploiting profiling trends/prediction techniques from volunteers' behavior; moreover, they quantitatively compare different MCS task assignment strategies based on large-scale and real MCS data campaigns run in the ParticipAct living lab, an ongoing MCS real-world experiment that involved more than 170 students of the University of Bologna for more than one year.

Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience

Directory of Open Access Journals (Sweden)

Paolo Bellavista

2015-07-01

Full Text Available Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of the challenging technical issues related to the efficient assignment of Mobile Crowd Sensing (MCS data collection tasks to volunteers in a crowdsensing campaign. In particular, the paper originally describes how to increase the effectiveness of the proposed sensing campaigns through the inclusion of several new facilities, including accurate participant selection algorithms able to profile and predict user mobility patterns, gaming techniques, and timely geo-notification. The reported results show the feasibility of exploiting profiling trends/prediction techniques from volunteers’ behavior; moreover, they quantitatively compare different MCS task assignment strategies based on large-scale and real MCS data campaigns run in the ParticipAct living lab, an ongoing MCS real-world experiment that involved more than 170 students of the University of Bologna for more than one year.

Current density profile evolution in JET

International Nuclear Information System (INIS)

Stubberfield, P.M.; Balet, B.; Campbell, D.; Challis, C.D.; Cordey, J.G.; O'Rourke, J.; Hammett, G.; Schmidt, G.L.

1989-01-01

Simulation studies have been made of the current density profile evolution in discharges where the bootstrap current is expected to be significant. The changes predicted in the total current profile have been confirmed by comparison with experimental results. (author) 8 refs., 6 figs

Predicting the educational performance of Isfahan University students of medical sciences based on their behaviour profile, mental health and demographic characteristic.

Science.gov (United States)

Samouei, Rahele; Fooladvand, Maryam; Janghorban, Shahla; Khorvash, Fariba

2015-01-01

The issue of students' academic failure is one of the most important educational, economic, and social issues. Cognizance of the factors related to academic downfall is so efficient in its prevention and control and leads to protecting governmental assets and labor force. In order to achieve this goal, this study intends to determine the predictive factors of the students' academic performance in Isfahan University of Medical Sciences in terms of their personality profile, mental health, and their demographic characteristics. This study was a descriptive-correlation study on 771 students who entered Isfahan University of Medical Sciences between 2005 and 2007. The information was gathered through using the students' educational and clinical files (for measuring personality characteristics and mental health) and SAMA Software (To get the mean scores). Minnesota Multiphasic Personality Inventory short form and General Health Questionnaire were used for collecting clinical data. The data were analyzed using SPSS 15 (stepwise regression coefficient, variance analysis, Student's t-test, and Spearman correlation coefficient). The results showed that the aforementioned students obtained a normal average for their personality profile and mental health indicators. Of all the reviewed variables, education, age, gender, depression, and hypochondria were the predictive factors of the students' educational performance. It could be concluded that some of the personality features, mental health indicators, and personality profile play such a significant role in the students' educational life that the disorder in any of them affects the students' educational performance and academic failure.

Modeling the wafer temperature profile in a multiwafer LPCVD furnace

Energy Technology Data Exchange (ETDEWEB)

Badgwell, T.A. [Rice Univ., Houston, TX (United States). Dept. of Chemical Engineering; Trachtenberg, I.; Edgar, T.F. [Univ. of Texas, Austin, TX (United States). Dept. of Chemical Engineering

1994-01-01

A mathematical model has been developed to predict wafer temperatures within a hot-wall multiwafer low pressure chemical vapor deposition (LPCVD) reactor. The model predicts both axial (wafer-to-wafer) and radial (across-wafer) temperature profiles. Model predictions compare favorably with in situ wafer temperature measurements described in an earlier paper. Measured axial and radial temperature nonuniformities are explained in terms of radiative heat-transfer effects. A simulation study demonstrates how changes in the outer tube temperature profile and reactor geometry affect wafer temperatures. Reactor design changes which could improve the wafer temperature profile are discussed.

Integrating milk metabolite profile information for the prediction of traditional milk traits based on SNP information for Holstein cows.

Directory of Open Access Journals (Sweden)

Nina Melzer

Full Text Available In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach. To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317 SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype.

Lyα Profile, Dust, and Prediction of Lyα Escape Fraction in Green Pea Galaxies

Science.gov (United States)

Yang, Huan; Malhotra, Sangeeta; Gronke, Max; Rhoads, James E.; Leitherer, Claus; Wofford, Aida; Jiang, Tianxing; Dijkstra, Mark; Tilvi, V.; Wang, Junxian

2017-08-01

We studied Lyman-α (Lyα) escape in a statistical sample of 43 Green Peas with HST/COS Lyα spectra. Green Peas are nearby star-forming galaxies with strong [O III]λ5007 emission lines. Our sample is four times larger than the previous sample and covers a much more complete range of Green Pea properties. We found that about two-thirds of Green Peas are strong Lyα line emitters with rest-frame Lyα equivalent width > 20 \\mathringA . The Lyα profiles of Green Peas are diverse. The Lyα escape fraction, defined as the ratio of observed Lyα flux to intrinsic Lyα flux, shows anti-correlations with a few Lyα kinematic features—both the blue peak and red peak velocities, the peak separations, and the FWHM of the red portion of the Lyα profile. Using properties measured from Sloan Digital Sky Survey optical spectra, we found many correlations—the Lyα escape fraction generally increases at lower dust reddening, lower metallicity, lower stellar mass, and higher [O III]/[O II] ratio. We fit their Lyα profiles with the H I shell radiative transfer model and found that the Lyα escape fraction is anti-correlated with the best-fit N H I . Finally, we fit an empirical linear relation to predict {f}{esc}{Lyα } from the dust extinction and Lyα red peak velocity. The standard deviation of this relation is about 0.3 dex. This relation can be used to isolate the effect of intergalactic medium (IGM) scatterings from Lyα escape and to probe the IGM optical depth along the line of sight of each z> 7 Lyα emission-line galaxy in the James Webb Space Telescope era.

Prediction of spur overlap time, radical yield profiles, and decomposition of trichloroethylene induced by various pulse types of electron beam

International Nuclear Information System (INIS)

Kim, D.-W.; Han, K.-C.; Lee, W.-K.; Ihm, S.-K.

1996-01-01

A kinetic model was suggested to compute the yield profiles of primary radicals generated from water radiolysis. For various cases including pulse radiolysis and steady irradiation time of spur overlap was computed in order to ensure homogeneity over the entire system. As a result, consistency to roughly first order kinetics was resulted for decomposition of 1 ppm trichloroethylene (TCE) and slight deviation from the linear model was predicted for 10 ppm TCE. (author)

HMMEditor: a visual editing tool for profile hidden Markov model

Directory of Open Access Journals (Sweden)

Cheng Jianlin

2008-03-01

Full Text Available Abstract Background Profile Hidden Markov Model (HMM is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. Results We develop a visual editor for profile Hidden Markov Models (HMMEditor. HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path. Conclusion HMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

Space potential, temperature, and density profile measurements on RENTOR

International Nuclear Information System (INIS)

Schoch, P.M.

1983-05-01

Radial profiles of the space potential, electron temperature, and density have been measured on RENTOR with a heavy-ion-beam probe. The potential profile has been compared to predictions from a stochastic magnetic field fluctuation theory, using the measured temperature and density profiles. The comparison shows strong qualitative agreement in that the potential is positive and the order of T/sub e//e. There is some quantitative disagreement in that the measured radial electric fields are somewhat smaller than the theoretical predictions. To facilitate this comparison, a detailed analysis of the possible errors has been completed

A model for predicting the radial power profile in a fuel pin

International Nuclear Information System (INIS)

Palmer, I.D.; Hesketh, K.W.; Jackson, P.A.

1983-01-01

A simple, fast running computer program for calculating radial power profiles, throughout life, in both standard and duplex fuel pellets for all types of thermal reactor has been developed. The code sub-divides the pellet into a number of annuli for each of which it solves for the concentrations of uranium and plutonium and hence calculates a mean inverse diffusion length. The diffusion equation is solved in terms of Bessel functions and the resulting flux profile multiplied by the concentration profiles to give a radial rating profile which is normalised to unity. The model shows good agreement with the results of detailed physics calculations for different thermal reactors over a wide burn-up range. Its incorporation into the HOTROD-4C and SLEUTH-SEER-77 fuel performance codes has led to a negligible increase in running times. (author)

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

Science.gov (United States)

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Injury Profile SIMulator, a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices, and the abiotic and biotic environment. I. Conceptual bases.

Directory of Open Access Journals (Sweden)

Jean-Noël Aubertot

Full Text Available The limitation of damage caused by pests (plant pathogens, weeds, and animal pests in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i develop, and to a lesser extent ii combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM strategies (vertical integration, there is a need for tools to help manage Injury Profiles (horizontal integration. Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator, a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat. In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop.

Injury Profile SIMulator, a qualitative aggregative modelling framework to predict crop injury profile as a function of cropping practices, and the abiotic and biotic environment. I. Conceptual bases.

Science.gov (United States)

Aubertot, Jean-Noël; Robin, Marie-Hélène

2013-01-01

The limitation of damage caused by pests (plant pathogens, weeds, and animal pests) in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i) develop, and to a lesser extent ii) combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM) strategies (vertical integration), there is a need for tools to help manage Injury Profiles (horizontal integration). Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator), a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat). In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop.

Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features

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Zhang Ya-Nan

2012-05-01

Full Text Available Abstract Background Adenosine-5′-triphosphate (ATP is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. Results In this paper, we propose a novel framework for predicting the proteins’ functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target’s knowledge and multiple sources of descriptors can be complementary. Conclusions Prediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.

Alcator C-Mod predictive modeling

International Nuclear Information System (INIS)

Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

2001-01-01

Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles

Cerebrospinal fluid cytokine profiles predict risk of early mortality and immune reconstitution inflammatory syndrome in HIV-associated cryptococcal meningitis.

Directory of Open Access Journals (Sweden)

Joseph N Jarvis

2015-04-01

Full Text Available Understanding the host immune response during cryptococcal meningitis (CM is of critical importance for the development of immunomodulatory therapies. We profiled the cerebrospinal fluid (CSF immune-response in ninety patients with HIV-associated CM, and examined associations between immune phenotype and clinical outcome. CSF cytokine, chemokine, and macrophage activation marker concentrations were assayed at disease presentation, and associations between these parameters and microbiological and clinical outcomes were examined using principal component analysis (PCA. PCA demonstrated a co-correlated CSF cytokine and chemokine response consisting primarily of Th1, Th2, and Th17-type cytokines. The presence of this CSF cytokine response was associated with evidence of increased macrophage activation, more rapid clearance of Cryptococci from CSF, and survival at 2 weeks. The key components of this protective immune-response were interleukin (IL-6 and interferon-γ, IL-4, IL-10 and IL-17 levels also made a modest positive contribution to the PC1 score. A second component of co-correlated chemokines was identified by PCA, consisting primarily of monocyte chemotactic protein-1 (MCP-1 and macrophage inflammatory protein-1α (MIP-1α. High CSF chemokine concentrations were associated with low peripheral CD4 cell counts and CSF lymphocyte counts and were predictive of immune reconstitution inflammatory syndrome (IRIS. In conclusion CSF cytokine and chemokine profiles predict risk of early mortality and IRIS in HIV-associated CM. We speculate that the presence of even minimal Cryptococcus-specific Th1-type CD4+ T-cell responses lead to increased recruitment of circulating lymphocytes and monocytes into the central nervous system (CNS, more effective activation of CNS macrophages and microglial cells, and faster organism clearance; while high CNS chemokine levels may predispose to over recruitment or inappropriate recruitment of immune cells to the CNS and

Evoked emotions predict food choice.

Science.gov (United States)

Dalenberg, Jelle R; Gutjar, Swetlana; Ter Horst, Gert J; de Graaf, Kees; Renken, Remco J; Jager, Gerry

2014-01-01

In the current study we show that non-verbal food-evoked emotion scores significantly improve food choice prediction over merely liking scores. Previous research has shown that liking measures correlate with choice. However, liking is no strong predictor for food choice in real life environments. Therefore, the focus within recent studies shifted towards using emotion-profiling methods that successfully can discriminate between products that are equally liked. However, it is unclear how well scores from emotion-profiling methods predict actual food choice and/or consumption. To test this, we proposed to decompose emotion scores into valence and arousal scores using Principal Component Analysis (PCA) and apply Multinomial Logit Models (MLM) to estimate food choice using liking, valence, and arousal as possible predictors. For this analysis, we used an existing data set comprised of liking and food-evoked emotions scores from 123 participants, who rated 7 unlabeled breakfast drinks. Liking scores were measured using a 100-mm visual analogue scale, while food-evoked emotions were measured using 2 existing emotion-profiling methods: a verbal and a non-verbal method (EsSense Profile and PrEmo, respectively). After 7 days, participants were asked to choose 1 breakfast drink from the experiment to consume during breakfast in a simulated restaurant environment. Cross validation showed that we were able to correctly predict individualized food choice (1 out of 7 products) for over 50% of the participants. This number increased to nearly 80% when looking at the top 2 candidates. Model comparisons showed that evoked emotions better predict food choice than perceived liking alone. However, the strongest predictive strength was achieved by the combination of evoked emotions and liking. Furthermore we showed that non-verbal food-evoked emotion scores more accurately predict food choice than verbal food-evoked emotions scores.

Metabolite profiles and the risk of developing diabetes.

Science.gov (United States)

Wang, Thomas J; Larson, Martin G; Vasan, Ramachandran S; Cheng, Susan; Rhee, Eugene P; McCabe, Elizabeth; Lewis, Gregory D; Fox, Caroline S; Jacques, Paul F; Fernandez, Céline; O'Donnell, Christopher J; Carr, Stephen A; Mootha, Vamsi K; Florez, Jose C; Souza, Amanda; Melander, Olle; Clish, Clary B; Gerszten, Robert E

2011-04-01

Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography-tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.

Theoretical prediction of pullout strengths for dental and orthopaedic screws with conical profile and buttress threads.

Science.gov (United States)

Shih, Kao-Shang; Hou, Sheng-Mou; Lin, Shang-Chih

2017-12-01

The pullout strength of a screw is an indicator of how secure bone fragments are being held in place. Such bone-purchasing ability is sensitive to bone quality, thread design, and the pilot hole, and is often evaluated by experimental and numerical methods. Historically, there are some mathematical formulae to simulate the screw withdrawal from the synthetic bone. There are great variations in screw specifications. However, extensive investigation of the correlation between experimental and analytical results has not been reported in literature. Referring to the literature formulae, this study aims to evaluate the differences in the calculated pullout strengths. The pullout tests of the surgical screws are measured and the sawbone is used as the testing block. The absolute errors and correlation coefficients of the experimental and analytical results are calculated as the comparison baselines of the formulae. The absolute error of the dental, traumatic, and spinal groups are 21.7%, 95.5%, and 37.0%, respectively. For the screws with a conical profile and/or tiny threads, the calculated and measured results are not well correlated. The formulae are not accurate indicators of the pullout strengths of the screws where the design parameters are slightly varied. However, the experimental and numerical results are highly correlated for the cylindrical screws. The pullout strength of a conical screw is higher than that of its counterpart, but all formulae consistently predict the opposite results. In general, the bony purchase of the buttress threads is securer than that of the symmetric thread. An absolute error of up to 51.4% indicates the theoretical results cannot predict the actual value of the pullout strength. Only thread diameter, pitch, and depth are considered in the investigated formulae. The thread profile and shape should be formulated to modify the slippage mechanism at the bone-screw interfaces and simulate the strength change in the squeezed bones

Androgen receptor profiling predicts prostate cancer outcome

NARCIS (Netherlands)

S. Stelloo (Suzan); E. Nevedomskaya (Ekaterina); H.G. van der Poel (Henk G.); J. de Jong (Jeroen); G.J.H.L. Leenders (Geert); G.W. Jenster (Guido); L. Wessels (Lodewyk); A.M. Bergman (Andries); W. Zwart (Wilbert)

2015-01-01

textabstractProstate cancer is the second most prevalent malignancy in men. Biomarkers for outcome prediction are urgently needed, so that high-risk patients could be monitored more closely postoperatively. To identify prognostic markers and to determine causal players in prostate cancer

Relationship of carbohydrates and lignin molecular structure spectral profiles to nutrient profile in newly developed oats cultivars and barley grain

Science.gov (United States)

Prates, Luciana Louzada; Refat, Basim; Lei, Yaogeng; Louzada-Prates, Mariana; Yu, Peiqiang

2018-01-01

The objectives of this study were to quantify the chemical profile and the magnitude of differences in the oat and barley grain varieties developed by Crop Development Centre (CDC) in terms of Cornell Net Carbohydrate Protein System (CNCPS) carbohydrate sub-fractions: CA4 (sugars), CB1 (starch), CB2 (soluble fibre), CB3 (available neutral detergent fibre - NDF), and CC (unavailable carbohydrate); to estimate the energy values; to detect the lignin and carbohydrate (CHO) molecular structure profiles in CDC Nasser and CDC Seabiscuit oat and CDC Meredith barley grains by using Fourier transform infrared attenuated total reflectance (FTIR-ATR); to develop a model to predict nutrient supply based on CHO molecular profile. Results showed that NDF, ADF and CHO were greater (P 0.05) for oat and barley grains as well as non-structural CHO. However, cellulosic compounds peak area and height were greater (P < 0.05) in oat than barley grains. Multiple regressions were determined to predict nutrient supply by using lignin and CHO molecular profiles. It was concluded that although there were some differences between oat and barley grains, CDC Nasser and CDC Meredith presented similarities related to chemical and molecular profiles, indicating that CDC Meredith barley could be replaced for CDC Nasser as ruminant feed. The FTIR was able to identify functional groups related to CHO molecular spectral in oat and barley grains and FTIR-ATR results could be used to predict nutrient supply in ruminant livestock systems.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

Directory of Open Access Journals (Sweden)

Bart V J Cuppen

Full Text Available In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA respond insufficiently to TNF-α inhibitors (TNFis. The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124. The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC, sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI. The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28, erythrocyte sedimentation rate (ESR or C-reactive protein (CRP. Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6, sn1-LPC(15:0, ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01. The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23 and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05. Our study established an accurate

Equilibrium shoreface profiles

DEFF Research Database (Denmark)

Aagaard, Troels; Hughes, Michael G

2017-01-01

Large-scale coastal behaviour models use the shoreface profile of equilibrium as a fundamental morphological unit that is translated in space to simulate coastal response to, for example, sea level oscillations and variability in sediment supply. Despite a longstanding focus on the shoreface...... profile and its relevance to predicting coastal response to changing environmental conditions, the processes and dynamics involved in shoreface equilibrium are still not fully understood. Here, we apply a process-based empirical sediment transport model, combined with morphodynamic principles to provide......; there is no tuning or calibration and computation times are short. It is therefore easily implemented with repeated iterations to manage uncertainty....

Prediction of temperature profile in oil wells

International Nuclear Information System (INIS)

Laderion, A.

2000-01-01

A mathematical model has been developed to predict the temperature distribution in well bores either offshore or inshore. It is incorporate the different activities encountered during drilling operations. Furthermore, the effect of drill collar and casings and bit rotating in a well during completion has been considered. The two dimensional approach is presented in the form of a computer program which is adopted for solution of the finite difference equations describing the heat transmission in the well bore in the form of a direct solution technique. The power law model has been selected for drilling mud and its indices have been calculated. Comparing measured data, recorded for a period of 82 hours during different activities in a drilling operation for 15/20 A-4, an exploration well in the Central North Sea with calculated results, show there is a good agreement between the prediction and measured temperatures in the well bore

Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer: a study of gene expression in the DBCG82bc cohort.

Science.gov (United States)

Tramm, Trine; Mohammed, Hayat; Myhre, Simen; Kyndi, Marianne; Alsner, Jan; Børresen-Dale, Anne-Lise; Sørlie, Therese; Frigessi, Arnoldo; Overgaard, Jens

2014-10-15

To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into "high LRR risk" and "low LRR risk" groups. PMRT significantly reduced risk of LRR in "high LRR risk" patients, whereas "low LRR risk" patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT. ©2014 American Association for Cancer Research.

Metabolite profiles and the risk of developing diabetes

OpenAIRE

2011-01-01

Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines, and other polar metabolites were profiled in baseline specimens using liquid chromatography-tandem mass spectrometry. Cases and controls were matched for age, body mass index and fasting g...

Metabolite Profiles of Diabetes Risk

OpenAIRE

Gerszten, Robert E.

2013-01-01

Metabolic diseases present particular difficulty for clinicians because they are often present for years before becoming clinically apparent. We investigated whether metabolite profiles can predict the development of diabetes in the Framingham Heart Study. Five branched-chain and aromatic amino acids had highly-significant associations with future diabetes, while a combination of three amino acids strongly predicted future diabetes by up to 12 years (>5-fold increased risk for individuals in ...

Pulverized coal devolatilization prediction

International Nuclear Information System (INIS)

Rojas, Andres F; Barraza, Juan M

2008-01-01

The aim of this study was to predict the two bituminous coals devolatilization at low rate of heating (50 Celsius degrade/min), with program FG-DVC (functional group Depolymerization. Vaporization and crosslinking), and to compare the devolatilization profiles predicted by program FG-DVC, which are obtained in the thermogravimetric analyzer. It was also study the volatile liberation at (10 4 k/s) in a drop-tube furnace. The tar, methane, carbon monoxide, and carbon dioxide, formation rate profiles, and the hydrogen, oxygen, nitrogen and sulphur, elemental distribution in the devolatilization products by FG-DVC program at low rate of heating was obtained; and the liberation volatile and R factor at high rate of heating was calculated. it was found that the program predicts the bituminous coals devolatilization at low rate heating, at high rate heating, a volatile liberation around 30% was obtained

Parametric dependencies of JET electron temperature profiles

Energy Technology Data Exchange (ETDEWEB)

Schunke, B [Commission of the European Communities, Abingdon (United Kingdom). JET Joint Undertaking; Imre, K; Riedel, K [New York Univ., NY (United States)

1994-07-01

The JET Ohmic, L-Mode and H-Mode electron temperature profiles obtained from the LIDAR Thomson Scattering Diagnostic are parameterized in terms of the normalized flux parameter and a set of the engineering parameters like plasma current, toroidal field, line averages electron density... It is shown that the electron temperature profiles fit a log-additive model well. It is intended to use the same model to predict the profile shape for D-T discharges in JET and in ITER. 2 refs., 5 figs.

Geographic profiling survey : a preliminary examination of geographic profilers' views and experiences

NARCIS (Netherlands)

Emeno, Karla; Bennell, Craig; Snook, Brent; Taylor, Paul Jonathon

Geographic profiling (GP) is an investigative technique that involves predicting a serial offender?s home location (or some other anchor point) based on where he or she committed a crime. Although the use of GP in police investigations appears to be on the rise, little is known about the procedure

A predictive model to evaluate the impact of the cooling profile on growth of psychrotrophic bacteria in raw milk from conventional and robotic milking.

Science.gov (United States)

Christiansson, Anders

2017-08-01

This Research Communication explores the usefulness of predictive modelling to explain bacterial behaviour during cooling. A simple dynamic lag phase model was developed and validated. The model takes into account the effect of the cooling profile on the lag phase and growth in bulk tank milk. The time before the start of cooling was the most critical and should not exceed 1 h. The cooling rate between 30 and approximately 10 °C was the second most critical period. Cooling from 30 to 10 °C within 2 h ensured minimal growth of psychrotrophic bacteria in the milk. The cooling rate between 10 and 4 °C (the slowest phase of cooling) was of surprisingly little importance. Given a normal cooling profile to 10 °C, several hours of prolonged cooling time made practically no difference in psychrotrophic counts. This behaviour can be explained by the time/temperature dependence of the work needed by the bacteria to complete the lag phase at low temperature. For milk quality advisors, it is important to know that slow cooling below 10 °C does not result in high total counts of bacteria. In practice, slow cooling is occasionally found at farms with robotic milking. However, when comparing psychrotrophic growth in bulk milk tanks designed for robotic milking or conventional milking, the model predicted less growth for robotic milking for identical cooling profiles. It is proposed that due to the different rates of milk entering the tank, fewer bacteria will exit the lag phase during robotic milking and they will be more diluted than in conventional milking systems. At present, there is no international standard that specifies the cooling profile in robotic systems. The information on the insignificant effect of the cooling rate below 10 °C may be useful in the development of a standard.

Biochemical Profile of Heritage and Modern Apple Cultivars and Application of Machine Learning Methods To Predict Usage, Age, and Harvest Season.

Science.gov (United States)

Anastasiadi, Maria; Mohareb, Fady; Redfern, Sally P; Berry, Mark; Simmonds, Monique S J; Terry, Leon A

2017-07-05

The present study represents the first major attempt to characterize the biochemical profile in different tissues of a large selection of apple cultivars sourced from the United Kingdom's National Fruit Collection comprising dessert, ornamental, cider, and culinary apples. Furthermore, advanced machine learning methods were applied with the objective to identify whether the phenolic and sugar composition of an apple cultivar could be used as a biomarker fingerprint to differentiate between heritage and mainstream commercial cultivars as well as govern the separation among primary usage groups and harvest season. A prediction accuracy of >90% was achieved with the random forest method for all three models. The results highlighted the extraordinary phytochemical potency and unique profile of some heritage, cider, and ornamental apple cultivars, especially in comparison to more mainstream apple cultivars. Therefore, these findings could guide future cultivar selection on the basis of health-promoting phytochemical content.

In Silico Analysis of Microarray-Based Gene Expression Profiles Predicts Tumor Cell Response to Withanolides

Directory of Open Access Journals (Sweden)

Thomas Efferth

2012-05-01

Full Text Available Withania somnifera (L. Dunal (Indian ginseng, winter cherry, Solanaceae is widely used in traditional medicine. Roots are either chewed or used to prepare beverages (aqueous decocts. The major secondary metabolites of Withania somnifera are the withanolides, which are C-28-steroidal lactone triterpenoids. Withania somnifera extracts exert chemopreventive and anticancer activities in vitro and in vivo. The aims of the present in silico study were, firstly, to investigate whether tumor cells develop cross-resistance between standard anticancer drugs and withanolides and, secondly, to elucidate the molecular determinants of sensitivity and resistance of tumor cells towards withanolides. Using IC50 concentrations of eight different withanolides (withaferin A, withaferin A diacetate, 3-azerininylwithaferin A, withafastuosin D diacetate, 4-B-hydroxy-withanolide E, isowithanololide E, withafastuosin E, and withaperuvin and 19 established anticancer drugs, we analyzed the cross-resistance profile of 60 tumor cell lines. The cell lines revealed cross-resistance between the eight withanolides. Consistent cross-resistance between withanolides and nitrosoureas (carmustin, lomustin, and semimustin was also observed. Then, we performed transcriptomic microarray-based COMPARE and hierarchical cluster analyses of mRNA expression to identify mRNA expression profiles predicting sensitivity or resistance towards withanolides. Genes from diverse functional groups were significantly associated with response of tumor cells to withaferin A diacetate, e.g. genes functioning in DNA damage and repair, stress response, cell growth regulation, extracellular matrix components, cell adhesion and cell migration, constituents of the ribosome, cytoskeletal organization and regulation, signal transduction, transcription factors, and others.

Quantifying and Predicting Three-Dimensional Heterogeneity in Transient Storage Using Roving Profiling

Science.gov (United States)

Kaplan, D. A.; Reaver, N.; Hensley, R. T.; Cohen, M. J.

2017-12-01

Hydraulic transport is an important component of nutrient spiraling in streams. Quantifying conservative solute transport is a prerequisite for understanding the cycling and fate of reactive solutes, such as nutrients. Numerous studies have modeled solute transport within streams using the one-dimensional advection, dispersion and storage (ADS) equation calibrated to experimental data from tracer experiments. However, there are limitations to the information about in-stream transient storage that can be derived from calibrated ADS model parameters. Transient storage (TS) in the ADS model is most often modeled as a single process, and calibrated model parameters are "lumped" values that are the best-fit representation of multiple real-world TS processes. In this study, we developed a roving profiling method to assess and predict spatial heterogeneity of in-stream TS. We performed five tracer experiments on three spring-fed rivers in Florida (USA) using Rhodamine WT. During each tracer release, stationary fluorometers were deployed to measure breakthrough curves for multiple reaches within the river. Teams of roving samplers moved along the rivers measuring tracer concentrations at various locations and depths within the reaches. A Bayesian statistical method was used to calibrate the ADS model to the stationary breakthrough curves, resulting in probability distributions for both the advective and TS zone as a function of river distance and time. Rover samples were then assigned a probability of being from either the advective or TS zone by comparing measured concentrations to the probability distributions of concentrations in the ADS advective and TS zones. A regression model was used to predict the probability of any in-stream position being located within the advective versus TS zone based on spatiotemporal predictors (time, river position, depth, and distance from bank) and eco-geomorphological feature (eddies, woody debris, benthic depressions, and aquatic

EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles.

Science.gov (United States)

Dittmar, W James; McIver, Lauren; Michalak, Pawel; Garner, Harold R; Valdez, Gregorio

2014-07-01

The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server 'EvoCor', to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Improving integrative searching of systems chemical biology data using semantic annotation.

Science.gov (United States)

Chen, Bin; Ding, Ying; Wild, David J

2012-03-08

Systems chemical biology and chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued. We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology. Chem2Bio2OWL is available at http://chem2bio2rdf.org/owl. The document is available at http://chem2bio2owl.wikispaces.com.

Performance of CT ASPECTS and Collateral Score in Risk Stratification: Can Target Perfusion Profiles Be Predicted without Perfusion Imaging?

Science.gov (United States)

Dehkharghani, S; Bammer, R; Straka, M; Bowen, M; Allen, J W; Rangaraju, S; Kang, J; Gleason, T; Brasher, C; Nahab, F

2016-08-01

Endovascular trials suggest that revascularization benefits a subset of acute ischemic stroke patients with large-artery occlusion and small-core infarct volumes. The objective of our study was to identify thresholds of noncontrast CT-ASPECTS and collateral scores on CT angiography that best predict ischemic core volume thresholds quantified by CT perfusion among patients with acute ischemic stroke. Fifty-four patients with acute ischemic stroke (collateral score of 3 had 100% specificity for identifying patients with a CBF core volume of ≤50 mL. NCCT-ASPECTS of ≤6 had 100% specificity for identifying patients with a CBF core volume of >50 mL. In our cohort, 44 (81%) patients had an NCCT-ASPECTS of ≥9, a CTA collateral score of 3, or an NCCT-ASPECTS of ≤6. Using an NCCT-ASPECTS of ≥9 or a CTA collateral score of 3 best predicts CBF core volume infarct of ≤50 mL, while an NCCT-ASPECTS of ≤6 best predicts a CBF core volume infarct of >50 mL. Together these thresholds suggest that a specific population of patients with acute ischemic stroke not meeting such profiles may benefit most from CTP imaging to determine candidacy for revascularization. © 2016 by American Journal of Neuroradiology.

Prediction of selective estrogen receptor beta agonist using open data and machine learning approach.

Science.gov (United States)

Niu, Ai-Qin; Xie, Liang-Jun; Wang, Hui; Zhu, Bing; Wang, Sheng-Qi

2016-01-01

Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects. Herein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods. The chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists. These results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists.

Comparison of midlatitude ionospheric F region peak parameters and topside Ne profiles from IRI2012 model prediction with ground-based ionosonde and Alouette II observations

Science.gov (United States)

Gordiyenko, G. I.; Yakovets, A. F.

2017-07-01

The ionospheric F2 peak parameters recorded by a ground-based ionosonde at the midlatitude station Alma-Ata [43.25N, 76.92E] were compared with those obtained using the latest version of the IRI model (http://omniweb.gsfc.nasa.gov/vitmo/iri2012_vitmo.html). It was found that for the Alma-Ata (Kazakhstan) location, the IRI2012 model describes well the morphology of seasonal and diurnal variations of the ionospheric critical frequency (foF2) and peak density height (hmF2) monthly medians. The model errors in the median foF2 prediction (percentage deviations between the median foF2 values and their model predictions) were found to vary approximately in the range from about -20% to 34% and showed a stable overestimation in the median foF2 values for daytime in January and July and underestimation for day- and nighttime hours in the equinoctial months. The comparison between the ionosonde hmF2 and IRI results clearly showed that the IRI overestimates the nighttime hmF2 values for March and September months, and the difference is up to 30 km. The daytime Alma-Ata hmF2 data were found to be close to the IRI predictions (deviations are approximately ±10-15 km) in winter and equinoctial months, except in July when the observed hmF2 values were much more (from approximately 50-200 km). The comparison between the Alouette foF2 data and IRI predictions showed mixed results. In particular, the Alouette foF2 data showed a tendency to be overestimated for daytime in winter months similar to the ionosonde data; however, the overestimated foF2 values for nighttime in the autumn equinox were in disagreement with the ionosonde observations. There were large deviations between the observed hmF2 values and their model predictions. The largest deviations were found during winter and summer (up to -90 km). The comparison of the Alouette II electron density profiles with those predicted by the adapted IRI2012 model in the altitude range hmF2 of the satellite position showed a great

Diagnostic value of the flow profile in the distal descending aorta by phase-contrast magnetic resonance for predicting severe coarctation of the aorta.

Science.gov (United States)

Muzzarelli, Stefano; Ordovas, Karen Gomes; Hope, Michael D; Meadows, Jeffery J; Higgins, Charles B; Meadows, Alison Knauth

2011-06-01

To compare aortic flow profiles at the level of the proximal descending (PDAo) and distal descending aorta (DDAo) in patients investigated for coarctation of the aorta (CoA), and compare their respective diagnostic value for predicting severe CoA. Diastolic flow decay in the PDAo predicts severe CoA, but flow measurements at this level are limited by flow turbulence, aliasing, and stent-related artifacts. We studied 49 patients evaluated for CoA with phase contrast magnetic resonance imaging (PC-MRI). Parameters of diastolic flow decay in the PDAo and DDAo were compared. Their respective diagnostic value was compared with the standard reference of transcatheter peak gradient ≥20 mmHg. Flow measurement in the PDAo required repeated acquisition with adjustment of encoding velocity or location of the imaging plane in 69% of patients; measurement in the DDAo was achieved in single acquisition in all cases. Parameters of diastolic flow decay in the PDAo and DDAo, including rate-corrected (RC) deceleration time and RC flow deceleration yielded a good correlation (r = 0.78; P RC deceleration time at DDAo (sensitivity 85%, specificity 85%). Characterization of aortic flow profiles at the DDAo offers a quick and reliable noninvasive means of assessing hemodynamically significant CoA. Copyright © 2011 Wiley-Liss, Inc.

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

Directory of Open Access Journals (Sweden)

Karacali Bilge

2007-10-01

Full Text Available Abstract Background Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a all genes on the microarray platform and b a list of known disease-related genes (a priori selection. We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. Results Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform. Conclusion Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine

Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas.

Science.gov (United States)

Gihr, Georg Alexander; Horvath-Rizea, Diana; Kohlhof-Meinecke, Patricia; Ganslandt, Oliver; Henkes, Hans; Richter, Cindy; Hoffmann, Karl-Titus; Surov, Alexey; Schob, Stefan

2018-06-14

Meningiomas are the most frequently diagnosed intracranial masses, oftentimes requiring surgery. Especially procedure-related morbidity can be substantial, particularly in elderly patients. Hence, reliable imaging modalities enabling pretherapeutic prediction of tumor grade, growth kinetic, realistic prognosis, and-as a consequence-necessity of surgery are of great value. In this context, a promising diagnostic approach is advanced analysis of magnetic resonance imaging data. Therefore, our study investigated whether histogram profiling of routinely acquired postcontrast T1-weighted images is capable of separating low-grade from high-grade lesions and whether histogram parameters reflect Ki-67 expression in meningiomas. Pretreatment T1-weighted postcontrast volumes of 44 meningioma patients were used for signal intensity histogram profiling. WHO grade, tumor volume, and Ki-67 expression were evaluated. Comparative and correlative statistics investigating the association between histogram profile parameters and neuropathology were performed. None of the investigated histogram parameters revealed significant differences between low-grade and high-grade meningiomas. However, significant correlations were identified between Ki-67 and the histogram parameters skewness and entropy as well as between entropy and tumor volume. Contrary to previously reported findings, pretherapeutic postcontrast T1-weighted images can be used to predict growth kinetics in meningiomas if whole tumor histogram analysis is employed. However, no differences between distinct WHO grades were identifiable in out cohort. As a consequence, histogram analysis of postcontrast T1-weighted images is a promising approach to obtain quantitative in vivo biomarkers reflecting the proliferative potential in meningiomas. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

A Danish Profiling System

DEFF Research Database (Denmark)

Rosholm, Michael; Staghøj, Jonas; Svarer, Michael

2006-01-01

This paper describes the statistical model used for profiling new unemployed workers in Denmark. When a worker – during his or her first six months in unemployment – enters the employment office for the first time, this model predicts whether or not he or she will be unemployed for more than six ...

A one-dimensional Fickian model to predict the Ga depth profiles in three-stage Cu(In,Ga)Se2

International Nuclear Information System (INIS)

Rodriguez-Alvarez, H.; Mainz, R.; Sadewasser, S.

2014-01-01

We present a one-dimensional Fickian model that predicts the formation of a double Ga gradient during the fabrication of Cu(In,Ga)Se 2 thin films by three-stage thermal co-evaporation. The model is based on chemical reaction equations, structural data, and effective Ga diffusivities. In the model, the Cu(In,Ga)Se 2 surface is depleted from Ga during the deposition of Cu-Se in the second deposition stage, leading to an accumulation of Ga near the back contact. During the third deposition stage, where In-Ga-Se is deposited at the surface, the atomic fluxes within the growing layer are inverted. This results in the formation of a double Ga gradient within the Cu(In,Ga)Se 2 layer and reproduces experimentally observed Ga distributions. The final shape of the Ga depth profile strongly depends on the temperatures, times and deposition rates used. The model is used to evaluate possible paths to flatten the marked Ga depth profile that is obtained when depositing at low substrate temperatures. We conclude that inserting Ga during the second deposition stage is an effective way to achieve this.

Profiling of plasma metabolites in canine oral melanoma using gas chromatography-mass spectrometry.

Science.gov (United States)

Kawabe, Mifumi; Baba, Yuta; Tamai, Reo; Yamamoto, Ryohei; Komori, Masayuki; Mori, Takashi; Takenaka, Shigeo

2015-08-01

Malignant melanoma is one of the most common and aggressive tumors in the oral cavity of dog. The tumor has a poor prognosis, and methods for diagnosis and prediction of prognosis after treatment are required. Here, we examined metabolite profiling using gas chromatography-mass spectrometry (GC-MS) for development of a discriminant model for evaluation of prognosis. Metabolite profiles were evaluated in healthy and melanoma plasma samples using orthogonal projection to latent structure using discriminant analysis (OPLS-DA). Cases that were predicted to be healthy using the OPLS discriminant model had no advanced lesions after radiation therapy. These results indicate that metabolite profiling may be useful in diagnosis and prediction of prognosis of canine malignant melanoma.

Application of Depth-Averaged Velocity Profile for Estimation of Longitudinal Dispersion in Rivers

Directory of Open Access Journals (Sweden)

Mohammad Givehchi

2010-01-01

Full Text Available River bed profiles and depth-averaged velocities are used as basic data in empirical and analytical equations for estimating the longitudinal dispersion coefficient which has always been a topic of great interest for researchers. The simple model proposed by Maghrebi is capable of predicting the normalized isovel contours in the cross section of rivers and channels as well as the depth-averaged velocity profiles. The required data in Maghrebi’s model are bed profile, shear stress, and roughness distributions. Comparison of depth-averaged velocities and longitudinal dispersion coefficients observed in the field data and those predicted by Maghrebi’s model revealed that Maghrebi’s model had an acceptable accuracy in predicting depth-averaged velocity.

Study on a Cavitating Hydrofoil having a Practical Blade Profile Shape.

Science.gov (United States)

1980-10-31

OR Isuet.. #Ad. It M*4*etan d idsmctty 4Y 44"Aa numbs#) cavitating foil experiment theory partially cavitating flow supercavitating flow 4 . AIS7XACI...of a modi- fied Tulin two-term camber with a blunt trailing edge. This profile shape was taken after the cross-section profile of a supercavitating ...prediction theory, might have caused an erroneous prediction for the thrust coefficient and efficiency of this supercavitating propeller. In order to

In Silico Approaches for Predicting Adme Properties

Science.gov (United States)

Madden, Judith C.

A drug requires a suitable pharmacokinetic profile to be efficacious in vivo in humans. The relevant pharmacokinetic properties include the absorption, distribution, metabolism, and excretion (ADME) profile of the drug. This chapter provides an overview of the definition and meaning of key ADME properties, recent models developed to predict these properties, and a guide as to how to select the most appropriate model(s) for a given query. Many tools using the state-of-the-art in silico methodology are now available to users, and it is anticipated that the continual evolution of these tools will provide greater ability to predict ADME properties in the future. However, caution must be exercised in applying these tools as data are generally available only for "successful" drugs, i.e., those that reach the marketplace, and little supplementary information, such as that for drugs that have a poor pharmacokinetic profile, is available. The possibilities of using these methods and possible integration into toxicity prediction are explored.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

Science.gov (United States)

Speck-Planche, Alejandro; Cordeiro, M N D S

2014-02-10

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

The relationship between fetal biophysical profile and cord blood PH

Directory of Open Access Journals (Sweden)

Valadan M

2009-02-01

Full Text Available "nBackground: The Biophysical Profile (BPP is a noninvasive test that predicts the presence or absence of fetal asphyxia and, ultimately, the risk of fetal death in the antenatal period. Intervention on the basis of an abnormal biophysical profile result has been reported to yield a significant reduction in prenatal mortality, and an association exists between biophysical profile scoring and a decreased cerebral palsy rate in a given population. The BPP evaluates five characteristics: fetal movement, tone, breathing, heart reactivity, and amniotic fluid (AF volume estimation. The purpose of study was to determine whether there are different degree of acidosis at which the biophysical activity (acute marker are affected. "nMethods: In a prospective study of 140 patients undergoing cesarean section before onset of labor, the fetal biophysical profile was performed 24h before the time of cesarean and was matched with cord arterial PH that was obtained from a cord segment (10-20cm that was double clamped after delivery of newborn. (using cord arterial PH less than 7.20 for the diagnosis of acidosis. "nResults: The fetal biophysical profile was found to have a significant relationship with umbilical blood PH. The sensitivity, specificity, positive predictive value, negative predictive value of fetal biophysical profile score were: 88.9%, 88.6%, 50%, 98.1%. "nConclusion: The first manifestations of fetal acidosis are nonreactive nonstress testing and fetal breathing loss; in advanced acidemia fetal movements and fetal tone are compromised. A protocol of antepartum fetal evaluation is suggested based upon the individual biophysical components rather than the score alone.

Sensitivity of the urban airshed model to mixing height profiles

Energy Technology Data Exchange (ETDEWEB)

Rao, S.T.; Sistla, G.; Ku, J.Y.; Zhou, N.; Hao, W. [New York State Dept. of Environmental Conservation, Albany, NY (United States)

1994-12-31

The United States Environmental Protection Agency (USEPA) has recommended the use of the Urban Airshed Model (UAM), a grid-based photochemical model, for regulatory applications. One of the important parameters in applications of the UAM is the height of the mixed layer or the diffusion break. In this study, we examine the sensitivity of the UAM-predicted ozone concentrations to (a) a spatially invariant diurnal mixing height profile, and (b) a spatially varying diurnal mixing height profile for a high ozone episode of July 1988 for the New York Airshed. The 1985/88 emissions inventory used in the EPA`s Regional Oxidant Modeling simulations has been regridded for this study. Preliminary results suggest that the spatially varying case yields a higher peak ozone concentrations compared to the spatially invariant mixing height simulation, with differences in the peak ozone ranging from a few ppb to about 40 ppb for the days simulated. These differences are attributed to the differences in the shape of the mixing height profiles and its rate of growth during the morning hours when peak emissions are injected into the atmosphere. Examination of the impact of emissions reductions associated with these two mixing height profiles indicates that NO{sub x}-focussed controls provide a greater change in the predicted ozone peak under spatially invariant mixing heights than under the spatially varying mixing height profile. On the other hand, VOC-focussed controls provide a greater change in the predicted peak ozone levels under spatially varying mixing heights than under the spatially invariant mixing height profile.

Predicting the impact of vegetations in open channels with different distributaries' operations on water surface profile using artificial neural networks

International Nuclear Information System (INIS)

Abdeen, Mostafa A. M.

2008-01-01

Most of the open water irrigation channels in Egypt suffer from the infestation of aquatic weeds, especially the submerged ones that cause numerous hydraulic problems for the open channels themselves and their water distributaries such as increasing water losses, obstructing water flow, and reducing channels' water distribution efficiencies. Accurate simulation and prediction of flow behavior in such channels is very essential for water distribution decision makers. Artificial neural networks (ANN) have proven to be very successful in the simulation of several physical phenomena, in general, and in the water research field in particular. Therefore, the current study aims towards introducing the utilization of ANN in simulating the impact of vegetation in main open channel, which supplies water to different distributaries, on the water surface profile in this main channel. Specifically, the study, presented in the current paper utilizes ANN technique for the development of various models to simulate the impact of different submerged weeds' densities, different flow discharges, and different distributaries operation scheduling on the water surface profile in an experimental main open channel that supplies water to different distributaries. In the investigated experiment, the submerged weeds were simulated as branched flexible elements. The investigated experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results showed that the ANN technique is very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds' cases that were considered in the ANN development process

Prediction of wall shear stresses in transitional boundary layers using near-wall mean velocity profiles

International Nuclear Information System (INIS)

Jeon, Woo Pyung; Shin, Sung Ho; Kang, Shin Hyoung

2000-01-01

The local wall shear stress in transitional boundary layer was estimated from the near-wall mean velocity data using the principle of Computational Preston tube Method(CPM). The previous DNS and experimental databases of transitional boundary layers were used to demonstrate the accuracy of the method and to provide the applicable range of wall unit y + . The skin friction coefficients predicted by the CPM agreed well with those from previous studies. To reexamine the applicability of the CPM, near-wall hot-wire measurements were conducted in developing transitional boundary layers on a flat plate with different freestream turbulence intensities. The intermittency profiles across the transitional boundary layers were reasonably obtained from the conditional sampling technique. An empirical correlation between the representative intermittency near the wall and the free parameter K 1 of the extended wall function of CPM has been newly proposed using the present and other experimental data. The CPM has been verified as a useful tool to measure the wall shear stress in transitional boundary layer with reasonable accuracy

The child behavior checklist dysregulation profile predicts adolescent DSM-5 pathological personality traits 4 years later.

Science.gov (United States)

De Caluwé, Elien; Decuyper, Mieke; De Clercq, Barbara

2013-07-01

Emotional dysregulation in childhood has been associated with various forms of later psychopathology, although no studies have investigated the personality related adolescent outcomes associated with early emotional dysregulation. The present study uses a typological approach to examine how the child behavior checklist-dysregulation profile (CBCL-DP) predicts DSM-5 pathological personality traits (as measured with the personality inventory for the diagnostic and statistical manual of mental disorders 5 or PID-5 by Krueger et al. (Psychol Med 2012)) across a time span of 4 years in a sample of 243 children aged 8-14 years (57.2 % girls). The results showed that children assigned to the CBCL-DP class are at risk for elevated scores on a wide range of DSM-5 personality pathology features, including higher scores on hostility, risk taking, deceitfulness, callousness, grandiosity, irresponsibility, impulsivity and manipulativeness. These results are discussed in the context of identifying early manifestations of persistent regulation problems, because of their enduring impact on a child's personality development.

Prediction of graft-versus-host disease in humans by donor gene-expression profiling.

Directory of Open Access Journals (Sweden)

Chantal Baron

2007-01-01

Full Text Available BACKGROUND: Graft-versus-host disease (GVHD results from recognition of host antigens by donor T cells following allogeneic hematopoietic cell transplantation (AHCT. Notably, histoincompatibility between donor and recipient is necessary but not sufficient to elicit GVHD. Therefore, we tested the hypothesis that some donors may be "stronger alloresponders" than others, and consequently more likely to elicit GVHD. METHODS AND FINDINGS: To this end, we measured the gene-expression profiles of CD4(+ and CD8(+ T cells from 50 AHCT donors with microarrays. We report that pre-AHCT gene-expression profiling segregates donors whose recipient suffered from GVHD or not. Using quantitative PCR, established statistical tests, and analysis of multiple independent training-test datasets, we found that for chronic GVHD the "dangerous donor" trait (occurrence of GVHD in the recipient is under polygenic control and is shaped by the activity of genes that regulate transforming growth factor-beta signaling and cell proliferation. CONCLUSIONS: These findings strongly suggest that the donor gene-expression profile has a dominant influence on the occurrence of GVHD in the recipient. The ability to discriminate strong and weak alloresponders using gene-expression profiling could pave the way to personalized transplantation medicine.

Improving integrative searching of systems chemical biology data using semantic annotation

Directory of Open Access Journals (Sweden)

Chen Bin

2012-03-01

Full Text Available Abstract Background Systems chemical biology and chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued. Results We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i simplifies the process of building SPARQL queries, (ii enables useful new kinds of queries on the data and (iii makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology. Availability Chem2Bio2OWL is available at http://chem2bio2rdf.org/owl. The document is available at http://chem2bio2owl.wikispaces.com.

LocTree3 prediction of localization

DEFF Research Database (Denmark)

Goldberg, T.; Hecht, M.; Hamp, T.

2014-01-01

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria a...

GAUSSIAN RANDOM FIELD: PHYSICAL ORIGIN OF SERSIC PROFILES

International Nuclear Information System (INIS)

Cen, Renyue

2014-01-01

While the Sersic profile family provides adequate fits for the surface brightness profiles of observed galaxies, its physical origin is unknown. We show that if the cosmological density field is seeded by random Gaussian fluctuations, as in the standard cold dark matter model, galaxies with steep central profiles have simultaneously extended envelopes of shallow profiles in the outskirts, whereas galaxies with shallow central profiles are accompanied by steep density profiles in the outskirts. These properties are in accord with those of the Sersic profile family. Moreover, galaxies with steep central profiles form their central regions in smaller denser subunits that possibly merge subsequently, which naturally leads to the formation of bulges. In contrast, galaxies with shallow central profiles form their central regions in a coherent fashion without significant substructure, a necessary condition for disk galaxy formation. Thus, the scenario is self-consistent with respect to the correlation between observed galaxy morphology and the Sersic index. We further predict that clusters of galaxies should display a similar trend, which should be verifiable observationally

Using an Acculturation-Stress-Resilience Framework to Explore Latent Profiles of Latina/o Language Brokers.

Science.gov (United States)

Kam, Jennifer A; Marcoulides, Katerina M; Merolla, Andy J

2017-12-01

With survey data from 243 Latina/o early adolescent language brokers, latent profile analyses were conducted to identify different types (i.e., profiles) of brokers. Profiles were based on how often Latina/o early adolescents brokered for family members, as well as their levels of family-based acculturation stress, negative brokering beliefs, parentification, and positive brokering beliefs. Three brokering profiles emerged: (1) infrequent-ambivalents, (2) occasional-moderates, and (3) parentified-endorsers. Profile membership was significantly predicted by ethnic identification and brokering in a medical context. Respect, brokering at school, and brokering at home did not significantly predict profile membership. In addition, parentified-endorsers had more frequent perceived ethnic/racial discrimination and depressive symptoms than other profiles. In contrast, infrequent-ambivalents engaged in risky behaviors less frequently than other profiles. © 2017 The Authors. Journal of Research on Adolescence © 2017 Society for Research on Adolescence.

Deriving profiles of incident and scattered neutrons for TOF experiments with the spallation sources

International Nuclear Information System (INIS)

Watanabe, Hidehiro

1993-01-01

A formula that closely matches the incident profile of epi-thermal and thermal neutrons for time of flight experiments carried out with a spallation neutron source and moderator scheme is derived based on the slowing-down and diffusing-out processes in a moderator. This analytical description also enables us to predict burst-function profiles; these profiles are verified by a comparison with a diffraction pattern. The limits of the analytical model are discussed through the predictable peak position shift brought about by the slowing-down process. (orig.)

Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

Science.gov (United States)

Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

2016-02-01

Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

A one-dimensional Fickian model to predict the Ga depth profiles in three-stage Cu(In,Ga)Se{sub 2}

Energy Technology Data Exchange (ETDEWEB)

Rodriguez-Alvarez, H., E-mail: humberto.rodriguez@helmholtz-berlin.de [International Iberian Nanotechnology Laboratory, Avenida Mestre Jose Veiga s/n, 4715-330 Braga (Portugal); Helmholtz-Zentrum Berlin, Hahn-Meitner Platz 1, 14109 Berlin (Germany); Mainz, R. [Helmholtz-Zentrum Berlin, Hahn-Meitner Platz 1, 14109 Berlin (Germany); Sadewasser, S. [International Iberian Nanotechnology Laboratory, Avenida Mestre Jose Veiga s/n, 4715-330 Braga (Portugal)

2014-05-28

We present a one-dimensional Fickian model that predicts the formation of a double Ga gradient during the fabrication of Cu(In,Ga)Se{sub 2} thin films by three-stage thermal co-evaporation. The model is based on chemical reaction equations, structural data, and effective Ga diffusivities. In the model, the Cu(In,Ga)Se{sub 2} surface is depleted from Ga during the deposition of Cu-Se in the second deposition stage, leading to an accumulation of Ga near the back contact. During the third deposition stage, where In-Ga-Se is deposited at the surface, the atomic fluxes within the growing layer are inverted. This results in the formation of a double Ga gradient within the Cu(In,Ga)Se{sub 2} layer and reproduces experimentally observed Ga distributions. The final shape of the Ga depth profile strongly depends on the temperatures, times and deposition rates used. The model is used to evaluate possible paths to flatten the marked Ga depth profile that is obtained when depositing at low substrate temperatures. We conclude that inserting Ga during the second deposition stage is an effective way to achieve this.

Sleep-wake profiles predict longitudinal changes in manic symptoms and memory in young people with mood disorders.

Science.gov (United States)

Robillard, Rébecca; Hermens, Daniel F; Lee, Rico S C; Jones, Andrew; Carpenter, Joanne S; White, Django; Naismith, Sharon L; Southan, James; Whitwell, Bradley; Scott, Elizabeth M; Hickie, Ian B

2016-10-01

Mood disorders are characterized by disabling symptoms and cognitive difficulties which may vary in intensity throughout the course of the illness. Sleep-wake cycles and circadian rhythms influence emotional regulation and cognitive functions. However, the relationships between the sleep-wake disturbances experienced commonly by people with mood disorders and the longitudinal changes in their clinical and cognitive profile are not well characterized. This study investigated associations between initial sleep-wake patterns and longitudinal changes in mood symptoms and cognitive functions in 50 young people (aged 13-33 years) with depression or bipolar disorder. Data were based on actigraphy monitoring conducted over approximately 2 weeks and clinical and neuropsychological assessment. As part of a longitudinal cohort study, these assessments were repeated after a mean follow-up interval of 18.9 months. No significant differences in longitudinal clinical changes were found between the participants with depression and those with bipolar disorder. Lower sleep efficiency was predictive of longitudinal worsening in manic symptoms (P = 0.007). Shorter total sleep time (P = 0.043) and poorer circadian rhythmicity (P = 0.045) were predictive of worsening in verbal memory. These findings suggest that some sleep-wake and circadian disturbances in young people with mood disorders may be associated with less favourable longitudinal outcomes, notably for subsequent manic symptoms and memory difficulties. © 2016 European Sleep Research Society.

Prediction of Microstructure in HAZ of Welds

Science.gov (United States)

Khurana, S. P.; Yancey, R.; Jung, G.

2004-06-01

A modeling technique for predicting microstructure in the heat-affected zone (HAZ) of the hypoeutectoid steels is presented. This technique aims at predicting the phase fractions of ferrite, pearlite, bainite and martensite present in the HAZ after the cool down of a weld. The austenite formation kinetics and austenite decomposition kinetics are calculated using the transient temperature profile. The thermal profile in the weld and the HAZ is calculated by finite-element analysis (FEA). Two kinds of austenite decomposition models are included. The final phase fractions are predicted with the help of a continuous cooling transformation (CCT) diagram of the material. In the calculation of phase fractions either the experimental CCT diagram or the mathematically calculated CCT diagram can be used.

Prediction of microstructure in HAZ of welds

International Nuclear Information System (INIS)

Khurana, S.P.; Yancey, R.; Jung, G.

2004-01-01

A modeling technique for predicting microstructure in the heat-affected zone (HAZ) of the hypoeutectoid steels is presented. This technique aims at predicting the phase fractions of ferrite, pearlite, bainite and martensite present in the HAZ after the cool down of a weld. The austenite formation kinetics and austenite decomposition kinetics are calculated using the transient temperature profile. The thermal profile in the weld and the HAZ is calculated by finite-element analysis (FEA). Two kinds of austenite decomposition models are included. The final phase fractions are predicted with the help of a continuous cooling transformation (CCT) diagram of the material. In the calculation of phase fractions either the experimental CCT diagram or the mathematically calculated CCT diagram can be used

Character profiles and life satisfaction.

Science.gov (United States)

Park, Hwanjin; Suh, Byung Seong; Kim, Won Sool; Lee, Hye-Kyung; Park, Seon-Cheol; Lee, Kounseok

2015-04-01

There is a surge of interest in subjective well-being (SWB), which concerns how individuals feel about their happiness. Life satisfaction tends to be influenced by individual psychological traits and external social factors. The aim of this study was to examine the relationship between individual character and SWB. Data from 3522 university students were analyzed in this study. Character profiles were evaluated using the Temperament and Character Inventory-Revised Short version (TCI-RS). Life satisfaction was assessed using the Satisfaction with Life Scale (SWLS). All statistical tests regarding the correlations between each character profile and life satisfaction were conducted using ANOVAs, t-tests, multiple linear regression models and correlation analyses. The creative (SCT) profile was associated with the highest levels of life satisfaction, whereas the depressive (sct) profile was associated with the lowest levels of life satisfaction. Additionally, high self-directedness, self-transcendence and cooperation were associated with high life satisfaction. The results of gender-adjusted multiple regression analysis showed that the effects of self-directedness were the strongest in the assessment of one's quality of life, followed by self-transcendence and cooperativeness, in that order. All of the three-character profiles were significantly correlated with one's quality of life, and the character profiles of TCI-RS explained 27.6% of life satisfaction in total. Among the three-character profiles, the self-directedness profile was most associated with life satisfaction. Our study was cross-sectional, and self-reported data from students at a single university were analyzed. The results of this study showed that, among the character profiles, the effects of self-directedness were the strongest for predicting life satisfaction. Copyright © 2014 Elsevier Inc. All rights reserved.

Integrative structural modeling with small angle X-ray scattering profiles

Directory of Open Access Journals (Sweden)

Schneidman-Duhovny Dina

2012-07-01

Full Text Available Abstract Recent technological advances enabled high-throughput collection of Small Angle X-ray Scattering (SAXS profiles of biological macromolecules. Thus, computational methods for integrating SAXS profiles into structural modeling are needed more than ever. Here, we review specifically the use of SAXS profiles for the structural modeling of proteins, nucleic acids, and their complexes. First, the approaches for computing theoretical SAXS profiles from structures are presented. Second, computational methods for predicting protein structures, dynamics of proteins in solution, and assembly structures are covered. Third, we discuss the use of SAXS profiles in integrative structure modeling approaches that depend simultaneously on several data types.

Early pharmaceutical profiling to predict oral drug absorption

DEFF Research Database (Denmark)

Bergström, Christel A S; Holm, René; Jørgensen, Søren Astrup

2014-01-01

Preformulation measurements are used to estimate the fraction absorbed in vivo for orally administered compounds and thereby allow an early evaluation of the need for enabling formulations. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the pharmac......Preformulation measurements are used to estimate the fraction absorbed in vivo for orally administered compounds and thereby allow an early evaluation of the need for enabling formulations. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary...... and state-of-the art methodologies to study API properties impacting on oral absorption are reviewed. Assays performed during early development, i.e. physicochemical characterization, dissolution profiles under physiological conditions, permeability assays and the impact of excipients on these properties...

Design of the tool for periodic not evolvent profiles

Directory of Open Access Journals (Sweden)

Anisimov Roman

2017-01-01

Full Text Available The new approach to profiling of the tool for processing of parts with periodic not evolvent profiles are considered in the article The discriminatory analysis of periodic profiles including repetition of profile both in the plane of perpendicular axis of part, and in the plane of passing along part of axis is offered. In the basis of the offered profiling method the idea of space shaping by rated surface of product of tool surface lies. The big advantage of the offered approach in profiling is its combination with the analysis of parameters of process of engineering work. It allows to predict the accuracy and surface quality of product with not evolvent periodic profile. While using the offered approach the pinion cutter for processing of wheels with internal triangular teeths and mill for processing of the screw of the counter of consumption of liquid, complex profile of which consists of several formings, have been received

Comparison of percentage body fat and body mass index for the prediction of inflammatory and atherogenic lipid risk profiles in elderly women.

Science.gov (United States)

Funghetto, Silvana Schwerz; Silva, Alessandro de Oliveira; de Sousa, Nuno Manuel Frade; Stival, Marina Morato; Tibana, Ramires Alsamir; Pereira, Leonardo Costa; Antunes, Marja Letícia Chaves; de Lima, Luciano Ramos; Prestes, Jonato; Oliveira, Ricardo Jacó; Dutra, Maurílio Tiradentes; Souza, Vinícius Carolino; Nascimento, Dahan da Cunha; Karnikowski, Margô Gomes de Oliveira

2015-01-01

To compare the clinical classification of the body mass index (BMI) and percentage body fat (PBF) for the prediction of inflammatory and atherogenic lipid profile risk in older women. Cross-sectional analytical study with 277 elderly women from a local community in the Federal District, Brazil. PBF and fat-free mass (FFM) were determined by dual energy X-ray absorptiometry. The investigated inflammatory parameters were interleukin 6 and C-reactive protein. Twenty-five percent of the elderly women were classified as normal weight, 50% overweight, and 25% obese by the BMI. The obese group had higher levels of triglycerides and very low-density lipoproteins than did the normal weight group (P≤0.05) and lower levels of high-density lipoproteins (HDL) than did the overweight group (P≤0.05). According to the PBF, 49% of the elderly women were classified as eutrophic, 28% overweight, and 23% obese. In the binomial logistic regression analyses including age, FFM, and lipid profile, only FFM (odds ratio [OR]=0.809, 95% confidence interval [CI]: 0.739-0.886; Pprofile is key to assessing the risk of cardiometabolic diseases. Classification based on dual energy X-ray absorptiometry measures, along with biochemical and inflammatory parameters, seems to have a great clinical importance, since it allows the lipid profile eutrophic distinction in elderly overweight women.

Investigating Profiles of Lexical Quality in Preschool and Their Contribution to First Grade Reading

Science.gov (United States)

Murphy, Kimberly A.; Farquharson, Kelly

2016-01-01

This longitudinal study investigated profiles of lexical quality domains in preschool children and the extent to which profile membership predicted reading comprehension in first grade. A latent profile analysis was conducted to classify 420 preschool children on lexical quality domains, including orthography, phonology, morphosyntax, and…

Predicting fragmentation sizing profiles for different blasting patterns

International Nuclear Information System (INIS)

Sheikh, A.M.; Chung, S.H.

1987-01-01

This paper evaluates the efficiency of blasting in a large scale underground heap leaching operation. The prediction model is based on the dynamic tensile breaking strength of rock formation, the detonation characteristics of the explosives and the drill hole pattern. The modelling includes crack pattern development and fragmentation computation fitted by the Rosin-Rammler distribution equation

Complexity factors and prediction of performance

International Nuclear Information System (INIS)

Braarud, Per Oeyvind

1998-03-01

Understanding of what makes a control room situation difficult to handle is important when studying operator performance, both with respect to prediction as well as improvement of the human performance. A factor analytic approach identified eight factors from operators' answers to an 39 item questionnaire about complexity of the operator's task in the control room. A Complexity Profiling Questionnaire was developed, based on the factor analytic results from the operators' conception of complexity. The validity of the identified complexity factors was studied by prediction of crew performance and prediction of plant performance from ratings of the complexity of scenarios. The scenarios were rated by both process experts and the operators participating in the scenarios, using the Complexity Profiling Questionnaire. The process experts' complexity ratings predicted both crew performance and plant performance, while the operators' rating predicted plant performance only. The results reported are from initial studies of complexity, and imply a promising potential for further studies of the concept. The approach used in the study as well as the reported results are discussed. A chapter about the structure of the conception of complexity, and a chapter about further research conclude the report. (author)

The opportunities and barriers of user profiling in the public sector

NARCIS (Netherlands)

Pieterson, Willem Jan; Ebbers, Wolfgang E.; van Dijk, Johannes A.G.M.

2005-01-01

Like the private sector, the public sector makes more and more use of user profiling to personalize the electronic services that are being offered to citizens. User profiling offers great opportunities to make communication more effective and efficient, to infer and predict citizens’ behavior and to

H-mode profile parametrization for extrapolation and control

International Nuclear Information System (INIS)

Imre, K.; Riedel, K.S.; Schissel, D.P.; Schunke, B.

1996-01-01

A steady-state ELMy H-mode profile data set of 68 DIII-D discharges and 74 JET discharges is fitted with an error of 7-8%. The advantages of a parametrization of the plasma profiles in terms of a semi-parametric representation, T(ρ, I p , n-bar, B t , P L , R), are described. The shape of the temperature profile depends almost exclusively upon the size, R and q 95 , with a secondary dependence on the heating power. The density profile depends primarily upon q95 with a secondary dependence on n-bar. The line-average temperature T-bar e scales as n-bar -0.31 instead of T-bar∼''n-bar'' -1.0 . The predicted ITER temperature is T-bar = 17.1 keV. (Author)

Heating profiles on ICRF antenna Faraday shields

International Nuclear Information System (INIS)

Taylor, D.J.; Baity, F.W.; Hahs, C.L. Riemer, B.W.; Ryan, D.M.; Williamson, D.E.

1992-01-01

Poor definition of the heating profiles that occur during normal operation of Faraday shields for ion cyclotron resonant frequency (ICRF) antennas has complicated the mechanical design of ICRF system components. This paper reports that at Oak Ridge National Laboratory (ORNL), Faraday shield analysis is being used in defining rf heating profiles. In recent numerical analyses of proposed hardware for the Burning Plasma Experiment (BPX) and DIII-D, rf magnetic fields at Faraday shield surfaces were calculated, providing realistic predictions of the induced skin currents flowing on the shield elements and the resulting dissipated power profile. Detailed measurements on mock-ups of the Faraday shields for DIII-D and the Tokamak Fusion Test Reactor (TFTR) confirmed the predicted magnetic field distributions. A conceptual design for an uncooled Faraday shield for the BPX ion cyclotron resonance heating (ICRH) antenna, which should withstand the proposed long-pulse operation, has been completed. The analytical effort is described in detail, with emphasis on the design work for the BPX ICRH antenna conceptual design and for the replacement Faraday shield for the DIII-D FWCD antenna. Results of analyses are shown, and configuration issues involved in component modeling are discussed

pH-dependent solubility and permeability profiles: A useful tool for prediction of oral bioavailability.

Science.gov (United States)

Sieger, P; Cui, Y; Scheuerer, S

2017-07-15

pH-dependent solubility - permeability profiles offer a simple way to predict bioavailability after oral application, if bioavailability is only solubility and permeability driven. Combining both pH-dependent solubility and pH-dependent permeability in one diagram provides a pH-window (=ΔpH sol-perm ) from which the conditions for optimal oral bioavailability can be taken. The size of this window is directly proportional to the observed oral bioavailability. A set of 21 compounds, with known absolute human oral bioavailability, was used to establish this correlation. Compounds with ΔpH sol-perm bioavailability (bioavailability typically by approximately 25%. For compounds where ΔpH sol-perm ≥3 but still showing poor bioavailability, most probably other pharmacokinetic aspects (e.g. high clearance), are limiting exposure. Interestingly, the location of this pH-window seems to have a negligible influence on the observed oral bioavailability. In scenarios, where the bioavailability is impaired by certain factors, like for example proton pump inhibitor co-medication or food intake, the exact position of this pH-window might be beneficial for understanding the root cause. Copyright © 2017 Elsevier B.V. All rights reserved.

Choosing face: The curse of self in profile image selection.

Science.gov (United States)

White, David; Sutherland, Clare A M; Burton, Amy L

2017-01-01

People draw automatic social inferences from photos of unfamiliar faces and these first impressions are associated with important real-world outcomes. Here we examine the effect of selecting online profile images on first impressions. We model the process of profile image selection by asking participants to indicate the likelihood that images of their own face ("self-selection") and of an unfamiliar face ("other-selection") would be used as profile images on key social networking sites. Across two large Internet-based studies (n = 610), in line with predictions, image selections accentuated favorable social impressions and these impressions were aligned to the social context of the networking sites. However, contrary to predictions based on people's general expertise in self-presentation, other-selected images conferred more favorable impressions than self-selected images. We conclude that people make suboptimal choices when selecting their own profile pictures, such that self-perception places important limits on facial first impressions formed by others. These results underscore the dynamic nature of person perception in real-world contexts.

Damage profiles and ion distribution in Pt-irradiated SiC

Energy Technology Data Exchange (ETDEWEB)

Xue, H.Z. [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Zhang, Y., E-mail: Zhangy1@ornl.gov [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Zhu, Z. [Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 (United States); Zhang, W.M. [Department of Radiation Therapy, Peking University First Hospital, Beijing 100034 (China); Bae, I.-T. [Small Scale Systems Integration and Packaging Center, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY 13902 (United States); Weber, W.J. [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)

2012-09-01

Single crystalline 6H-SiC samples were irradiated at 150 K with 2 MeV Pt ions. The local volume swelling was determined by electron energy loss spectroscopy (EELS), and a nearly sigmoidal dependence on irradiation dose is observed. The disorder profiles and ion distribution were determined by Rutherford backscattering spectrometry (RBS), transmission electron microscopy, and secondary ion mass spectrometry. Since the volume swelling reaches 12% over the damage region at high ion fluence, the effect of lattice expansion is considered and corrected for in the analysis of RBS spectra to obtain depth profiles. Projectile and damage profiles are estimated by SRIM (Stopping and Range of Ions in Matter). When compared with the measured profiles, the SRIM code predictions of ion distribution and the damage profiles are underestimated due to significant overestimation of the electronic stopping power for the slow heavy Pt ions. By utilizing the reciprocity method, which is based on the invariance of the inelastic energy loss in ion-solid collisions against interchange of projectile and target atom, a much lower electronic stopping power is deduced. A simple approach, based on reducing the density of SiC target in SRIM simulation, is proposed to compensate the overestimated SRIM electronic stopping power values, which results in improved agreement between predicted and measured damage profiles and ion ranges.

Coagulation profile of children with sickle cell anemia in steady state ...

African Journals Online (AJOL)

Background: Sickle cell anemia is associated with a hypercoagulable state that may lead to alterations in a coagulation profile. Measurements of coagulation factors are known to have some predictive value for clinical outcome. Objectives: To determine the coagulation profile of children with SCA in steady state and crisis ...

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study.

Science.gov (United States)

Austdal, Marie; Tangerås, Line H; Skråstad, Ragnhild B; Salvesen, Kjell; Austgulen, Rigmor; Iversen, Ann-Charlotte; Bathen, Tone F

2015-09-08

Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study

Directory of Open Access Journals (Sweden)

Marie Austdal

2015-09-01

Full Text Available Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension. Preeclampsia developed in 26 (4.3% and gestational hypertension in 21 (3.5% women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

Childhood Sports Participation and Adolescent Sport Profile.

Science.gov (United States)

Gallant, François; O'Loughlin, Jennifer L; Brunet, Jennifer; Sabiston, Catherine M; Bélanger, Mathieu

2017-12-01

We aimed to increase understanding of the link between sport specialization during childhood and adolescent physical activity (PA). The objectives were as follows: (1) describe the natural course of sport participation over 5 years among children who are early sport samplers or early sport specializers and (2) determine if a sport participation profile in childhood predicts the sport profile in adolescence. Participants ( n = 756, ages 10-11 years at study inception) reported their participation in organized and unorganized PA during in-class questionnaires administered every 4 months over 5 years. They were categorized as early sport samplers, early sport specializers, or nonparticipants in year 1 and as recreational sport participants, performance sport participants, or nonparticipants in years 2 to 5. The likelihood that a childhood sport profile would predict the adolescent profile was computed as relative risks. Polynomial logistic regression was used to identify predictors of an adolescent sport profile. Compared with early sport specialization and nonparticipation, early sport sampling in childhood was associated with a higher likelihood of recreational participation (relative risk, 95% confidence interval: 1.55, 1.18-2.03) and a lower likelihood of nonparticipation (0.69, 0.51-0.93) in adolescence. Early sport specialization was associated with a higher likelihood of performance participation (1.65, 1.19-2.28) but not of nonparticipation (1.01, 0.70-1.47) in adolescence. Nonparticipation in childhood was associated with nearly doubling the likelihood of nonparticipation in adolescence (1.88, 1.36-2.62). Sport sampling should be promoted in childhood because it may be linked to higher PA levels during adolescence. Copyright © 2017 by the American Academy of Pediatrics.

Clinical versus actuarial geographic profiling strategies : A Review of the Research

NARCIS (Netherlands)

Bennell, Craig; Taylor, Paul; Snook, Brent

2007-01-01

Geographic profiling predictions can be produced using a variety of strategies. Some predictions are made using an equation or mechanical aid (actuarial strategy) while others are made by human judges drawing on experience or heuristic principles (clinical strategy). We review research that bears

Spiking the expectancy profiles

DEFF Research Database (Denmark)

Hansen, Niels Chr.; Loui, Psyche; Vuust, Peter

Melodic expectations have long been quantified using expectedness ratings. Motivated by statistical learning and sharper key profiles in musicians, we model musical learning as a process of reducing the relative entropy between listeners' prior expectancy profiles and probability distributions...... of a given musical style or of stimuli used in short-term experiments. Five previous probe-tone experiments with musicians and non-musicians are revisited. Exp. 1-2 used jazz, classical and hymn melodies. Exp. 3-5 collected ratings before and after exposure to 5, 15 or 400 novel melodies generated from...... a finite-state grammar using the Bohlen-Pierce scale. We find group differences in entropy corresponding to degree and relevance of musical training and within-participant decreases after short-term exposure. Thus, whereas inexperienced listeners make high-entropy predictions by default, statistical...

Stratigraphic Profiles for Selected Hanford Site Seismometer Stations and Other Locations

Energy Technology Data Exchange (ETDEWEB)

Last, George V. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2014-02-01

Stratigraphic profiles were constructed for eight selected Hanford Site seismometer stations, five Hanford Site facility reference locations, and seven regional three-component broadband seismometer stations. These profiles provide interpretations of the subsurface layers to support estimation of ground motions from past earthquakes, and the prediction of ground motions from future earthquakes. In most cases these profiles terminated at the top of the Wanapum Basalt, but at selected sites profiles were extended down to the top of the crystalline basement. The composite one-dimensional stratigraphic profiles were based primarily on previous interpretations from nearby boreholes, and in many cases the nearest deep borehole is located kilometers away.

Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

Science.gov (United States)

Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

2017-12-01

Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

The impact of time perspective latent profiles on college drinking: a multidimensional approach.

Science.gov (United States)

Braitman, Abby L; Henson, James M

2015-04-01

Zimbardo and Boyd's(1) time perspective, or the temporal framework individuals use to process information, has been shown to predict health behaviors such as alcohol use. Previous studies supported the predictive validity of individual dimensions of time perspective, with some dimensions acting as protective factors and others as risk factors. However, some studies produced findings contrary to the general body of literature. In addition, time perspective is a multidimensional construct, and the combination of perspectives may be more predictive than individual dimensions in isolation; consequently, multidimensional profiles are a more accurate measure of individual differences and more appropriate for predicting health behaviors. The current study identified naturally occurring profiles of time perspective and examined their association with risky alcohol use. Data were collected from a college student sample (n = 431, mean age = 20.41 years) using an online survey. Time perspective profiles were identified using latent profile analysis. Bootstrapped regression models identified a protective class that engaged in significantly less overall drinking (β = -0.254) as well as engaging in significantly less episodic high risk drinking (β = -0.274). There was also emerging evidence of a high risk time perspective profile that was linked to more overall drinking (β = 0.198) and engaging in more high risk drinking (β = 0.245), though these differences were not significant. CONCLUSIONS/IMPORTANCE: These findings support examining time perspective in a multidimensional framework rather than individual dimensions in isolation. Implications include identifying students most in need of interventions, and tailoring interventions to target temporal framing in decision-making.

Predicting turns in proteins with a unified model.

Directory of Open Access Journals (Sweden)

Qi Song

Full Text Available MOTIVATION: Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. RESULTS: In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i using newly exploited features of structural evolution information (secondary structure and shape string of protein based on structure homologies, (ii considering all types of turns in a unified model, and (iii practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

Prediction of work piece geometry in electrochemical cavity sinking

Energy Technology Data Exchange (ETDEWEB)

Riggs, J B; Muller, R H; Tobias, C W

1981-01-01

A computer-implemented model for predicting ECM work piece geometry has been developed and experimentally verified with a commercial ECM machine for cavity sinking in copper and 302-stainless steel with 2N KNO/sub 3/ electrolyte. Constant tool piece feed rates of 7-10 x 10/sup -4/ cm/s, and applied voltages of 11-25 V were used. The model predicts the dependence of work piece geometry on operating conditions and on the electrochemical and physical properties of the metal-electrolyte pair. Comparison of eight equilibrium and six unsteady state experimental cavity profiles in copper showed satisfactory agreement with predictions, as did five equilibrium profiles for cavity sinking in 302-stainless steel.

Sensitivity and specificity of a brief personality screening instrument in predicting future substance use, emotional, and behavioral problems: 18-month predictive validity of the Substance Use Risk Profile Scale.

Science.gov (United States)

Castellanos-Ryan, Natalie; O'Leary-Barrett, Maeve; Sully, Laura; Conrod, Patricia

2013-01-01

This study assessed the validity, sensitivity, and specificity of the Substance Use Risk Profile Scale (SURPS), a measure of personality risk factors for substance use and other behavioral problems in adolescence. The concurrent and predictive validity of the SURPS was tested in a sample of 1,162 adolescents (mean age: 13.7 years) using linear and logistic regressions, while its sensitivity and specificity were examined using the receiver operating characteristics curve analyses. Concurrent and predictive validity tests showed that all 4 brief scales-hopelessness (H), anxiety sensitivity (AS), impulsivity (IMP), and sensation seeking (SS)-were related, in theoretically expected ways, to measures of substance use and other behavioral and emotional problems. Results also showed that when using the 4 SURPS subscales to identify adolescents "at risk," one can identify a high number of those who developed problems (high sensitivity scores ranging from 72 to 91%). And, as predicted, because each scale is related to specific substance and mental health problems, good specificity was obtained when using the individual personality subscales (e.g., most adolescents identified at high risk by the IMP scale developed conduct or drug use problems within the next 18 months [a high specificity score of 70 to 80%]). The SURPS is a valuable tool for identifying adolescents at high risk for substance misuse and other emotional and behavioral problems. Implications of findings for the use of this measure in future research and prevention interventions are discussed. Copyright © 2012 by the Research Society on Alcoholism.

The clinical utility of lipid profile and positive troponin in predicting future cardiac events

Directory of Open Access Journals (Sweden)

Arun Kumar

2012-02-01

Full Text Available Objective: To study the usefulness of traditional lipid profile levels in screening subjects who had developed chest pain due to cardiac event as indicated by a positive troponin I (TnI test. Methods: In this retrospective study data of the 740 patients presented to the emergency department with symptoms of cardiac ischemia that underwent both troponin and lipid profiles tests were compared with the lipid profiles of 411 normal healthy subjects (controls. The troponin was detected qualitatively when a specimen contains TnI above the 99th percentile (TnI >0.5 ng/ mL. The total cholesterol (TC, high density lipoproteins (HDL, very low density lipoproteins (VLDL, and triacyl glycerol (TG levels were also analyzed and low density lipoprotein level (LDL was calculated using Friedewald ’s formula. Results: Patients with chest pain and positive troponin test (with confirmed cardiac event were found to have significantly elevated levels of TC, TG, LDL and significantly reduced HDL levels when compared to the patients who experienced only chest pain (negative troponin and healthy controls. Conclusions: Traditional lipid profile levels still can be used in screening populations to identify the subjects with high risk of developing cardiac event which is identified by highly sensitive and specific positive troponin test.

cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila.

Science.gov (United States)

Yang, Tzu-Hsien; Wang, Chung-Ching; Hung, Po-Cheng; Wu, Wei-Sheng

2014-01-01

Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms

Evaluation of vertical profiles to design continuous descent approach procedure

Science.gov (United States)

Pradeep, Priyank

The current research focuses on predictability, variability and operational feasibility aspect of Continuous Descent Approach (CDA), which is among the key concepts of the Next Generation Air Transportation System (NextGen). The idle-thrust CDA is a fuel economical, noise and emission abatement procedure, but requires increased separation to accommodate for variability and uncertainties in vertical and speed profiles of arriving aircraft. Although a considerable amount of researches have been devoted to the estimation of potential benefits of the CDA, only few have attempted to explain the predictability, variability and operational feasibility aspect of CDA. The analytical equations derived using flight dynamics and Base of Aircraft and Data (BADA) Total Energy Model (TEM) in this research gives insight into dependency of vertical profile of CDA on various factors like wind speed and gradient, weight, aircraft type and configuration, thrust settings, atmospheric factors (deviation from ISA (DISA), pressure and density of the air) and descent speed profile. Application of the derived equations to idle-thrust CDA gives an insight into sensitivity of its vertical profile to multiple factors. This suggests fixed geometric flight path angle (FPA) CDA has higher degree of predictability and lesser variability at the cost of non-idle and low thrust engine settings. However, with optimized design this impact can be overall minimized. The CDA simulations were performed using Future ATM Concept Evaluation Tool (FACET) based on radar-track and aircraft type data (BADA) of the real air-traffic to some of the busiest airports in the USA (ATL, SFO and New York Metroplex (JFK, EWR and LGA)). The statistical analysis of the vertical profiles of CDA shows 1) mean geometric FPAs derived from various simulated vertical profiles are consistently shallower than 3° glideslope angle and 2) high level of variability in vertical profiles of idle-thrust CDA even in absence of

Asthma pharmacogenetics and the development of genetic profiles for personalized medicine

Directory of Open Access Journals (Sweden)

Ortega VE

2015-01-01

Full Text Available Victor E Ortega, Deborah A Meyers, Eugene R Bleecker Center for Genomics and Personalized Medicine Research, Pulmonary Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA Abstract: Human genetics research will be critical to the development of genetic profiles for personalized or precision medicine in asthma. Genetic profiles will consist of gene variants that predict individual disease susceptibility and risk for progression, predict which pharmacologic therapies will result in a maximal therapeutic benefit, and predict whether a therapy will result in an adverse response and should be avoided in a given individual. Pharmacogenetic studies of the glucocorticoid, leukotriene, and β2-adrenergic receptor pathways have focused on candidate genes within these pathways and, in addition to a small number of genome-wide association studies, have identified genetic loci associated with therapeutic responsiveness. This review summarizes these pharmacogenetic discoveries and the future of genetic profiles for personalized medicine in asthma. The benefit of a personalized, tailored approach to health care delivery is needed in the development of expensive biologic drugs directed at a specific biologic pathway. Prior pharmacogenetic discoveries, in combination with additional variants identified in future studies, will form the basis for future genetic profiles for personalized tailored approaches to maximize therapeutic benefit for an individual asthmatic while minimizing the risk for adverse events. Keywords: asthma, pharmacogenetics, response heterogeneity, single nucleotide polymorphism, genome-wide association study

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

Directory of Open Access Journals (Sweden)

Daniël B van Schalkwijk

Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

Charnock's Roughness Length Model and Non-dimensional Wind Profiles Over the Sea

DEFF Research Database (Denmark)

Pena Diaz, Alfredo; Gryning, Sven-Erik

2008-01-01

An analysis tool for the study of wind speed profiles over the water has been developed. The profiles are analysed using a modified dimensionless wind speed and dimensionless height, assuming that the sea surface roughness can be predicted by Charnock's roughness length model. In this form, the r...

Compact Web browsing profiles for click-through rate prediction

DEFF Research Database (Denmark)

Fruergaard, Bjarne Ørum; Hansen, Lars Kai

2014-01-01

In real time advertising we are interested in finding features that improve click-through rate prediction. One source of available information is the bipartite graph of websites previously engaged by identifiable users. In this work, we investigate three different decompositions of such a graph...

In-Situ Observation of Undisturbed Surface Layer Scaler Profiles for Characterizing Evaporative Duct Properties

Science.gov (United States)

2016-06-01

9 Figure 4. Prototype RHIB-based tethered balloon MAPS used in CASPER Pilot. The...profile measurements over the ocean. The system is designed to make profiling measurements with multiple up/downs using an instrumented tethered balloon ...temperature profiles with high vertical resolution. With the ultimate goal of improving evaporative duct prediction, we use a tethered 2 balloon

Chloride ingress prediction

DEFF Research Database (Denmark)

Frederiksen, Jens Mejer; Geiker, Mette Rica

2008-01-01

Prediction of chloride ingress into concrete is an important part of durability design of reinforced concrete structures exposed to chloride containing environment. This paper presents experimentally based design parameters for Portland cement concretes with and without silica fume and fly ash...... in marine atmospheric and submersed South Scandinavian environment. The design parameters are based on sequential measurements of 86 chloride profiles taken over ten years from 13 different types of concrete. The design parameters provide the input for an analytical model for chloride profiles as function...... of depth and time, when both the surface chloride concentration and the diffusion coefficient are allowed to vary in time. The model is presented in a companion paper....

Molecular markers predicting radiotherapy response: Report and recommendations from an International Atomic Energy Agency technical meeting

International Nuclear Information System (INIS)

West, Catharine M.L.; McKay, Michael J.; Hoelscher, Tobias; Baumann, Michael; Stratford, Ian J.; Bristow, Robert G.; Iwakawa, Mayumi; Imai, Takashi; Zingde, Surekha M.; Anscher, Mitchell S.; Bourhis, Jean; Begg, Adrian C.; Haustermans, Karin; Bentzen, Soren M.; Hendry, Jolyon H.

2005-01-01

Purpose: There is increasing interest in radiogenomics and the characterization of molecular profiles that predict normal tissue and tumor radioresponse. A meeting in Amsterdam was organized by the International Atomic Energy Agency to discuss this topic on an international basis. Methods and Materials: This report is not completely exhaustive, but highlights some of the ongoing studies and new initiatives being carried out worldwide in the banking of tumor and normal tissue samples underpinning the development of molecular marker profiles for predicting patient response to radiotherapy. It is generally considered that these profiles will more accurately define individual or group radiosensitivities compared with the nondefinitive findings from the previous era of cellular-based techniques. However, so far there are only a few robust reports of molecular markers predicting normal tissue or tumor response. Results: Many centers in different countries have initiated tissue and tumor banks to store samples from clinical trials for future molecular profiling analysis, to identify profiles that predict for radiotherapy response. The European Society for Therapeutic Radiology and Oncology GENEtic pathways for the Prediction of the effects of Irradiation (GENEPI) project, to store, document, and analyze sample characteristics vs. response, is the most comprehensive in this regard. Conclusions: The next 5-10 years are likely to see the results of these and other correlative studies, and promising associations of profiles with response should be validated in larger definitive trials

Experimentally determined profiles of fast wave current drive on DIII-D

International Nuclear Information System (INIS)

Forest, C.B.; Petty, C.C.; Baity, F.W.; Chiu, S.C.; deGrassie, J.S.; Groebner, R.J.; Ikezi, H.; Jaeger, E.F.; Kupfer, K.; Murakami, M.; Pinsker, R.I.; Prater, R.; Rice, B.W.; Wade, M.R.; Whyte, D.G.

1996-01-01

Profiles of non-inductive current driven by fast waves have been determined for reversed-shear DIII-D discharges. Both the current profile and toroidal electric field profile are determined from time sequences of equilibrium reconstructions [C. B. Forest et al., Phys. Rev. Lett. 73, 2224 (1994)]. Using this information, the measured current profile has been separated into inductive and non-inductive portions. By comparing similar discharges with co and counter antenna phasings and similar fast wave power, the portion of the total non-inductive current driven by fast waves was determined. The experimentally determined profiles of FWCD are in general agreement with theoretical predictions. Specifically, 135 kA was driven by 1.4 MW of rf power with a profile peaked inside ρ=2. copyright 1996 American Institute of Physics

Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours.

Science.gov (United States)

Wilson, Martin; Cummins, Carole L; Macpherson, Lesley; Sun, Yu; Natarajan, Kal; Grundy, Richard G; Arvanitis, Theodoros N; Kauppinen, Risto A; Peet, Andrew C

2013-01-01

Brain tumours cause the highest mortality and morbidity rate of all childhood tumour groups and new methods are required to improve clinical management. (1)H magnetic resonance spectroscopy (MRS) allows non-invasive concentration measurements of small molecules present in tumour tissue, providing clinically useful imaging biomarkers. The primary aim of this study was to investigate whether MRS detectable molecules can predict the survival of paediatric brain tumour patients. Short echo time (30ms) single voxel (1)H MRS was performed on children attending Birmingham Children's Hospital with a suspected brain tumour and 115 patients were included in the survival analysis. Patients were followed-up for a median period of 35 months and Cox-Regression was used to establish the prognostic value of individual MRS detectable molecules. A multivariate model of survival was also investigated to improve prognostic power. Lipids and scyllo-inositol predicted poor survival whilst glutamine and N-acetyl aspartate predicted improved survival (pmodel of survival based on three MRS biomarkers predicted survival with a similar accuracy to histologic grading (p5e-5). A negative correlation between lipids and glutamine was found, suggesting a functional link between these molecules. MRS detectable biomolecules have been identified that predict survival of paediatric brain tumour patients across a range of tumour types. The evaluation of these biomarkers in large prospective studies of specific tumour types should be undertaken. The correlation between lipids and glutamine provides new insight into paediatric brain tumour metabolism that may present novel targets for therapy. Copyright © 2012 Elsevier Ltd. All rights reserved.

Bradsim-prediction of solute concentration. Temperature and physical property profiles along pulsed plate columns

International Nuclear Information System (INIS)

Logsdail, D.H.; Evans, S.F.; Jenkins, J.A.; Smith, I.J.

1988-01-01

Dynamic model of the operation of the BRADSIM pulsed plate column is developed. Examples of simulation of the pures process extraction system are given. Profiles of dissolved substances concentrations and profiles of physical properties of liquid along the column are provided. Calculated values are compared with the experimental data, obtained in case of the column 50 mm in diameter, Harwell extractional facility and Sellafield pulsed column 300 mm in diameter for extraction systems uranyl nitrate-nitric acid-20% and 30% TBP in kerosene. 2 refs.; 6 figs

Dissociative features in posttraumatic stress disorder: A latent profile analysis.

Science.gov (United States)

Műllerová, Jana; Hansen, Maj; Contractor, Ateka A; Elhai, Jon D; Armour, Cherie

2016-09-01

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) characterizes the dissociative subtype of posttraumatic stress disorder (PTSD) in terms of the individual meeting the criteria for PTSD and additionally reporting symptoms of depersonalization and/or derealization. The current study aimed to examine whether a dissociative PTSD profile may include alternative features of dissociation and whether it could be differentiated from a nondissociative PTSD profile on certain psychopathologies and demographics. Data from 309 trauma-exposed participants, collected through Amazon Mechanical Turk, were subjected to latent profile analysis. Regression analyses were used to examine the predictors of latent classes. Three discrete profiles named Baseline, PTSD, and Dissociative profile were uncovered. All examined features of dissociation were significantly elevated in the Dissociative profile. Anxiety, male sex, being employed, and having a minority racial background significantly predicted the Dissociative profile relative to the PTSD profile. The study points to the importance of alternative symptoms of dissociation in the dissociative PTSD subtype beyond the symptoms of depersonalization and derealization. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

IRI profile parameters at equatorial latitudes

International Nuclear Information System (INIS)

Reinisch, B.W.; Huang Xueqin; Conway, J.

2002-01-01

The IRI bottom-side electron density profile is specified as a function of three parameters B0, B1, and D1 describing the F2 layer thickness and shape, and the shape of the F1 layer, respectively. Together with the URSI or CCIR coefficients for the F2 layer peak density and height, they completely specify the profiles as function of time, season and solar activity. In support of the international effort of determining the best set of parameters we have analyzed the diurnal variations of B0, B1, and D1 for Jicamarca for high solar activity during 1999 and 2000 for different seasons and magnetic activity. The B0 values vary from a minimum of ∼95 km at 0300 LT to ∼250 km at local noon (1700 UT). The diurnal variation is similar to the IRI2000 prediction. B1 varies from ∼1.9 at daytime to ∼2.2 at night. The value of D1 is ∼0.5. The parameters show little Kp dependence. Standard deviations are shown. We calculated the ionospheric total electron contents for March and April 1998 from the ionogram profiles at Jicamarca and compared them with IRI predictions using the IRI 2000 parameters. While there is fair agreement, a significant time shift of 1 to 2 hours occurs in the transition from night to daytime values. (author)

Quantitative phenotyping via deep barcode sequencing.

Science.gov (United States)

Smith, Andrew M; Heisler, Lawrence E; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J; Chee, Mark; Roth, Frederick P; Giaever, Guri; Nislow, Corey

2009-10-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

The Development of Early Profiles of Temperament: Characterization, Continuity, and Etiology

Science.gov (United States)

Beekman, Charles; Neiderhiser, Jenae M.; Buss, Kristin A.; Loken, Eric; Moore, Ginger A.; Leve, Leslie D.; Ganiban, Jody M.; Shaw, Daniel S.; Reiss, David

2015-01-01

This study used a data-driven, person-centered approach to examine the characterization, continuity, and etiology of child temperament from infancy to toddlerhood. Data from 561 families who participated in an ongoing prospective adoption study, the Early Growth and Development Study, were used to estimate latent profiles of temperament at 9, 18, and 27 months. Results indicated that four profiles of temperament best fit the data at all three points of assessment. The characterization of profiles was stable over time while membership in profiles changed across age. Facets of adoptive parent and birth mother personality were predictive of children’s profile membership at each age, providing preliminary evidence for specific environmental and genetic influences on patterns of temperament development from infancy to toddlerhood. PMID:26332208

Effect of microstructure on the arsenic profile in implanted silicon

International Nuclear Information System (INIS)

Coghlan, W.A.; Rhee, M.H.; Williams, J.M.; Streit, L.A.; Williams, P.

1985-10-01

According to an irradiation damage model, the profile of an implanted ion at temperature great enough for diffusion to occur will depend on the sink density in the material. To test this model, pure silicon wafers were prepared with high and low dislocation densities. These wafers were implanted with about 5 x 10 19 As +2 /m 2 at 77 0 K, 300 0 C, and 600 0 C. After implanting the profiles were measured using Rutherford backscattering spectroscopy and secondary ion mass spectroscopy. The observed spreading of the As-profile contradicts initial theoretical predictions. Further speculation is presented to explain the differences

Relationships between methane production and milk fatty acid profiles in dairy cattle

NARCIS (Netherlands)

Dijkstra, J.; Zijderveld, van S.M.; Apajalahti, J.A.; Bannink, A.; Gerrits, W.J.J.; Newbold, J.R.; Perdok, H.B.; Berends, H.

2011-01-01

There is a need to develop simple ways of quantifying and estimating CH4 production in cattle. Our aim was to evaluate the relationship between CH4 production and milk fatty acid (FA) profile in order to use milk FA profiles to predict CH4 production in dairy cattle. Data from 3 experiments with

Correaltion of full-scale drag predictions with flight measurements on the C-141A aircraft. Phase 2: Wind tunnel test, analysis, and prediction techniques. Volume 1: Drag predictions, wind tunnel data analysis and correlation

Science.gov (United States)

Macwilkinson, D. G.; Blackerby, W. T.; Paterson, J. H.

1974-01-01

The degree of cruise drag correlation on the C-141A aircraft is determined between predictions based on wind tunnel test data, and flight test results. An analysis of wind tunnel tests on a 0.0275 scale model at Reynolds number up to 3.05 x 1 million/MAC is reported. Model support interference corrections are evaluated through a series of tests, and fully corrected model data are analyzed to provide details on model component interference factors. It is shown that predicted minimum profile drag for the complete configuration agrees within 0.75% of flight test data, using a wind tunnel extrapolation method based on flat plate skin friction and component shape factors. An alternative method of extrapolation, based on computed profile drag from a subsonic viscous theory, results in a prediction four percent lower than flight test data.

Phylo_dCor: distance correlation as a novel metric for phylogenetic profiling.

Science.gov (United States)

Sferra, Gabriella; Fratini, Federica; Ponzi, Marta; Pizzi, Elisabetta

2017-09-05

Elaboration of powerful methods to predict functional and/or physical protein-protein interactions from genome sequence is one of the main tasks in the post-genomic era. Phylogenetic profiling allows the prediction of protein-protein interactions at a whole genome level in both Prokaryotes and Eukaryotes. For this reason it is considered one of the most promising methods. Here, we propose an improvement of phylogenetic profiling that enables handling of large genomic datasets and infer global protein-protein interactions. This method uses the distance correlation as a new measure of phylogenetic profile similarity. We constructed robust reference sets and developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation that makes it applicable to large genomic data. Using Saccharomyces cerevisiae and Escherichia coli genome datasets, we showed that Phylo-dCor outperforms phylogenetic profiling methods previously described based on the mutual information and Pearson's correlation as measures of profile similarity. In this work, we constructed and assessed robust reference sets and propose the distance correlation as a measure for comparing phylogenetic profiles. To make it applicable to large genomic data, we developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation. Two R scripts that can be run on a wide range of machines are available upon request.

Dose Profiles in ECAL Crystals for Various Irradiation Conditions

CERN Document Server

Huhtinen, Mika

1998-01-01

Simulated dose profiles in various irradiation and beam test conditions are compared to the expected dose profiles in the ECAL crystals at LHC. Simple front or side irradiations with photons give too steep or too flat dose profiles, respectively. Thus, if dose maxima are fitted to agree, front irradiation underestimate the average dose whereas side irradiations tend to overestimate. Different profiles are difficult to compare reliably, but it seems likely that in both cases the discrepancy is about a factor of 2-3 but in different directions. For most purposes this is likely to be good enough, but should be taken into account in the interpretation of the test results. It is shown that using a customized lead mask between the source and the crystal can significantly improve the agreement between 60 Co side irradiations and the LHC predictions. A 400 MeV/c pion beam incident on a crystal matrix can also reproduce rather well the profiles expected in the barrel ECAL.

Memory Resilience to Alzheimer's Genetic Risk: Sex Effects in Predictor Profiles.

Science.gov (United States)

McDermott, Kirstie L; McFall, G Peggy; Andrews, Shea J; Anstey, Kaarin J; Dixon, Roger A

2017-10-01

Apolipoprotein E (APOE) ɛ4 and Clusterin (CLU) C alleles are risk factors for Alzheimer's disease (AD) and episodic memory (EM) decline. Memory resilience occurs when genetically at-risk adults perform at high and sustained levels. We investigated whether (a) memory resilience to AD genetic risk is predicted by biological and other risk markers and (b) the prediction profiles vary by sex and AD risk variant. Using a longitudinal sample of nondemented adults (n = 642, aged 53-95) we focused on memory resilience (over 9 years) to 2 AD risk variants (APOE, CLU). Growth mixture models classified resilience. Random forest analysis, stratified by sex, tested the predictive importance of 22 nongenetic risk factors from 5 domains (n = 24-112). For both sexes, younger age, higher education, stronger grip, and everyday novel cognitive activity predicted memory resilience. For women, 9 factors from functional, health, mobility, and lifestyle domains were also predictive. For men, only fewer depressive symptoms was an additional important predictor. The prediction profiles were similar for APOE and CLU. Although several factors predicted resilience in both sexes, a greater number applied only to women. Sex-specific mechanisms and intervention targets are implied. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Metabolome of human gut microbiome is predictive of host dysbiosis.

Science.gov (United States)

Larsen, Peter E; Dai, Yang

2015-01-01

Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome's interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome-host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

Poloidal rotation and the evolution of H-mode and VH-mode profiles

International Nuclear Information System (INIS)

Hinton, F.L.; Staebler, G.M.; Kim, Y.B.

1993-12-01

The physics which determines poloidal rotation, and its role in the development of profiles during H- and VH-modes, is discussed. A simple phenomenological transport model, which incorporates the rvec E x rvec B flow shear suppression of turbulence, is shown to predict profile evolution similar to that observed experimentally during H-mode and VH-mode

Building a profile of subjective well-being for social media users.

Science.gov (United States)

Chen, Lushi; Gong, Tao; Kosinski, Michal; Stillwell, David; Davidson, Robert L

2017-01-01

Subjective well-being includes 'affect' and 'satisfaction with life' (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users' affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p social media language.

Coping profiles characterize individual flourishing, languishing, and depression.

Science.gov (United States)

Faulk, Kathryn E; Gloria, Christian T; Steinhardt, Mary A

2013-01-01

According to the broaden-and-build theory of positive emotions, negative emotions narrow one's thought-action repertoire. In contrast, positive emotions have a broadening effect, expanding cognitive capacity, increasing potential coping strategies that come to mind, and enhancing decision-making, reaction, and adaptation to adversity. Fredrickson and Losada determined that a positivity ratio - the ratio of experienced positive to negative emotions - at or above 2.9 promotes human flourishing. A ratio below 2.9 is indicative of languishing individuals, whereas a ratio below 1.0 is a marker of depression. This study examined whether adaptive and maladaptive coping profiles differentiated those who flourish, languish, or are depressed in two convenience samples - military spouses (n =367) and public school teachers (n=267). Results were consistent with the theoretical predictions, as coping profiles of the groups differed significantly, with flourishing individuals favoring adaptive coping strategies more than those who were languishing or depressed. Conversely, depressed individuals reported greater use of maladaptive coping strategies than those who were languishing or flourishing. These results provide further empirical support for the mathematical model of Fredrickson and Losada, as the set of positivity criteria were predictive of coping profiles in two samples where successful coping and adaptation are important.

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

International Nuclear Information System (INIS)

Liu, Guangming; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Hua, Jianfeng

2015-01-01

Highlights: • An energy prediction (EP) method is introduced for battery E RDE determination. • EP determines E RDE through coupled prediction of future states, parameters, and output. • The PAEP combines parameter adaptation and prediction to update model parameters. • The PAEP provides improved E RDE accuracy compared with DC and other EP methods. - Abstract: In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the

TP53, STK11 and EGFR Mutations Predict Tumor Immune Profile and the Response to anti-PD-1 in Lung Adenocarcinoma.

Science.gov (United States)

Biton, Jerome; Mansuet-Lupo, Audrey; Pécuchet, Nicolas; Alifano, Marco; Ouakrim, Hanane; Arrondeau, Jennifer; Boudou-Rouquette, Pascaline; Goldwasser, Francois; Leroy, Karen; Goc, Jeremy; Wislez, Marie; Germain, Claire; Laurent-Puig, Pierre; Dieu-Nosjean, Marie-Caroline; Cremer, Isabelle; Herbst, Ronald; Blons, Hélène F; Damotte, Diane

2018-05-15

By unlocking anti-tumor immunity, antibodies targeting programmed cell death 1 (PD-1) exhibit impressive clinical results in non-small cell lung cancer, underlining the strong interactions between tumor and immune cells. However, factors that can robustly predict long-lasting responses are still needed. We performed in depth immune profiling of lung adenocarcinoma using an integrative analysis based on immunohistochemistry, flow-cytometry and transcriptomic data. Tumor mutational status was investigated using next-generation sequencing. The response to PD-1 blockers was analyzed from a prospective cohort according to tumor mutational profiles and to PD-L1 expression, and a public clinical database was used to validate the results obtained. We showed that distinct combinations of STK11 , EGFR and TP53 mutations, were major determinants of the tumor immune profile (TIP) and of the expression of PD-L1 by malignant cells. Indeed, the presence of TP53 mutations without co-occurring STK11 or EGFR alterations ( TP53 -mut/ STK11 - EGFR -WT), independently of KRAS mutations, identified the group of tumors with the highest CD8 T cell density and PD-L1 expression. In this tumor subtype, pathways related to T cell chemotaxis, immune cell cytotoxicity, and antigen processing were up-regulated. Finally, a prolonged progression-free survival (PFS: HR=0.32; 95% CI, 0.16-0.63, p <0.001) was observed in anti-PD-1 treated patients harboring TP53 -mut/ STK11 - EGFR -WT tumors. This clinical benefit was even more remarkable in patients with associated strong PD-L1 expression. Our study reveals that different combinations of TP53 , EGFR and STK11 mutations , together with PD-L1 expression by tumor cells, represent robust parameters to identify best responders to PD-1 blockade. Copyright ©2018, American Association for Cancer Research.

Burnout, work engagement and workaholism among highly educated employees: Profiles, antecedents and outcomes

Directory of Open Access Journals (Sweden)

Hely Innanen

2014-06-01

Full Text Available The present study examined the longitudinal profiles of burnout, engagement and workaholism among highly educated employees. First, the latent profile modeling indicated two latent classes: Engaged and Exhausted-Workaholic. Second, the results revealed that employees with the Engaged profile experienced high levels of energy and dedication, whereas employees with the Exhausted-Workaholic profile experienced exhaustion, cynicism and workaholism. Social pessimism in the transition from high education to work predicted poor subjective well-being at work. Further, workaholism decreased during the career among members of the Exhausted-Workaholic profile suggesting positive direction during career. Finally, Engaged employees experienced detachment and relaxation, life satisfaction and rewards.

Predicting Bacillus coagulans spores inactivation in tomato pulp under nonisothermal heat treatments.

Science.gov (United States)

Zimmermann, Morgana; Longhi, Daniel A; Schaffner, Donald W; Aragão, Gláucia M F

2014-05-01

The knowledge and understanding of Bacillus coagulans inactivation during a thermal treatment in tomato pulp, as well as the influence of temperature variation during thermal processes are essential for design, calculation, and optimization of the process. The aims of this work were to predict B. coagulans spores inactivation in tomato pulp under varying time-temperature profiles with Gompertz-inspired inactivation model and to validate the model's predictions by comparing the predicted values with experimental data. B. coagulans spores in pH 4.3 tomato pulp at 4 °Brix were sealed in capillary glass tubes and heated in thermostatically controlled circulating oil baths. Seven different nonisothermal profiles in the range from 95 to 105 °C were studied. Predicted inactivation kinetics showed similar behavior to experimentally observed inactivation curves when the samples were exposed to temperatures in the upper range of this study (99 to 105 °C). Profiles that resulted in less accurate predictions were those where the range of temperatures analyzed were comparatively lower (inactivation profiles starting at 95 °C). The link between fail prediction and both lower starting temperature and magnitude of the temperature shift suggests some chemical or biological mechanism at work. Statistical analysis showed that overall model predictions were acceptable, with bias factors from 0.781 to 1.012, and accuracy factors from 1.049 to 1.351, and confirm that the models used were adequate to estimate B. coagulans spores inactivation under fluctuating temperature conditions in the range from 95 to 105 °C. How can we estimate Bacillus coagulans inactivation during sudden temperature shifts in heat processing? This article provides a validated model that can be used to predict B. coagulans under changing temperature conditions. B. coagulans is a spore-forming bacillus that spoils acidified food products. The mathematical model developed here can be used to predict the spoilage

An assessment of predictive value of the biophysical profile in women with preeclampsia using data from the fullPIERS database.

Science.gov (United States)

Payne, Beth A; Kyle, Phillipa M; Lim, Kenneth; Lisonkova, Sarka; Magee, Laura A; Pullar, Barbra; Qu, Ziguang; von Dadelszen, Peter

2013-07-01

Pre-eclampsia is associated with increased risk to both the mother and fetus. Effective monitoring of the fetal condition is essential to the management of women with pre-eclampsia. The biophysical profile (BPP) is one monitoring tool available to clinicians. To compare the BPP test with cardiotocography/non-stress test (CTG/NST) alone for their ability to predict fetal acidemia at birth or a composite adverse perinatal outcome among women with preeclampsia and to estimate the effect of BPP assessment on mode of delivery and birth outcome. Secondary analysis of a prospective cohort of women with preeclampsia. The predictive ability of the tests was assessed based on sensitivity, specificity, positive and negative likelihood ratios (LR+, LR-). Women assessed with the BPP were compared with matched controls not assessed with the BPP to determine the odds of Cesarean delivery or adverse perinatal outcomes after adjustment for potential confounders. Five out of 89 women (5.6%) had an abnormal BPP; 18 out of 89 (20.2%) had an abnormal CTG/NST. Fetal acidemia was diagnosed in 13 fetuses (14.6%); composite adverse perinatal outcome in 68 fetuses/infants (76.4%). Both tests had relatively poor predictive performance for both outcomes (LR+ between 2.50 and 3.90 and LR- between 0.64 and 0.93). Assessment with the BPP was positively associated with fetal acidemia (adjusted OR 4.84; 95% CI 1.33-17.66). The BPP and CTG/NST alone were poor predictors of perinatal outcome in this cohort; multiple tests should be considered when assessing fetal risk in women with preeclampsia. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Modeling of Changing Electrode Profiles

Energy Technology Data Exchange (ETDEWEB)

Prentice, Geoffrey Allen [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Materials and Molecular Research Division; Univ. of California, Berkeley, CA (United States). Dept. of Chemical Engineering

1980-12-01

A model for simulating the transient behavior of solid electrodes undergoing deposition or dissolution has been developed. The model accounts for ohmic drop, charge transfer overpotential, and mass transport limitations. The finite difference method, coupled with successive overrelaxation, was used as the basis of the solution technique. An algorithm was devised to overcome the computational instabilities associated with the calculations of the secondary and tertiary current distributions. Simulations were performed on several model electrode profiles: the sinusoid, the rounded corner, and the notch. Quantitative copper deposition data were obtained in a contoured rotating cylinder system, Sinusoidal cross-sections, machined on stainless steel cylinders, were used as model geometries, Kinetic parameters for use in the simulation were determined from polarization curves obtained on copper rotating cylinders, These parameters, along with other physical property and geometric data, were incorporated in simulations of growing sinusoidal profiles. The copper distributions on the sinusoidal cross-sections were measured and found to compare favorably with the simulated results. At low Wagner numbers the formation of a slight depression at the profile peak was predicted by the simulation and observed on the profile. At higher Wagner numbers, the simulated and experimental results showed that the formation of a depression was suppressed. This phenomenon was shown to result from the competition between ohmic drop and electrode curvature.

Late-Onset Alzheimer's Disease Polygenic Risk Profile Score Predicts Hippocampal Function.

Science.gov (United States)

Xiao, Ena; Chen, Qiang; Goldman, Aaron L; Tan, Hao Yang; Healy, Kaitlin; Zoltick, Brad; Das, Saumitra; Kolachana, Bhaskar; Callicott, Joseph H; Dickinson, Dwight; Berman, Karen F; Weinberger, Daniel R; Mattay, Venkata S

2017-11-01

We explored the cumulative effect of several late-onset Alzheimer's disease (LOAD) risk loci using a polygenic risk profile score (RPS) approach on measures of hippocampal function, cognition, and brain morphometry. In a sample of 231 healthy control subjects (19-55 years of age), we used an RPS to study the effect of several LOAD risk loci reported in a recent meta-analysis on hippocampal function (determined by its engagement with blood oxygen level-dependent functional magnetic resonance imaging during episodic memory) and several cognitive metrics. We also studied effects on brain morphometry in an overlapping sample of 280 subjects. There was almost no significant association of LOAD-RPS with cognitive or morphometric measures. However, there was a significant negative relationship between LOAD-RPS and hippocampal function (familywise error [small volume correction-hippocampal region of interest] p risk score based on APOE haplotype, and for a combined LOAD-RPS + APOE haplotype risk profile score (p risk genes on hippocampal function even in healthy volunteers. The effect of LOAD-RPS on hippocampal function in the relative absence of any effect on cognitive and morphometric measures is consistent with the reported temporal characteristics of LOAD biomarkers with the earlier manifestation of synaptic dysfunction before morphometric and cognitive changes. Copyright © 2017 Society of Biological Psychiatry. All rights reserved.

[Safety profile of dolutegravir].

Science.gov (United States)

Rivero, Antonio; Domingo, Pere

2015-03-01

Integrase inhibitors are the latest drug family to be added to the therapeutic arsenal against human immunodeficiency virus infection. Drugs in this family that do not require pharmacological boosting are characterized by a very good safety profile. The latest integrase inhibitor to be approved for use is dolutegravir. In clinical trials, dolutegravir has shown an excellent tolerability profile, both in antiretroviral-naïve and previously treated patients. Discontinuation rates due to adverse effects were 2% and 3%, respectively. The most frequent adverse effects were nausea, headache, diarrhea and sleep disturbance. A severe hypersensitivity reaction has been reported in only one patient. In patients coinfected with hepatropic viruses, the safety profile is similar to that in patients without coinfection. The lipid profile of dolutegravir is similar to that of raltegravir and superior to those of Atripla® and darunavir/ritonavir. Dolutegravir induces an early, predictable and non-progressive increase in serum creatinine of around 10% of baseline values in treatment-naïve patients and of 14% in treatment-experienced patients. This increase is due to inhibition of tubular creatinine secretion through the OCT2 receptor and does not lead to a real decrease in estimated glomerular filtration rate with algorithms that include serum creatinine. The effect of the combination of dolutegravir plus Kivexa(®) on biomarkers of bone remodeling is lower than that of Atripla(®). Dolutegravir has an excellent tolerability profile with no current evidence of long-term adverse effects. Its use is accompanied by an early and non-progressive increase in serum creatinine due to OCT2 receptor inhibition. In combination with abacavir/lamivudine, dolutegravir has a lower impact than enofovir/emtricitabine/efavirenz on bone remodelling markers. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

The influence of humidity fluxes on offshore wind speed profiles

DEFF Research Database (Denmark)

Barthelmie, Rebecca Jane; Sempreviva, Anna Maria; Pryor, Sara

2010-01-01

extrapolation from lower measurements. With humid conditions and low mechanical turbulence offshore, deviations from the traditional logarithmic wind speed profile become significant and stability corrections are required. This research focuses on quantifying the effect of humidity fluxes on stability corrected...... wind speed profiles. The effect on wind speed profiles is found to be important in stable conditions where including humidity fluxes forces conditions towards neutral. Our results show that excluding humidity fluxes leads to average predicted wind speeds at 150 m from 10 m which are up to 4% higher...... than if humidity fluxes are included, and the results are not very sensitive to the method selected to estimate humidity fluxes....

Pharmacology profiling of chemicals and proteins

DEFF Research Database (Denmark)

Kringelum, Jens Vindahl

between pharmaceuticals and proteins in vivo potential leads to unwanted adverse effects, toxicity and reduced half-life, but can also lead to novel therapeutic effects of already approved pharmaceuticals. Hence identification of in vivo targets is of importance in discovery, development and repurposing....... This limitation complicates adverse effect assessment in the early drug-development phase, thus contributing to drugattrition. Prediction models offer the possibility to close these gaps and provide more complete pharmacology profiles, however improvements in performances are required for these tools to serve...... to its nonself origin, which potentially alters the pharmacology profile of the substance. The neutralization of biopharmaceuticals by antidrug antibodies (ADAs) is an important element in the immune response cascade, however studies of ADA binding site on biopharmaceuticals, referred to as B...

Fast ion profiles during neutral beam and lower hybrid heating

International Nuclear Information System (INIS)

Heidbrink, W.W.; Strachan, J.D.; Bell, R.E.; Cavallo, A.; Motley, R.; Schilling, G.; Stevens, J.; Wilson, J.R.

1985-07-01

Profiles of the d(d,p)t fusion reaction are measured in the PLT tokamak using an array of collimated 3 MeV proton detectors. During deuterium neutral beam injection, the emission profile indicates that the beam deposition is at least as narrow as predicted by a bounce-averaged Fokker-Planck code. The fast ion tail formed by lower hybrid waves (at densities above the critical density for current drive) also peaks strongly near the magnetic axis

CFD predictions of wake-stabilised jet flames in a cross-flow

International Nuclear Information System (INIS)

Lawal, Mohammed S.; Fairweather, Michael; Gogolek, Peter; Ingham, Derek B.; Ma, Lin; Pourkashanian, Mohamed; Williams, Alan

2013-01-01

This study describes an investigation into predicting the major flow properties in wake-stabilised jet flames in a cross flow of air using first- and second-order turbulence models, applied within a RANS (Reynolds-averaged Navier–Stokes) modelling framework. Standard and RNG (re-normalisation group) versions of the k-ε turbulence model were employed at the first-order level and the results compared with a second-moment closure, or RSM (Reynolds stress model). The combustion process was modelled using the laminar flamelet approach together with a thermal radiation model using the discrete ordinate method. The ability of the various turbulence models to reproduce experimentally established flame appearance, profiles of velocity and turbulence intensity, as well as the combustion efficiency of such flames is reported. The results show that all the turbulence models predict similar velocity profiles over the majority of the flow domain considered, except in the wake region, where the predictions of the RSM and RNG k-ε models are in closer agreement with experimental data. In contrast, the standard k-ε model over-predicts the peak turbulence intensity. Also, it is found that the RSM provides superior predictions of the planar recirculation and flame zones attached to the release pipe in the wake region. - Highlights: ► We investigated the prediction of the major properties in wake-stabilised methane jet flames in a cross flow. ► The ability of the various turbulence models to reproduce experimentally established flame parameters is reported. ► All the turbulence models considered predict similar velocity profiles, except in the wake region

An ultra-high frequency boundary layer Doppler/interferometric profiler

International Nuclear Information System (INIS)

Van Baelen, J.S.

1994-01-01

The planetary boundary layer (PBL) is that portion of the earth's atmosphere that is directly influenced by the earth's surface. The PBL can be vigorously turbulent and range in depth from a few hundred meters to a few kilometers. Solar energy is primarily absorbed at the earth's surface and transmitted to the free atmosphere through boundary-layer processes. An accurate portrayal of these transfers within the PBL is crucial to understand and predict many atmospheric processes from pollutant dispersion to numerical weather prediction and numerical simulations of climate change. This paper describes and discusses wind profiling techniques, focusing on the newly developed radio acoustic sounding system (RASS), and reviews past efforts to measure flux within the PBL. A new UHF wind profiling radar, the UHF Doppler/Interferometric Boundary Layer Radar, for accurately measuring both mean and flux quantities, as well as wind divergence and acoustic wave propagation, is outlined

Development of migration prediction system (MIGSTEM) for cationic species of radionuclides through soil layers

International Nuclear Information System (INIS)

Ohnuki, Toshihiko; Takebe, Shinichi; Yamamoto, Tadatoshi

1989-01-01

The migration prediction system (MIGSTEM) has been developed for estimating the migration of cationic species of radionuclides through soil layers systematically. The MIGSTEM consists of the migration experiments, the one-dimensional fitting code (inverse analysis code) for determining retardation factor and dispersivity (migration factors) and the three-dimensional differential code (prediction code) for estimating the migration of the radionuclides. The migration experiments are carried out for obtaining the concentration profiles of the radionuclides in unsaturated and saturated soil layers. Using the inverse analysis code, the migration factors are obtained at one time by fitting the concentration profiles calculated to those observed. The prediction code can give the contours of concentration and the one-dimensional concentration profiles at selected time, as well as the changing in the concentration at a selected position with time. The validity of the MIGSTEM was obtained by the benchmark test on the prediction and inverse analysis codes. The MIGSTEM was applied to estimate the migration of Sr 2+ through the sandy soil. (author)

Model for radial gas fraction profiles in vertical pipe flow

International Nuclear Information System (INIS)

Lucas, D.; Krepper, E.; Prasser, H.M.

2001-01-01

A one-dimensional model is presented, which predicts the radial volume fraction profiles from a given bubble size distribution. It bases on the assumption of an equilibrium of the forces acting on a bubble perpendicularly to the flow path (non drag forces). For the prediction of the flow pattern this model could be used within an procedure together with appropriate models for local bubble coalescence and break-up. (orig.)

A community effort to assess and improve drug sensitivity prediction algorithms.

Science.gov (United States)

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2014-12-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Analysis and Prediction of the Billet Butt and Transverse Weld in the Continuous Extrusion Process of a Hollow Aluminum Profile

Science.gov (United States)

Lou, Shumei; Wang, Yongxiao; Liu, Chuanxi; Lu, Shuai; Liu, Sujun; Su, Chunjian

2017-08-01

In continuous extrusions of aluminum profiles, the thickness of the billet butt and the length of the discarded extrudate containing the transverse weld play key roles in reducing material loss and improving product quality. The formation and final distribution of the billet butt and transverse weld depend entirely on the flow behavior of the billet skin material. This study examined the flow behavior of the billet skin material as well as the formation and evolution of the billet butt and the transverse weld in detail through numerical simulation and a series of experiments. In practical extrusions, even if the billet skin is removed by lathe turning shortly before extrusion, billet skin impurities are still distributed around the transverse weld and in the billet butt. The thickness of the scrap billet butt and the length of the discarded extrudate containing the transverse weld can be exactly predicted via simulation.

Metabolome of human gut microbiome is predictive of host dysbiosis

Energy Technology Data Exchange (ETDEWEB)

Larsen, Peter E.; Dai, Yang

2015-09-14

Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.

Intensity profiles behind a five-stage neutron interferometer

International Nuclear Information System (INIS)

Kischko, U.

1983-01-01

By means of the quantitative photography intensity profiles behind a five-stage ideal-crystal neutron interferometer at the thermal channel H25 of the high-flux reactor at the institute Laue-Langevin in Grenoble/France were dermined and compared with theoretical profiles. Contravily to X-rays by neutrons the hole Borrmann range is excited. This leads in the interference picture to superposition of several wave field components. It was shown that the spherical wave theory, as it was developed by W. Bauspiess, U. Bonse, and W. Graeff for the absorption-free neutron interferometer, describes well quantitatively the experimental intensity profiles. Expecially for the t-2t-t geometry the theoretically predicted focusing was confirmed. For the H-beam the intensity profile is symmetric and spatially limited; the O-beam is asymetric with intensities decreasing slowly up to the boundary. Geometrical differences within single stages lead to unique changes in the intensity profile. The pigtail pattern leading in the past to some puzzle guessing could be explained by the influence of geometrical defocusings on the phase shift. Important conclusions for the geometrical tolerances, which have to be regarded in the construction of neutron interferometers, could be obtained. (orig.) [de

Modelling of Temperature Profiles and Transport Scaling in Auxiliary Heated Tokamaks

DEFF Research Database (Denmark)

Callen, J.D.; Christiansen, J.P.; Cordey, J.G.

1987-01-01

time , the heating effectiveness η, and the energy offset W(0). Considering both the temperature profile responses and the global transport scaling, the constant heat pinch or excess temperature gradient model is found to best characterize the present JET data. Finally, new methods are proposed......The temperature profiles produced by various heating profiles are calculated from local heat transport models. The models take the heat flux to be the sum of heat diffusion and a non-diffusive heat flow, consistent with local measurements of heat transport. Two models are developed analytically...... in detail: (i) a heat pinch or excess temperature gradient model with constant coefficients; and (ii) a non-linear heat diffusion coefficient (χ) model. Both models predict weak (lesssim20%) temperature profile responses to physically relevant changes in the heat deposition profile – primarily because...

The Development of Early Profiles of Temperament: Characterization, Continuity, and Etiology.

Science.gov (United States)

Beekman, Charles; Neiderhiser, Jenae M; Buss, Kristin A; Loken, Eric; Moore, Ginger A; Leve, Leslie D; Ganiban, Jody M; Shaw, Daniel S; Reiss, David

2015-01-01

This study used a data-driven, person-centered approach to examine the characterization, continuity, and etiology of child temperament from infancy to toddlerhood. Data from 561 families who participated in an ongoing prospective adoption study, the Early Growth and Development Study, were used to estimate latent profiles of temperament at 9, 18, and 27 months. Results indicated that four profiles of temperament best fit the data at all three points of assessment. The characterization of profiles was stable over time, while membership in profiles changed across age. Facets of adoptive parent and birth mother personality were predictive of children's profile membership at each age, providing preliminary evidence for specific environmental and genetic influences on patterns of temperament development from infancy to toddlerhood. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

Free surface profiles in river flows: Can standard energy-based gradually-varied flow computations be pursued?

Science.gov (United States)

Cantero, Francisco; Castro-Orgaz, Oscar; Garcia-Marín, Amanda; Ayuso, José Luis; Dey, Subhasish

2015-10-01

Is the energy equation for gradually-varied flow the best approximation for the free surface profile computations in river flows? Determination of flood inundation in rivers and natural waterways is based on the hydraulic computation of flow profiles. This is usually done using energy-based gradually-varied flow models, like HEC-RAS, that adopts a vertical division method for discharge prediction in compound channel sections. However, this discharge prediction method is not so accurate in the context of advancements over the last three decades. This paper firstly presents a study of the impact of discharge prediction on the gradually-varied flow computations by comparing thirteen different methods for compound channels, where both energy and momentum equations are applied. The discharge, velocity distribution coefficients, specific energy, momentum and flow profiles are determined. After the study of gradually-varied flow predictions, a new theory is developed to produce higher-order energy and momentum equations for rapidly-varied flow in compound channels. These generalized equations enable to describe the flow profiles with more generality than the gradually-varied flow computations. As an outcome, results of gradually-varied flow provide realistic conclusions for computations of flow in compound channels, showing that momentum-based models are in general more accurate; whereas the new theory developed for rapidly-varied flow opens a new research direction, so far not investigated in flows through compound channels.

A streamlined ribosome profiling protocol for the characterization of microorganisms

DEFF Research Database (Denmark)

Latif, Haythem; Szubin, Richard; Tan, Justin

2015-01-01

Ribosome profiling is a powerful tool for characterizing in vivo protein translation at the genome scale, with multiple applications ranging from detailed molecular mechanisms to systems-level predictive modeling. Though highly effective, this intricate technique has yet to become widely used...... in the microbial research community. Here we present a streamlined ribosome profiling protocol with reduced barriers to entry for microbial characterization studies. Our approach provides simplified alternatives during harvest, lysis, and recovery of monosomes and also eliminates several time-consuming steps...

FERAL : Network-based classifier with application to breast cancer outcome prediction

NARCIS (Netherlands)

Allahyar, A.; De Ridder, J.

2015-01-01

Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

Predictability of Harmonic Complexity Across 75 Years of Popular Music Hits

DEFF Research Database (Denmark)

Jensen, Karl Kristoffer; Hebert, David

2015-01-01

profiles, with strong predictability. From the 1940s is a sustained increase in JCC that nearly doubles, peaking in the 1980s, and gradually decreasing into the 21st century. Each decade was also deter- mined to correlate to a statistically distinctive harmonic profile. The find- ings presented here...

Forecast of solar proton flux profiles for well-connected events

Science.gov (United States)

Ji, Eun-Young; Moon, Yong-Jae; Park, Jinhye

2014-12-01

We have developed a forecast model of solar proton flux profiles (> 10 MeV channel) for well-connected events. Among 136 solar proton events (SPEs) from 1986 to 2006, we select 49 well-connected ones that are all associated with single X-ray flares stronger than M1 class and start to increase within 4 h after their X-ray peak times. These events show rapid increments in proton flux. By comparing several empirical functions, we select a modified Weibull curve function to approximate a SPE flux profile. The parameters (peak flux, rise time, and decay time) of this function are determined by the relationship between X-ray flare parameters (peak flux, impulsive time, and emission measure) and SPE parameters. For 49 well-connected SPEs, the linear correlation coefficient between the predicted and the observed proton peak fluxes is 0.65 with the RMS error of 0.55 log10(pfu). In addition, we determine another forecast model based on flare and coronal mass ejection (CME) parameters using 22 SPEs. The used CME parameters are linear speed and angular width. As a result, we find that the linear correlation coefficient between the predicted and the observed proton peak fluxes is 0.83 with the RMS error of 0.35 log10(pfu). From the relationship between error of model and CME acceleration, we find that CME acceleration is an important factor for predicting proton flux profiles.

Edge density profiles in high-performance JET plasmas

International Nuclear Information System (INIS)

Summers, D.D.R.; Viaccoz, B.; Vince, J.

1997-01-01

Detailed electron density profiles of the scrape-off layer in high-performance JET plasmas (plasma current, I p nbi ∝17 MW) have been measured by means of a lithium beam diagnostic system featuring high spatial resolution [Kadota (1978)[. Measurements were taken over a period of several seconds, allowing examination of the evolution of the edge profile at a location upstream from the divertor target. The data clearly show the effects of the H-mode transition - an increase in density near the plasma separatrix and a reduction in density scrape-off length. The profiles obtained under various plasma conditions are compared firstly with data from other diagnostics, located elsewhere in the vessel, and also with the predictions of an 'onion-skin' model (DIVIMP), which used, as initial parameters, data from an array of probes located in the divertor target. (orig.)

Parametric Jominy profiles predictor based on neural networks

Directory of Open Access Journals (Sweden)

Valentini, R.

2005-12-01

Full Text Available The paper presents a method for the prediction of the Jominy hardness profiles of steels for microalloyed Boron steel which is based on neural networks. The Jominy profile has been parameterized and the parameters, which are a sort of "compact representation" of the profile itself, are linked to the steel chemical composition through a neural network. Numerical results are presented and discussed.

El trabajo presenta un método de estimación de perfiles de dureza Jominy para aceros microaleados al boro basado en redes neuronales. Los parámetros de perfil Jominy, que constituyen una especie de "representación compacta" del perfil mismo, son determinados y puestos en relación con la composición química del acero mediante una red neuronal. Los resultados numéricos son expuestos y discutidos.

Modeling of Thickness and Profile Uniformity of Thermally Sprayed Coatings Deposited on Cylinders

Science.gov (United States)

Yanjun, Zhang; Wenbo, Li; Dayu, Li; Jinkun, Xiao; Chao, Zhang

2018-02-01

In thermal spraying processes, kinematic parameters of the robot play a decisive role in the coating thickness and profile. In this regard, some achievements have been made to optimize the spray trajectory on flat surfaces. However, few reports have focused on nonholonomic or variable-curvature cylindrical surfaces. The aim of this study is to investigate the correlation between the coating profile, coating thickness, and scanning step, which is determined by the radius of curvature and scanning angle. A mathematical simulation model was developed to predict the thickness of thermally sprayed coatings. Experiments were performed on cylinders with different radiuses of curvature to evaluate the predictive ability of the model.

Net-erosion profile model and simulation experiments

International Nuclear Information System (INIS)

Sagara, Akio

2001-01-01

Estimation of net-erosion profile is requisite for evaluating the lifetime of divertor plates under high heat and particle fluxes of fusion plasmas. As a reference in benchmark tests of numerical calculation codes, a self-consistent analytical solution is presented for a simplified divertor condition, wherein the magnetic field line is normal to the target plate and the ionization mean free path of sputtered particles is assumed constant. The primary flux profile of hydrogen and impurities are externally given as well as the return ratio of sputtered atoms to the target. In the direction along the divertor trace, all conditions are uniform. The analytical solution is compared with net-erosion experiments carried out using the Compact Helical System (CHS). The deposition profiles of Ti and O impurities are in very good agreement with the analytical predictions. Recent preliminary results observed on divertor plates in the Large Helical Device (LHD) are briefly presented. (author)

Language profiles in young children with autism spectrum disorder: A community sample using multiple assessment instruments.

Science.gov (United States)

Nevill, Rose; Hedley, Darren; Uljarević, Mirko; Sahin, Ensu; Zadek, Johanna; Butter, Eric; Mulick, James A

2017-11-01

This study investigated language profiles in a community-based sample of 104 children aged 1-3 years who had been diagnosed with autism spectrum disorder using Diagnostic and Statistical Manual of Mental Disorders (5th ed.) diagnostic criteria. Language was assessed with the Mullen scales, Preschool Language Scale, fifth edition, and Vineland-II parent-report. The study aimed to determine whether the receptive-to-expressive language profile is independent from the assessment instrument used, and whether nonverbal cognition, early communicative behaviors, and autism spectrum disorder symptoms predict language scores. Receptive-to-expressive language profiles differed between assessment instruments and reporters, and Preschool Language Scale, fifth edition profiles were also dependent on developmental level. Nonverbal cognition and joint attention significantly predicted receptive language scores, and nonverbal cognition and frequency of vocalizations predicted expressive language scores. These findings support the administration of multiple direct assessment and parent-report instruments when evaluating language in young children with autism spectrum disorder, for both research and in clinical settings. Results also support that joint attention is a useful intervention target for improving receptive language skills in young children with autism spectrum disorder. Future research comparing language profiles of young children with autism spectrum disorder to children with non-autism spectrum disorder developmental delays and typical development will add to our knowledge of early language development in children with autism spectrum disorder.

MODELLING AND VIBRATION ANALYSIS OF A ROAD PROFILE MEASURING SYSTEM

Directory of Open Access Journals (Sweden)

C. B. Patel

2010-06-01

Full Text Available During a vehicle development program, load data representing severe customer usage is required. The dilemma faced by a design engineer during the design process is that during the initial stage, only predicted loads estimated from historical targets are available, whereas the actual loads are available only at the fag end of the process. At the same time, changes required, if any, are easier and inexpensive during the initial stages of the design process whereas they are extremely costly in the latter stages of the process. The use of road profiles and vehicle models to predict the load acting on the whole vehicle is currently being researched. This work hinges on the ability to accurately measure road profiles. The objective of the work is to develop an algorithm, using MATLAB Simulink software, to convert the input signals into measured road profile. The algorithm is checked by the MATLAB Simulink 4 degrees of freedom half car model. To make the whole Simulink model more realistic, accelerometer and laser sensor properties are introduced. The present work contains the simulation of the mentioned algorithm with a half car model and studies the results in distance, time, and the frequency domain.

Improvement of a predictive model in ovarian cancer patients submitted to platinum-based chemotherapy: implications of a GST activity profile.

Science.gov (United States)

Pereira, Deolinda; Assis, Joana; Gomes, Mónica; Nogueira, Augusto; Medeiros, Rui

2016-05-01

The success of chemotherapy in ovarian cancer (OC) is directly associated with the broad variability in platinum response, with implications in patients survival. This heterogeneous response might result from inter-individual variations in the platinum-detoxification pathway due to the expression of glutathione-S-transferase (GST) enzymes. We hypothesized that GSTM1 and GSTT1 polymorphisms might have an impact as prognostic and predictive determinants for OC. We conducted a hospital-based study in a cohort of OC patients submitted to platinum-based chemotherapy. GSTM1 and GSTT1 genotypes were determined by multiplex PCR. GSTM1-null genotype patients presented a significantly longer 5-year survival and an improved time to progression when compared with GSTM1-wt genotype patients (log-rank test, P = 0.001 and P = 0.013, respectively). Multivariate Cox regression analysis indicates that the inclusion of genetic information regarding GSTM1 polymorphism increased the predictive ability of risk of death after OC platinum-based chemotherapy (c-index from 0.712 to 0.833). Namely, residual disease (HR, 4.90; P = 0.016) and GSTM1-wt genotype emerged as more important predictors of risk of death (HR, 2.29; P = 0.039; P = 0.036 after bootstrap). No similar effect on survival was observed regarding GSTT1 polymorphism, and there were no statistically significant differences between GSTM1 and GSTT1 genotypes and the assessed patients' clinical-pathological characteristics. GSTM1 polymorphism seems to have an impact in OC prognosis as it predicts a better response to platinum-based chemotherapy and hence an improved survival. The characterization of the GSTM1 genetic profile might be a useful molecular tool and a putative genetic marker for OC clinical outcome.

Classification of breast cancer patients using somatic mutation profiles and machine learning approaches.

Science.gov (United States)

Vural, Suleyman; Wang, Xiaosheng; Guda, Chittibabu

2016-08-26

The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. Several classification strategies based on ER/PR/HER2 expression or the expression profiles of a panel of genes have helped, but such methods often produce misleading results due to their dynamic nature. In contrast, somatic DNA mutations are relatively stable and lead to initiation and progression of many sporadic cancers. Hence in this study, we explore the use of gene mutation profiles to classify, characterize and predict the subgroups of breast cancers. We analyzed the whole exome sequencing data from 358 ethnically similar breast cancer patients in The Cancer Genome Atlas (TCGA) project. Somatic and non-synonymous single nucleotide variants identified from each patient were assigned a quantitative score (C-score) that represents the extent of negative impact on the gene function. Using these scores with non-negative matrix factorization method, we clustered the patients into three subgroups. By comparing the clinical stage of patients, we identified an early-stage-enriched and a late-stage-enriched subgroup. Comparison of the mutation scores of early and late-stage-enriched subgroups identified 358 genes that carry significantly higher mutations rates in the late stage subgroup. Functional characterization of these genes revealed important functional gene families that carry a heavy mutational load in the late state rich subgroup of patients. Finally, using the identified subgroups, we also developed a supervised classification model to predict the stage of the patients. This study demonstrates that gene mutation profiles can be effectively used with unsupervised machine-learning methods to identify clinically distinguishable breast cancer subgroups. The classification model developed in this method could provide a reasonable

STUDENT ACADEMIC PERFORMANCE PREDICTION USING SUPPORT VECTOR MACHINE

OpenAIRE

S.A. Oloruntoba1 ,J.L.Akinode2

2017-01-01

This paper investigates the relationship between students' preadmission academic profile and final academic performance. Data Sample of students in one of the Federal Polytechnic in south West part of Nigeria was used. The preadmission academic profile used for this study is the 'O' level grades(terminal high school results).The academic performance is defined using student's Grade Point Average(GPA). This research focused on using data mining technique to develop a model for predicting stude...

Early Childhood Profiles of Sleep Problems and Self-Regulation Predict Later School Adjustment

Science.gov (United States)

Williams, Kate E.; Nicholson, Jan M.; Walker, Sue; Berthelsen, Donna

2016-01-01

Background: Children's sleep problems and self-regulation problems have been independently associated with poorer adjustment to school, but there has been limited exploration of longitudinal early childhood profiles that include both indicators. Aims: This study explores the normative developmental pathway for sleep problems and self-regulation…

Predicting Cyber Events by Leveraging Hacker Sentiment

OpenAIRE

Deb, Ashok; Lerman, Kristina; Ferrara, Emilio

2018-01-01

Recent high-profile cyber attacks exemplify why organizations need better cyber defenses. Cyber threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into groups' collective malicious activity. We propose a novel approach to predict cyber events using sentiment analysis. We test our approach using cyber attack data from 2 major ...

A Kernel for Protein Secondary Structure Prediction

OpenAIRE

Guermeur , Yann; Lifchitz , Alain; Vert , Régis

2004-01-01

http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10338&mode=toc; International audience; Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is in...

Predictive value of a profile of routine blood measurements on mortality in older persons in the general population: the Leiden 85-plus Study.

Directory of Open Access Journals (Sweden)

Anne H van Houwelingen

Full Text Available BACKGROUND: Various questionnaires and performance tests predict mortality in older people. However, most are heterogeneous, laborious and a validated consensus index is not available yet. Since most older people are regularly monitored by laboratory tests, we compared the predictive value of a profile of seven routine laboratory measurements on mortality in older persons in the general population with other predictors of mortality; gait speed and disability in instrumental activities of daily living (IADL. METHODOLOGY/PRINCIPAL FINDINGS: Within the Leiden 85-plus Study, a prospective population-based study, we followed 562 participants aged 85 years for mortality over five years. At baseline (age 85 years high-density lipoprotein cholesterol, albumin, alanine transaminase, hemoglobin, creatinin clearance, C-reactive protein and homocysteine were measured. Participants were stratified based on their number of laboratory abnormalities (0, 1, 2-4 and 5-7. The predictive capacity was compared with gait speed (6-meter walking test and disability in IADL (Groningen Activity Restriction Scale by C-statistics. At baseline, 418 (74% 85-year old participants had at least one laboratory abnormality. All cause mortality risk increased with increasing number of laboratory abnormalities to a hazard ratio of 5.64 [95% CI 3.49-9.12] for those with 5-7 laboratory abnormalities (p<0.001 compared to those without abnormalities. The c-statistic was 0.66 [95% CI 0.59-0.69], similar to that of gait speed and disability in IADL. CONCLUSIONS/SIGNIFICANCE: In the general population of oldest old, the number of abnormalities in seven routine laboratory measurements predicts five-year mortality as accurately as gait speed and IADL disability.

MEASURING THE ULTIMATE HALO MASS OF GALAXY CLUSTERS: REDSHIFTS AND MASS PROFILES FROM THE HECTOSPEC CLUSTER SURVEY (HeCS)

International Nuclear Information System (INIS)

Rines, Kenneth; Geller, Margaret J.; Kurtz, Michael J.; Diaferio, Antonaldo

2013-01-01

The infall regions of galaxy clusters represent the largest gravitationally bound structures in a ΛCDM universe. Measuring cluster mass profiles into the infall regions provides an estimate of the ultimate mass of these halos. We use the caustic technique to measure cluster mass profiles from galaxy redshifts obtained with the Hectospec Cluster Survey (HeCS), an extensive spectroscopic survey of galaxy clusters with MMT/Hectospec. We survey 58 clusters selected by X-ray flux at 0.1 200 , a new observational cosmological test in essential agreement with simulations. Summed profiles binned in M 200 and in L X demonstrate that the predicted Navarro-Frenk-White form of the density profile is a remarkably good representation of the data in agreement with weak lensing results extending to large radius. The concentration of these summed profiles is also consistent with theoretical predictions.

Current density profile inside q=1 on Tore Supra

International Nuclear Information System (INIS)

Joffrin, E.; Desgranges, C.; Sabot, R.; Dubois, M.A.

1995-01-01

The Tore Supra polarimeter used to measure the poloidal field distribution is described. The current density profiles are computed in two different ways using the interferometric and polarimetric data in conjunction with the magnetic data and the location of the inversion radius determined by the soft X-ray camera. The current density inside the q=1 surface is investigated for normal and monster sawteeth. Its variation are also measured by the polarimeter and compared with that predicted by the current diffusion equation assuming complete reconnection. Finally, the safety factor profile is compared with that obtained with the striation data of the pellet ablation. The results of the evolution of the q profile during sawteeth are in good agreement with those obtained in other devices. (author) 9 refs.; 4 figs

Radiogenomics: predicting clinical normal tissue radiosensitivity

DEFF Research Database (Denmark)

Alsner, Jan

2006-01-01

Studies on the genetic basis of normal tissue radiosensitivity, or  'radiogenomics', aims at predicting clinical radiosensitivity and optimize treatment from individual genetic profiles. Several studies have now reported links between variations in certain genes related to the biological response...... to radiation injury and risk of normal tissue morbidity in cancer patients treated with radiotherapy. However, after these initial association studies including few genes, we are still far from being able to predict clinical radiosensitivity on an individual level. Recent data from our own studies on risk...

Genomic Prediction of Testcross Performance in Canola (Brassica napus)

Science.gov (United States)

Jan, Habib U.; Abbadi, Amine; Lücke, Sophie; Nichols, Richard A.; Snowdon, Rod J.

2016-01-01

Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable

Forensic DNA Phenotyping: Predicting human appearance from crime scene material for investigative purposes.

Science.gov (United States)

Kayser, Manfred

2015-09-01

Forensic DNA Phenotyping refers to the prediction of appearance traits of unknown sample donors, or unknown deceased (missing) persons, directly from biological materials found at the scene. "Biological witness" outcomes of Forensic DNA Phenotyping can provide investigative leads to trace unknown persons, who are unidentifiable with current comparative DNA profiling. This intelligence application of DNA marks a substantially different forensic use of genetic material rather than that of current DNA profiling presented in the courtroom. Currently, group-specific pigmentation traits are already predictable from DNA with reasonably high accuracies, while several other externally visible characteristics are under genetic investigation. Until individual-specific appearance becomes accurately predictable from DNA, conventional DNA profiling needs to be performed subsequent to appearance DNA prediction. Notably, and where Forensic DNA Phenotyping shows great promise, this is on a (much) smaller group of potential suspects, who match the appearance characteristics DNA-predicted from the crime scene stain or from the deceased person's remains. Provided sufficient funding being made available, future research to better understand the genetic basis of human appearance will expectedly lead to a substantially more detailed description of an unknown person's appearance from DNA, delivering increased value for police investigations in criminal and missing person cases involving unknowns. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Artificial neural networks to evaluate the boron concentration decreasing profile in Blood-BPA samples of BNCT patients

Energy Technology Data Exchange (ETDEWEB)

Garcia-Reiriz, Alejandro, E-mail: garciareiriz@gmail.com [Department of Analytical Chemistry, Faculty of Biochemical and Pharmaceutical Sciences, National University of Rosario, Rosario Institute of Chemistry (IQUIR-CONICET), Suipacha 531, Rosario S2002LRK (Argentina); Magallanes, Jorge [Comision Nacional de Energia Atomica, Av. Gral. Paz 1499, San Martin, B1650KNA, Buenos Aires (Argentina); Zupan, Jure [National Institute of Chemistry, Hajdrihova 19, SLO-1000 Ljubljana, Eslovenia (Slovenia); Liberman, Sara [Comision Nacional de Energia Atomica, Av. Gral. Paz 1499, San Martin, B1650KNA, Buenos Aires (Argentina)

2011-12-15

For the prediction of decay concentration profiles of the p-boronophenylalanine (BPA) in blood during BNCT treatment, a method is suggested based on Kohonen neural networks. The results of a model trained with the concentration profiles from the literature are described. The prediction of the model was validated by the leave-one-out method. Its robustness shows that it is mostly independent on small variations. The ability to fit retrospective experimental data shows an uncertainty lower than the two compartment model used previously. - Highlights: Black-Right-Pointing-Pointer We predicted decaying concentration profiles of BPA in blood during BNCT therapy. Black-Right-Pointing-Pointer Is suggested a method based on Kohonen neural networks. Black-Right-Pointing-Pointer The results show that it is very robust and mostly independent of small variations. Black-Right-Pointing-Pointer It has a better ability to fit retrospective experimental data. Black-Right-Pointing-Pointer The model could be progressively improved by adding new data to the training matrix.

Artificial neural networks to evaluate the boron concentration decreasing profile in Blood-BPA samples of BNCT patients

International Nuclear Information System (INIS)

García-Reiriz, Alejandro; Magallanes, Jorge; Zupan, Jure; Líberman, Sara

2011-01-01

For the prediction of decay concentration profiles of the p-boronophenylalanine (BPA) in blood during BNCT treatment, a method is suggested based on Kohonen neural networks. The results of a model trained with the concentration profiles from the literature are described. The prediction of the model was validated by the leave-one-out method. Its robustness shows that it is mostly independent on small variations. The ability to fit retrospective experimental data shows an uncertainty lower than the two compartment model used previously. - Highlights: ► We predicted decaying concentration profiles of BPA in blood during BNCT therapy. ► Is suggested a method based on Kohonen neural networks. ► The results show that it is very robust and mostly independent of small variations. ► It has a better ability to fit retrospective experimental data. ► The model could be progressively improved by adding new data to the training matrix.

Profiles of Observed Infant Anger Predict Preschool Behavior Problems: Moderation by Life Stress

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Brooker, Rebecca J.; Buss, Kristin A.; Lemery-Chalfant, Kathryn; Aksan, Nazan; Davidson, Richard J.; Goldsmith, H. Hill

2014-01-01

Using both traditional composites and novel profiles of anger, we examined associations between infant anger and preschool behavior problems in a large, longitudinal data set (N = 966). We also tested the role of life stress as a moderator of the link between early anger and the development of behavior problems. Although traditional measures of…

Abbreviated kinetic profiles in area-under-the-curve monitoring of cyclosporine therapy.

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Grevel, J; Kahan, B D

1991-11-01

Abbreviated kinetic profiles can reduce the number of phlebotomies and drug assays, and thereby the cost of area-under-the-curve (AUC) monitoring. In the present investigation, we used two independent data sets: group 1, 101 AUC profiles from 77 stable renal-transplant patients, which included a 5-h sample in addition to the usual 0-, 2-, 4-, 6-, 10-, 14-, and 24-h samples; and group 2, 100 profiles from 50 stable renal-transplant patients before and after a change in their daily oral dose of cyclosporine. Group I demonstrated a fair correlation between cyclosporine trough concentrations and the AUC calculated from a complete set of seven concentrations (r2 = 0.820 and 0.758 for the 24- and 0-h samples, respectively). Stepwise multiple linear-regression analysis revealed that the abbreviated set of three time points (2, 6, and 14 h) explained 96% of the variance in AUC values calculated from the full set of seven samples; additional time points increased the accuracy only slightly. For group 2, we examined the difference between the observed and the predicted concentrations by linear extrapolation; the error in the observed AUC value, compared with the predicted value calculated from seven time points (-13.2% to -1.2%), was similar to the error from just three time points (-11.5% to 4.5%). Abbreviated AUC profiles involving three time points used with a model equation seem to provide a reliable alternative to full seven-point profiles.

Using the load-velocity relationship for 1RM prediction.

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Jidovtseff, Boris; Harris, Nigel K; Crielaard, Jean-Michel; Cronin, John B

2011-01-01

The purpose of this study was to investigate the ability of the load-velocity relationship to accurately predict a bench press 1 repetition maximum (1RM). Data from 3 different bench press studies (n = 112) that incorporated both 1RM assessment and submaximal load-velocity profiling were analyzed. Individual regression analysis was performed to determine the theoretical load at zero velocity (LD0). Data from each of the 3 studies were analyzed separately and also presented as overall group mean. Thereafter, correlation analysis provided quantification of the relationships between 1RM and LD0. Practically perfect correlations (r = ∼0.95) were observed in our samples, confirming the ability of the load-velocity profile to accurately predict bench press 1RM.

An algorithm to discover gene signatures with predictive potential

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Hallett Robin M

2010-09-01

Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

Experimental research on the durability cutting tools for cutting-off steel profiles

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Cristea Alexandru

2017-01-01

Full Text Available The production lines used for manufacturing U-shaped profiles are very complex and they must have high productivity. One of the most important stages of the fabrication process is the cutting-off. This paper presents the experimental research and analysis of the durability of the cutting tools used for cutting-off U-shaped metal steel profiles. The results of this work can be used to predict the durability of the cutting tools.

Stokes profile analysis and vector magnetic fields. III. Extended temperature minima of sunspot umbrae as inferred from Stokes profiles of Mg I 4571 A

International Nuclear Information System (INIS)

Lites, B.W.; Skumanich, A.; Rees, D.E.; Murphy, G.A.; Carlsson, M.; Sydney Univ., Australia; Oslo Universitetet, Norway)

1987-01-01

Observed Stokes profiles of Mg I 4571 A are analyzed as a diagnostic of the magnetic field and thermal structure at the temperature minimum of sunspot umbrae. Multilevel non-LTE transfer calculations of the Mg I-II-III excitation and ionization balance in model umbral atmospheres show: (1) Mg I to be far less ionized in sunspot umbrae than in the quiet sun, leading to greatly enhanced opacity in 4571 A, and (2) LTE excitation of 4571 A. Existing umbral models predict emission cores of the Stokes I profile due to the chromospheric temperature rise. This feature is not present in observed umbral profiles. Moreover, such an emission reversal causes similar anomalous features in the Stokes Q, U, V profiles, which are also not observed. Umbral atmospheres with extended temperature minima are suggested. Implications for chromospheric heating mechanisms and the utility of this line for solar vector magnetic field measurements are discussed. 35 references

Taxonomic and predicted metabolic profiles of the human gut microbiome in pre-Columbian mummies.

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Santiago-Rodriguez, Tasha M; Fornaciari, Gino; Luciani, Stefania; Dowd, Scot E; Toranzos, Gary A; Marota, Isolina; Cano, Raul J

2016-11-01

Characterization of naturally mummified human gut remains could potentially provide insights into the preservation and evolution of commensal and pathogenic microorganisms, and metabolic profiles. We characterized the gut microbiome of two pre-Columbian Andean mummies dating to the 10-15th centuries using 16S rRNA gene high-throughput sequencing and metagenomics, and compared them to a previously characterized gut microbiome of an 11th century AD pre-Columbian Andean mummy. Our previous study showed that the Clostridiales represented the majority of the bacterial communities in the mummified gut remains, but that other microbial communities were also preserved during the process of natural mummification, as shown with the metagenomics analyses. The gut microbiome of the other two mummies were mainly comprised by Clostridiales or Bacillales, as demonstrated with 16S rRNA gene amplicon sequencing, many of which are facultative anaerobes, possibly consistent with the process of natural mummification requiring low oxygen levels. Metagenome analyses showed the presence of other microbial groups that were positively or negatively correlated with specific metabolic profiles. The presence of sequences similar to both Trypanosoma cruzi and Leishmania donovani could suggest that these pathogens were prevalent in pre-Columbian individuals. Taxonomic and functional profiling of mummified human gut remains will aid in the understanding of the microbial ecology of the process of natural mummification. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Geospatial application of the Water Erosion Prediction Project (WEPP) Model

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D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

2011-01-01

The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

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Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

2017-10-01

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

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Cen Wan

2017-10-01

Full Text Available Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Off-target effects of psychoactive drugs revealed by genome-wide assays in yeast.

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Elke Ericson

2008-08-01

Full Text Available To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac interfered with establishment of cell polarity, cyproheptadine (Periactin targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol and pimozide (Orap. Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.

Effects of the use of a flat wire electrode in gas metal arc welding and fuzzy logic model for the prediction of weldment shape profile

Energy Technology Data Exchange (ETDEWEB)

Karuthapandi, Sripriyan; Thyla, P. R. [PSG College of Technology, Coimbatore (India); Ramu, Murugan [Amrita University, Ettimadai (India)

2017-05-15

This paper describes the relationships between the macrostructural characteristics of weld beads and the welding parameters in Gas metal arc welding (GMAW) using a flat wire electrode. Bead-on-plate welds were produced with a flat wire electrode and different combinations of input parameters (i.e., welding current, welding speed, and flat wire electrode orientation). The macrostructural characteristics of the weld beads, namely, deposition, bead width, total bead width, reinforcement height, penetration depth, and depth of HAZ were investigated. A mapping technique was employed to measure these characteristics in various segments of the weldment zones. Results show that the use of a flat wire electrode improves the depth-to-width (D/W) ratio by 16.5 % on average compared with the D/W ratio when a regular electrode is used in GMAW. Furthermore, a fuzzy logic model was established to predict the effects of the use of a flat electrode on the weldment shape profile with varying input parameters. The predictions of the model were compared with the experimental results.

Real Mission Profile Based Lifetime Estimation of Fuel-cell Power Converter

DEFF Research Database (Denmark)

Zhou, Dao; Wang, Huai; Blaabjerg, Frede

2016-01-01

. This paper describes a lifetime prediction method for the power semiconductors used in the power conditioning of a fuel cell based backup system, considering both the long-term standby mode and active operation mode. The annual ambient temperature profile is taken into account to estimate its impact...... on the degradation of MOSFETs during the standby mode. At the presence of power outages, the backup system is activated into the operation mode and the MOSFETs withstand additional thermal stresses due to power losses. A study case of a 1 kW backup system is presented with two annual mission profiles in Denmark...... and India, respectively. The ambient temperature, occurrence frequency of power outages, active operation time and power levels are considered for the lifetime prediction of the applied MOSFETs. Comparisons of the accumulated lifetime consumptions are performed between standby mode and operation mode...

CPHmodels-3.0--remote homology modeling using structure-guided sequence profiles

DEFF Research Database (Denmark)

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2010-01-01

CPHmodels-3.0 is a web server predicting protein 3D structure by use of single template homology modeling. The server employs a hybrid of the scoring functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A query sequence is first attempted modeled using the fast CPHmodels-2.......0 profile-profile scoring function suitable for close homology modeling. The new computational costly remote homology-modeling algorithm is only engaged provided that no suitable PDB template is identified in the initial search. CPHmodels-3.0 was benchmarked in the CASP8 competition and produced models.......3 A. These performance values place the CPHmodels-3.0 method in the group of high performing 3D prediction tools. Beside its accuracy, one of the important features of the method is its speed. For most queries, the response time of the server is...

Well-log based prediction of thermal conductivity

DEFF Research Database (Denmark)

Fuchs, Sven; Förster, Andrea

Rock thermal conductivity (TC) is paramount for the determination of heat flow and the calculation of temperature profiles. Due to the scarcity of drill cores compared to the availability of petrophysical well logs, methods are desired to indirectly predict TC in sedimentary basins. Most...

Emotional labor actors: a latent profile analysis of emotional labor strategies.

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Gabriel, Allison S; Daniels, Michael A; Diefendorff, James M; Greguras, Gary J

2015-05-01

Research on emotional labor focuses on how employees utilize 2 main regulation strategies-surface acting (i.e., faking one's felt emotions) and deep acting (i.e., attempting to feel required emotions)-to adhere to emotional expectations of their jobs. To date, researchers largely have considered how each strategy functions to predict outcomes in isolation. However, this variable-centered perspective ignores the possibility that there are subpopulations of employees who may differ in their combined use of surface and deep acting. To address this issue, we conducted 2 studies that examined surface acting and deep acting from a person-centered perspective. Using latent profile analysis, we identified 5 emotional labor profiles-non-actors, low actors, surface actors, deep actors, and regulators-and found that these actor profiles were distinguished by several emotional labor antecedents (positive affectivity, negative affectivity, display rules, customer orientation, and emotion demands-abilities fit) and differentially predicted employee outcomes (emotional exhaustion, job satisfaction, and felt inauthenticity). Our results reveal new insights into the nature of emotion regulation in emotional labor contexts and how different employees may characteristically use distinct combinations of emotion regulation strategies to manage their emotional expressions at work. (c) 2015 APA, all rights reserved.

Transcript Profiling Distinguishes Complete Treatment Responders With Locally Advanced Cervical Cancer

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Jorge Fernandez-Retana

2015-04-01

Full Text Available Cervical cancer (CC mortality is a major public health concern since it is the second cause of cancer-related deaths among women. Patients diagnosed with locally advanced CC (LACC have an important rate of recurrence and treatment failure. Conventional treatment for LACC is based on chemotherapy and radiotherapy; however, up to 40% of patients will not respond to conventional treatment; hence, we searched for a prognostic gene signature able to discriminate patients who do not respond to the conventional treatment employed to treat LACC. Tumor biopsies were profiled with genome-wide high-density expression microarrays. Class prediction was performed in tumor tissues and the resultant gene signature was validated by quantitative reverse transcription–polymerase chain reaction. A 27-predictive gene profile was identified through its association with pathologic response. The 27-gene profile was validated in an independent set of patients and was able to distinguish between patients diagnosed as no response versus complete response. Gene expression analysis revealed two distinct groups of tumors diagnosed as LACC. Our findings could provide a strategy to select patients who would benefit from neoadjuvant radiochemotherapy-based treatment.

Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles.

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Zhu, Jie; Qin, Yufang; Liu, Taigang; Wang, Jun; Zheng, Xiaoqi

2013-01-01

Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance. In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports. Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes. Programs and data are available upon request.

Prediction du profil de durete de l'acier AISI 4340 traite thermiquement au laser

Science.gov (United States)

Maamri, Ilyes

Les traitements thermiques de surfaces sont des procedes qui visent a conferer au coeur et a la surface des pieces mecaniques des proprietes differentes. Ils permettent d'ameliorer la resistance a l'usure et a la fatigue en durcissant les zones critiques superficielles par des apports thermiques courts et localises. Parmi les procedes qui se distinguent par leur capacite en terme de puissance surfacique, le traitement thermique de surface au laser offre des cycles thermiques rapides, localises et precis tout en limitant les risques de deformations indesirables. Les proprietes mecaniques de la zone durcie obtenue par ce procede dependent des proprietes physicochimiques du materiau a traiter et de plusieurs parametres du procede. Pour etre en mesure d'exploiter adequatement les ressources qu'offre ce procede, il est necessaire de developper des strategies permettant de controler et regler les parametres de maniere a produire avec precision les caracteristiques desirees pour la surface durcie sans recourir au classique long et couteux processus essai-erreur. L'objectif du projet consiste donc a developper des modeles pour predire le profil de durete dans le cas de traitement thermique de pieces en acier AISI 4340. Pour comprendre le comportement du procede et evaluer les effets des differents parametres sur la qualite du traitement, une etude de sensibilite a ete menee en se basant sur une planification experimentale structuree combinee a des techniques d'analyse statistiques eprouvees. Les resultats de cette etude ont permis l'identification des variables les plus pertinentes a exploiter pour la modelisation. Suite a cette analyse et dans le but d'elaborer un premier modele, deux techniques de modelisation ont ete considerees, soient la regression multiple et les reseaux de neurones. Les deux techniques ont conduit a des modeles de qualite acceptable avec une precision d'environ 90%. Pour ameliorer les performances des modeles a base de reseaux de neurones, deux

Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

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Muhammad

2017-01-01

Full Text Available We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy model, the so-called Improved Profile-Energy (IPro-Energy, and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM, Pro-Energy, and Weather Conditioned Moving Average (WCMA. The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.

Concomitant prediction of function and fold at the domain level with GO-based profiles.

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Lopez, Daniel; Pazos, Florencio

2013-01-01

Predicting the function of newly sequenced proteins is crucial due to the pace at which these raw sequences are being obtained. Almost all resources for predicting protein function assign functional terms to whole chains, and do not distinguish which particular domain is responsible for the allocated function. This is not a limitation of the methodologies themselves but it is due to the fact that in the databases of functional annotations these methods use for transferring functional terms to new proteins, these annotations are done on a whole-chain basis. Nevertheless, domains are the basic evolutionary and often functional units of proteins. In many cases, the domains of a protein chain have distinct molecular functions, independent from each other. For that reason resources with functional annotations at the domain level, as well as methodologies for predicting function for individual domains adapted to these resources are required.We present a methodology for predicting the molecular function of individual domains, based on a previously developed database of functional annotations at the domain level. The approach, which we show outperforms a standard method based on sequence searches in assigning function, concomitantly predicts the structural fold of the domains and can give hints on the functionally important residues associated to the predicted function.

MiRNA Profiles in Lymphoblastoid Cell Lines of Finnish Prostate Cancer Families.

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Daniel Fischer

Full Text Available Heritable factors are evidently involved in prostate cancer (PrCa carcinogenesis, but currently, genetic markers are not routinely used in screening or diagnostics of the disease. More precise information is needed for making treatment decisions to distinguish aggressive cases from indolent disease, for which heritable factors could be a useful tool. The genetic makeup of PrCa has only recently begun to be unravelled through large-scale genome-wide association studies (GWAS. The thus far identified Single Nucleotide Polymorphisms (SNPs explain, however, only a fraction of familial clustering. Moreover, the known risk SNPs are not associated with the clinical outcome of the disease, such as aggressive or metastasised disease, and therefore cannot be used to predict the prognosis. Annotating the SNPs with deep clinical data together with miRNA expression profiles can improve the understanding of the underlying mechanisms of different phenotypes of prostate cancer.In this study microRNA (miRNA profiles were studied as potential biomarkers to predict the disease outcome. The study subjects were from Finnish high risk prostate cancer families. To identify potential biomarkers we combined a novel non-parametrical test with an importance measure provided from a Random Forest classifier. This combination delivered a set of nine miRNAs that was able to separate cases from controls. The detected miRNA expression profiles could predict the development of the disease years before the actual PrCa diagnosis or detect the existence of other cancers in the studied individuals. Furthermore, using an expression Quantitative Trait Loci (eQTL analysis, regulatory SNPs for miRNA miR-483-3p that were also directly associated with PrCa were found.Based on our findings, we suggest that blood-based miRNA expression profiling can be used in the diagnosis and maybe even prognosis of the disease. In the future, miRNA profiling could possibly be used in targeted screening

A finite element thermohydrodynamic analyis of profile bore bearing

International Nuclear Information System (INIS)

Shah Nor bin Basri

1994-01-01

A finite element-based method is presented for analysing the thermohydrodynamic (THD) behaviour of profile bore bearing. A variational statement for the governing equation is derived and used to formulate a non-linear quadrilateral finite element of serendipity family. The predicted behaviour is compared with experimental evidence where possible and favorable correlation is obtained

Gene expression signatures that predict radiation exposure in mice and humans.

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Holly K Dressman

2007-04-01

Full Text Available The capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation.We have made use of gene expression analysis of peripheral blood (PB mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans.We suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.

The Valuable Role of Measuring Serum Lipid Profile in Cancer Progression

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Farahnaz Ghahremanfard

2015-09-01

Full Text Available Objective: Serum lipid levels are not only associated with etiology, but also with prognosis in cancer. To investigate this issue further, we aimed to evaluate the serum levels of lipids in association with the most important prognostic indicators in cancer patients at the start of chemotherapy. Methods: In a retrospective cross-sectional study, using existing medical records obtained from 2009–2014, the data of all incident cancer cases in Iranian patients referred to the Semnan oncology clinic for chemotherapy were analyzed. Data on demographics, cancer type, prognostic indicators (e.g. lymph node involvement, metastasis, and stage of disease, as well as the patient’s lipid profile were collected. We used multiple logistic regression models to show the relationship between prognosis indicators and lipid profile adjusting for age, gender, and type of cancer. Results: The data of 205 patients was gathered. We found a significant difference in the lipid profile between different types of cancers (breast, colon, gastric, and ovarian. With the exception of high-density lipoprotein levels in women, which were higher than in men, the means of other lipid profiles were similar between the genders. There was a significant association between higher levels of low-density lipoprotein (LDL >110mg/dL in the serum and metastasis (adjusted odds ratio=2.4, 95% CI 1.2–3.5. No significant association was reported between lipid profile and lymph nodes involvement and stage of the disease. Conclusion: Our study suggested a benefit of measuring serum levels of lipids for predicting cancer progression. Increased LDL levels can be considered a predictive factor for increasing the risk of metastasis.

Halo mass and weak galaxy-galaxy lensing profiles in rescaled cosmological N-body simulations

Science.gov (United States)

Renneby, Malin; Hilbert, Stefan; Angulo, Raúl E.

2018-05-01

We investigate 3D density and weak lensing profiles of dark matter haloes predicted by a cosmology-rescaling algorithm for N-body simulations. We extend the rescaling method of Angulo & White (2010) and Angulo & Hilbert (2015) to improve its performance on intra-halo scales by using models for the concentration-mass-redshift relation based on excursion set theory. The accuracy of the method is tested with numerical simulations carried out with different cosmological parameters. We find that predictions for median density profiles are more accurate than ˜5 % for haloes with masses of 1012.0 - 1014.5h-1 M⊙ for radii 0.05 baryons, are likely required for interpreting future (dark energy task force stage IV) experiments.

Motivational Profiles and Motivation for Lifelong Learning of Medical Specialists.

Science.gov (United States)

van der Burgt, Stéphanie M E; Kusurkar, Rashmi A; Wilschut, Janneke A; Tjin A Tsoi, Sharon L N M; Croiset, Gerda; Peerdeman, Saskia M

2018-05-22

Medical specialists face the challenge of maintaining their knowledge and skills and continuing professional development, that is, lifelong learning. Motivation may play an integral role in many of the challenges facing the physician workforce today including maintenance of a high performance. The aim of this study was to determine whether medical specialists show different motivational profiles and if these profiles predict differences in motivation for lifelong learning. An online questionnaire was sent to every medical specialist working in five hospitals in the Netherlands. The questionnaire included the validated Multidimensional Work Motivation Scale and the Jefferson Scale of Physician Lifelong Learning together with background questions like age, gender, and type of hospital. Respondents were grouped into different motivational profiles by using a two-step clustering approach. Four motivational profiles were identified: (1) HAMC profile (for High Autonomous and Moderate Controlled motivation), (2) MAMC profile (for Moderate Autonomous and Moderate Controlled motivation), (3) MALC profile (for Moderate Autonomous and Low Controlled motivation), and (4) HALC profile (for High Autonomous and Low Controlled motivation). Most of the female specialists that work in an academic hospital and specialists with a surgical specialty were represented in the HALC profile. Four motivational profiles were found among medical specialists, differing in gender, experience and type of specialization. The profiles are based on the combination of autonomous motivation (AM) and controlled motivation (CM) in the specialists. The profiles that have a high score on autonomous motivation have a positive association with lifelong learning.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work

Comparison of percentage body fat and body mass index for the prediction of inflammatory and atherogenic lipid risk profiles in elderly women

Directory of Open Access Journals (Sweden)

Funghetto SS

2015-01-01

Full Text Available Silvana Schwerz Funghetto,1 Alessandro de Oliveira Silva,2 Nuno Manuel Frade de Sousa,3 Marina Morato Stival,1 Ramires Alsamir Tibana,4 Leonardo Costa Pereira,1 Marja Letícia Chaves Antunes,1 Luciano Ramos de Lima,1 Jonato Prestes,4 Ricardo Jacó Oliveira,1 Maurílio Tiradentes Dutra,2 Vinícius Carolino Souza,1,4 Dahan da Cunha Nascimento,4 Margô Gomes de Oliveira Karnikowski1 1University of Brasília (UnB, Brasília, DF, Brazil; 2Center University of Brasilia (UNICEUB, Brasilia, DF, Brazil; 3Laboratory of Exercise Physiology, Faculty Estácio de Sá of Vitória, ES, Brazil; 4Catholic University of Brasília, Brasília, DF, Brazil Objective: To compare the clinical classification of the body mass index (BMI and percentage body fat (PBF for the prediction of inflammatory and atherogenic lipid profile risk in older women.Method: Cross-sectional analytical study with 277 elderly women from a local community in the Federal District, Brazil. PBF and fat-free mass (FFM were determined by dual energy X-ray absorptiometry. The investigated inflammatory parameters were interleukin 6 and C-reactive protein.Results: Twenty-five percent of the elderly women were classified as normal weight, 50% overweight, and 25% obese by the BMI. The obese group had higher levels of triglycerides and very low-density lipoproteins than did the normal weight group (P≤0.05 and lower levels of high-density lipoproteins (HDL than did the overweight group (P≤0.05. According to the PBF, 49% of the elderly women were classified as eutrophic, 28% overweight, and 23% obese. In the binomial logistic regression analyses including age, FFM, and lipid profile, only FFM (odds ratio [OR]=0.809, 95% confidence interval [CI]: 0.739–0.886; P<0.0005 proved to be a predictor of reaching the eutrophic state by the BMI. When the cutoff points of PBF were used for the classification, FFM (OR=0.903, CI=0.884–0.965; P=0.003 and the total cholesterol/HDL ratio (OR=0.113, CI=0.023–0

Sun Protection Motivational Stages and Behavior: Skin Cancer Risk Profiles

Science.gov (United States)

Pagoto, Sherry L.; McChargue, Dennis E.; Schneider, Kristin; Cook, Jessica Werth

2004-01-01

Objective: To create skin cancer risk profiles that could be used to predict sun protection among Midwest beachgoers. Method: Cluster analysis was used with study participants (N=239), who provided information about sun protection motivation and behavior, perceived risk, burn potential, and tan importance. Participants were clustered according to…

A Metagenomic and in Silico Functional Prediction of Gut Microbiota Profiles May Concur in Discovering New Cystic Fibrosis Patient-Targeted Probiotics.

Science.gov (United States)

Vernocchi, Pamela; Del Chierico, Federica; Quagliariello, Andrea; Ercolini, Danilo; Lucidi, Vincenzina; Putignani, Lorenza

2017-12-09

Cystic fibrosis (CF) is a life-limiting hereditary disorder that results in aberrant mucosa in the lungs and digestive tract, chronic respiratory infections, chronic inflammation, and the need for repeated antibiotic treatments. Probiotics have been demonstrated to improve the quality of life of CF patients. We investigated the distribution of gut microbiota (GM) bacteria to identify new potential probiotics for CF patients on the basis of GM patterns. Fecal samples of 28 CF patients and 31 healthy controls (HC) were collected and analyzed by 16S rRNA-based pyrosequencing analysis of GM, to produce CF-HC paired maps of the distribution of operational taxonomic units (OTUs), and by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) for Kyoto Encyclopedia of Genes and Genomes (KEGG) biomarker prediction. The maps were scanned to highlight the distribution of bacteria commonly claimed as probiotics, such as bifidobacteria and lactobacilli, and of butyrate-producing colon bacteria, such as Eubacterium spp. and Faecalibacterium prausnitzii. The analyses highlighted 24 OTUs eligible as putative probiotics. Eleven and nine species were prevalently associated with the GM of CF and HC subjects, respectively. Their KEGG prediction provided differential CF and HC pathways, indeed associated with health-promoting biochemical activities in the latter case. GM profiling and KEGG biomarkers concurred in the evaluation of nine bacterial species as novel putative probiotics that could be investigated for the nutritional management of CF patients.

A Metagenomic and in Silico Functional Prediction of Gut Microbiota Profiles May Concur in Discovering New Cystic Fibrosis Patient-Targeted Probiotics

Directory of Open Access Journals (Sweden)

Pamela Vernocchi

2017-12-01

Full Text Available Cystic fibrosis (CF is a life-limiting hereditary disorder that results in aberrant mucosa in the lungs and digestive tract, chronic respiratory infections, chronic inflammation, and the need for repeated antibiotic treatments. Probiotics have been demonstrated to improve the quality of life of CF patients. We investigated the distribution of gut microbiota (GM bacteria to identify new potential probiotics for CF patients on the basis of GM patterns. Fecal samples of 28 CF patients and 31 healthy controls (HC were collected and analyzed by 16S rRNA-based pyrosequencing analysis of GM, to produce CF-HC paired maps of the distribution of operational taxonomic units (OTUs, and by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt for Kyoto Encyclopedia of Genes and Genomes (KEGG biomarker prediction. The maps were scanned to highlight the distribution of bacteria commonly claimed as probiotics, such as bifidobacteria and lactobacilli, and of butyrate-producing colon bacteria, such as Eubacterium spp. and Faecalibacterium prausnitzii. The analyses highlighted 24 OTUs eligible as putative probiotics. Eleven and nine species were prevalently associated with the GM of CF and HC subjects, respectively. Their KEGG prediction provided differential CF and HC pathways, indeed associated with health-promoting biochemical activities in the latter case. GM profiling and KEGG biomarkers concurred in the evaluation of nine bacterial species as novel putative probiotics that could be investigated for the nutritional management of CF patients.

Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

International Nuclear Information System (INIS)

Gevaert, Olivier; De Smet, Frank; Van Gorp, Toon; Pochet, Nathalie; Engelen, Kristof; Amant, Frederic; De Moor, Bart; Timmerman, Dirk; Vergote, Ignace

2008-01-01

In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs. The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study – although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours – were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models. We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology

Behavioural profiles are shaped by social experience: when, how and why.

Science.gov (United States)

Sachser, Norbert; Kaiser, Sylvia; Hennessy, Michael B

2013-05-19

The comprehensive understanding of individual variation in behavioural profiles is a current and timely topic not only in behavioural ecology, but also in biopsychological and biomedical research. This study focuses on the shaping of behavioural profiles by the social environment in mammals. We review evidence that the shaping of behavioural profiles occurs from the prenatal phase through adolescence and beyond. We focus specifically on adolescence, a sensitive phase during which environmental stimuli have distinctive effects on the modulation of behavioural profiles. We discuss causation, in particular, how behavioural profiles are shaped by social stimuli through behavioural and neuroendocrine processes. We postulate a central role for maternal hormones during the prenatal phase, for maternal behaviour during lactation and for the interaction of testosterone and stress hormones during adolescence. We refer to evolutionary history and attempt to place developmental shaping into broader evolutionary historical trends. Finally, we address survival value. We argue that the shaping of behavioural profiles by environmental stimuli from the prenatal phase through adolescence represents an effective mechanism for repeated and rapid adaptation during the lifetime. Notably, the adolescent phase may provide a last chance for correction if the future environment deviates from that predicted in earlier phases.

Objective Classification of Radar Profile Types, and Their Relationship to Lightning Occurrence

Science.gov (United States)

Boccippio, Dennis

2003-01-01

A cluster analysis technique is used to identify 16 "archetypal" vertical radar profile types from a large, globally representative sample of profiles from the TRMM Precipitation Radar. These include nine convective types (7 of these deep convective) and seven stratiform types (5 of these clearly glaciated). Radar profile classification provides an alternative to conventional deep convective storm metrics, such as 30 dBZ echo height, maximum reflectivity or VIL. As expected, the global frequency of occurrence of deep convective profile types matches satellite-observed total lightning production, including to very small scall local features. Each location's "mix" of profile types provides an objective description of the local convective spectrum, and in turn, is a first step in objectively classifying convective regimes. These classifiers are tested as inputs to a neural network which attempts to predict lightning occurrence based on radar-only storm observations, and performance is compared with networks using traditional radar metrics as inputs.

SU-C-204-01: A Fast Analytical Approach for Prompt Gamma and PET Predictions in a TPS for Proton Range Verification

International Nuclear Information System (INIS)

Kroniger, K; Herzog, M; Landry, G; Dedes, G; Parodi, K; Traneus, E

2015-01-01

Purpose: We describe and demonstrate a fast analytical tool for prompt-gamma emission prediction based on filter functions applied on the depth dose profile. We present the implementation in a treatment planning system (TPS) of the same algorithm for positron emitter distributions. Methods: The prediction of the desired observable is based on the convolution of filter functions with the depth dose profile. For both prompt-gammas and positron emitters, the results of Monte Carlo simulations (MC) are compared with those of the analytical tool. For prompt-gamma emission from inelastic proton-induced reactions, homogeneous and inhomogeneous phantoms alongside with patient data are used as irradiation targets of mono-energetic proton pencil beams. The accuracy of the tool is assessed in terms of the shape of the analytically calculated depth profiles and their absolute yields, compared to MC. For the positron emitters, the method is implemented in a research RayStation TPS and compared to MC predictions. Digital phantoms and patient data are used and positron emitter spatial density distributions are analyzed. Results: Calculated prompt-gamma profiles agree with MC within 3 % in terms of absolute yield and reproduce the correct shape. Based on an arbitrary reference material and by means of 6 filter functions (one per chemical element), profiles in any other material composed of those elements can be predicted. The TPS implemented algorithm is accurate enough to enable, via the analytically calculated positron emitters profiles, detection of range differences between the TPS and MC with errors of the order of 1–2 mm. Conclusion: The proposed analytical method predicts prompt-gamma and positron emitter profiles which generally agree with the distributions obtained by a full MC. The implementation of the tool in a TPS shows that reliable profiles can be obtained directly from the dose calculated by the TPS, without the need of full MC simulation

In silico modeling predicts drug sensitivity of patient-derived cancer cells.

Science.gov (United States)

Pingle, Sandeep C; Sultana, Zeba; Pastorino, Sandra; Jiang, Pengfei; Mukthavaram, Rajesh; Chao, Ying; Bharati, Ila Sri; Nomura, Natsuko; Makale, Milan; Abbasi, Taher; Kapoor, Shweta; Kumar, Ansu; Usmani, Shahabuddin; Agrawal, Ashish; Vali, Shireen; Kesari, Santosh

2014-05-21

Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

Validation of Lifetime Prediction of IGBT Modules Based on Linear Damage Accumulation by Means of Superimposed Power Cycling Tests

DEFF Research Database (Denmark)

Choi, Ui-Min; Ma, Ke; Blaabjerg, Frede

2018-01-01

In this paper, the lifetime prediction of power device modules based on the linear damage accumulation is studied in conjunction with simple mission profiles of converters. Superimposed power cycling conditions, which are called simple mission profiles in this paper, are made based on a lifetime ...... prediction of IGBT modules under power converter applications.......In this paper, the lifetime prediction of power device modules based on the linear damage accumulation is studied in conjunction with simple mission profiles of converters. Superimposed power cycling conditions, which are called simple mission profiles in this paper, are made based on a lifetime...... model in respect to junction temperature swing duration. This model has been built based on 39 power cycling test results of 600-V 30-A three-phase-molded IGBT modules. Six tests are performed under three superimposed power cycling conditions using an advanced power cycling test setup. The experimental...

Evaluation of tunnel seismic prediction (TSP) result using the Japanese highway rock mass classification system for Pahang-Selangor Raw Water Transfer Tunnel

Science.gov (United States)

Von, W. C.; Ismail, M. A. M.

2017-10-01

The knowing of geological profile ahead of tunnel face is significant to minimize the risk in tunnel excavation work and cost control in preventative measure. Due to mountainous area, site investigation with vertical boring is not recommended to obtain the geological profile for Pahang-Selangor Raw Water Transfer project. Hence, tunnel seismic prediction (TSP) method is adopted to predict the geological profile ahead of tunnel face. In order to evaluate the TSP results, IBM SPSS Statistic 22 is used to run artificial neural network (ANN) analysis to back calculate the predicted Rock Grade Points (JH) from actual Rock Grade Points (JH) using Vp, Vs and Vp/Vs from TSP. The results show good correlation between predicted Rock Grade points and actual Rock Grade Points (JH). In other words, TSP can provide geological profile prediction ahead of tunnel face significantly while allowing continuously TBM excavation works. Identifying weak zones or faults ahead of tunnel face is crucial for preventative measures to be carried out in advance for a safer tunnel excavation works.

Profiles of Wind and Turbulence in the Coastal Atmospheric Boundary Layer of Lake Erie

KAUST Repository

Wang, H

2014-06-16

Prediction of wind resource in coastal zones is difficult due to the complexity of flow in the coastal atmospheric boundary layer (CABL). A three week campaign was conducted over Lake Erie in May 2013 to investigate wind characteristics and improve model parameterizations in the CABL. Vertical profiles of wind speed up to 200 m were measured onshore and offshore by lidar wind profilers, and horizontal gradients of wind speed by a 3-D scanning lidar. Turbulence data were collected from sonic anemometers deployed onshore and offshore. Numerical simulations were conducted with the Weather Research Forecasting (WRF) model with 2 nested domains down to a resolution of 1-km over the lake. Initial data analyses presented in this paper investigate complex flow patterns across the coast. Acceleration was observed up to 200 m above the surface for flow coming from the land to the water. However, by 7 km off the coast the wind field had not yet reached equilibrium with the new surface (water) conditions. The surface turbulence parameters over the water derived from the sonic data could not predict wind profiles observed by the ZephlR lidar located offshore. Horizontal wind speed gradients near the coast show the influence of atmospheric stability on flow dynamics. Wind profiles retrieved from the 3-D scanning lidar show evidence of nocturnal low level jets (LLJs). The WRF model was able to capture the occurrence of LLJ events, but its performance varied in predicting their intensity, duration, and the location of the jet core.

CD4+ T-cell epitope prediction using antigen processing constraints.

Science.gov (United States)

Mettu, Ramgopal R; Charles, Tysheena; Landry, Samuel J

2016-05-01

T-cell CD4+ epitopes are important targets of immunity against infectious diseases and cancer. State-of-the-art methods for MHC class II epitope prediction rely on supervised learning methods in which an implicit or explicit model of sequence specificity is constructed using a training set of peptides with experimentally tested MHC class II binding affinity. In this paper we present a novel method for CD4+ T-cell eptitope prediction based on modeling antigen-processing constraints. Previous work indicates that dominant CD4+ T-cell epitopes tend to occur adjacent to sites of initial proteolytic cleavage. Given an antigen with known three-dimensional structure, our algorithm first aggregates four types of conformational stability data in order to construct a profile of stability that allows us to identify regions of the protein that are most accessible to proteolysis. Using this profile, we then construct a profile of epitope likelihood based on the pattern of transitions from unstable to stable regions. We validate our method using 35 datasets of experimentally measured CD4+ T cell responses of mice bearing I-Ab or HLA-DR4 alleles as well as of human subjects. Overall, our results show that antigen processing constraints provide a significant source of predictive power. For epitope prediction in single-allele systems, our approach can be combined with sequence-based methods, or used in instances where little or no training data is available. In multiple-allele systems, sequence-based methods can only be used if the allele distribution of a population is known. In contrast, our approach does not make use of MHC binding prediction, and is thus agnostic to MHC class II genotypes. Copyright © 2016 Elsevier B.V. All rights reserved.

Turbulence Dissipation Rates in the Planetary Boundary Layer from Wind Profiling Radars and Mesoscale Numerical Weather Prediction Models during WFIP2

Science.gov (United States)

Bianco, L.; McCaffrey, K.; Wilczak, J. M.; Olson, J. B.; Kenyon, J.

2016-12-01

When forecasting winds at a wind plant for energy production, the turbulence parameterizations in the forecast models are crucial for understanding wind plant performance. Recent research shows that the turbulence (eddy) dissipation rate in planetary boundary layer (PBL) parameterization schemes introduces significant uncertainty in the Weather Research and Forecasting (WRF) model. Thus, developing the capability to measure dissipation rates in the PBL will allow for identification of weaknesses in, and improvements to the parameterizations. During a preliminary field study at the Boulder Atmospheric Observatory in spring 2015, a 915-MHz wind profiling radar (WPR) measured dissipation rates concurrently with sonic anemometers mounted on a 300-meter tower. WPR set-up parameters (e.g., spectral resolution), post-processing techniques (e.g., filtering for non-atmospheric signals), and spectral averaging were optimized to capture the most accurate Doppler spectra for measuring spectral widths for use in the computation of the eddy dissipation rates. These encouraging results lead to the implementation of the observing strategy on a 915-MHz WPR in Wasco, OR, operating as part of the Wind Forecasting Improvement Project 2 (WFIP2). These observations are compared to dissipation rates calculated from the High-Resolution Rapid Refresh model, a WRF-based mesoscale numerical weather prediction model run for WFIP2 at 3000 m horizontal grid spacing and with a nest, which has 750-meter horizontal grid spacing, in the complex terrain region of the Columbia River Gorge. The observed profiles of dissipation rates are used to evaluate the PBL parameterization schemes used in the HRRR model, which are based on the modeled turbulent kinetic energy and a tunable length scale.

Prediction of crank torque and pedal angle profiles during pedaling movements by biomechanical optimization

DEFF Research Database (Denmark)

Farahani, Saeed Davoudabadi; Bertucci, William; Andersen, Michael Skipper

2015-01-01

This paper introduces the inverse-inverse dynamics method for prediction of human movement and applies it to prediction of cycling motions. Inverse-inverse dynamics optimizes a performance criterion by variation of a parameterized movement. First, a musculoskeletal model of cycling is built in th...

Numerical calculation of 'actual' radial profile of ion temperature from 'measured' energy spectra of charge-exchanged neutrals

Energy Technology Data Exchange (ETDEWEB)

Nakamura, Kazuo; Hiraki, Naoji; Toi, Kazuo; Itoh, Satoshi

1984-10-01

The energy spectra of charge-exchanged neutrals are observed in the TRIAM-1 tokamak by vertical scanning of the neutral energy analyzer. The ''apparent'' ion temperature obtained directly from the energy spectrum observed in the peripheral region is much higher than that predicted by neoclassical transport theory. The ''actual'' ion temperature profile is derived numerically from the energy spectra observed at various positions taking into account the wall-reflection effect of neutrals and the impermeability of the plasma. As a result, the ''actual'' ion temperature profile is found to agree well with that predicted by neoclassical transport theory.

Numerical calculation of 'actual' radial profile of ion temperature from 'measured' energy spectra of charge-exchanged neutrals

International Nuclear Information System (INIS)

Nakamura, Kazuo; Hiraki, Naoji; Toi, Kazuo; Itoh, Satoshi

1984-01-01

The energy spectra of charge-exchanged neutrals are observed in the TRIAM-1 tokamak by vertical scanning of the neutral energy analyzer. The ''apparent'' ion temperature obtained directly from the energy spectrum observed in the peripheral region is much higher than that predicted by neoclassical transport theory. The ''actual'' ion temperature profile is derived numerically from the energy spectra observed at various positions taking into account the wall-reflection effect of neutrals and the impermeability of the plasma. As a result, the ''actual'' ion temperature profile is found to agree well with that predicted by neoclassical transport theory. (author)

Questions of time and affect: a person's affectivity profile, time perspective, and well-being.

Science.gov (United States)

Garcia, Danilo; Sailer, Uta; Nima, Ali Al; Archer, Trevor

2016-01-01

Background. A "balanced" time perspective has been suggested to have a positive influence on well-being: a sentimental and positive view of the past (high Past Positive), a less pessimistic attitude toward the past (low Past Negative), the desire of experiencing pleasure with slight concern for future consequences (high Present Hedonistic), a less fatalistic and hopeless view of the future (low Present Fatalistic), and the ability to find reward in achieving specific long-term goals (high Future). We used the affective profiles model (i.e., combinations of individuals' experience of high/low positive/negative affectivity) to investigate differences between individuals in time perspective dimensions and to investigate if the influence of time perspective dimensions on well-being was moderated by the individual's type of profile. Method. Participants (N = 720) answered to the Positive Affect Negative Affect Schedule, the Zimbardo Time Perspective Inventory and two measures of well-being: the Temporal Satisfaction with Life Scale and Ryff's Scales of Psychological Well-Being-short version. A Multivariate Analysis of Variance (MANOVA) was conducted to identify differences in time perspective dimensions and well-being among individuals with distinct affective profiles. Four structural equation models (SEM) were used to investigate which time perspective dimensions predicted well-being for individuals in each profile. Results. Comparisons between individuals at the extreme of the affective profiles model suggested that individuals with a self-fulfilling profile (high positive/low negative affect) were characterized by a "balanced" time perspective and higher well-being compared to individuals with a self-destructive profile (low positive/high negative affect). However, a different pattern emerged when individuals who differed in one affect dimension but matched in the other were compared to each other. For instance, decreases in the past negative time perspective

Motivational profile of astronauts at the International Space Station

Science.gov (United States)

Brcic, Jelena

2010-11-01

Research has demonstrated that the motive triad of needs for achievement, power, and affiliation can predict variables such as occupational success and satisfaction, innovation, aggressiveness, susceptibility to illness, cooperation, conformity, and many others. The present study documents the motivational profiles of astronauts at three stages of their expedition. Thematic content analysis was employed for references to Winter's well-established motive markers in narratives (media interviews, journals, and oral histories) of 46 astronauts participating in International Space Station (ISS) expeditions. Significant pre-flight differences were found in relation to home agency and job status. NASA astronauts, compared with those from the Russian Space Agency, are motivated by higher need for power, as are commanders in comparison to flight engineers. The need for affiliation motive showed a significant change from pre-flight to in-flight stages. The implications of the relationship between the motivational profile of astronauts and the established behavioural correlates of such profiles are discussed.

Thermomechanical response of a cross-ply titanium matrix composite subjected to a generic hypersonic flight profile

International Nuclear Information System (INIS)

Mirdamadi, M.; Johnson, W.S.

1993-01-01

Cross-ply laminate behavior of Ti-15V-3Cr-3AI-3Sn (Ti-15-3) matrix reinforced with continuous silicon-carbide fibers (SCS-6) subjected to a generic hypersonic flight profile was evaluated experimentally and analytically. Thermomechanical fatigue test techniques were developed to conduct a simulation of a generic hypersonic flight profile. A micromechanical analysis was used. The analysis predicts the stress-strain response of the laminate and of the constituents in each ply during thermal and mechanical cycling by using only constituent properties as input. The fiber was modeled as elastic with transverse orthotropic and temperature-dependent properties. The matrix was modeled using a thermoviscoplastic constitutive relation. The fiber transverse modulus was reduced in the analysis to simulate the fiber-matrix interface failure. Excellent correlation was found between measured and predicted laminate stress-strain response due to generic hypersonic flight profile when fiber debonding was modeled

Generative Recurrent Networks for De Novo Drug Design.

Science.gov (United States)

Gupta, Anvita; Müller, Alex T; Huisman, Berend J H; Fuchs, Jens A; Schneider, Petra; Schneider, Gisbert

2018-01-01

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Cytokines in systemic lupus erythematosus: far beyond Th1/Th2 dualism lupus: cytokine profiles.

Science.gov (United States)

Guimarães, Poliana Macedo; Scavuzzi, Bruna Miglioranza; Stadtlober, Nicole Perugini; Franchi Santos, Lorena Flor da Rosa; Lozovoy, Marcell Alysson Batisti; Iriyoda, Tatiana Mayumi Veiga; Costa, Neide Tomimura; Reiche, Edna Maria Vissoci; Maes, Michael; Dichi, Isaias; Simão, Andréa Name Colado

2017-10-01

The aims of this study were to delineate cytokine profiles of systemic lupus erythematosus (SLE), construct prediction models for diagnosis and disease activity using those profiles, and to examine the associations between TNFB Ncol polymorphism, body mass index (BMI) and vitamin D levels with cytokine levels. Two hundred SLE patients and 196 healthy controls participated in this case-control study. Plasma cytokines levels of tumor necrosis factor (TNF)-α, interferon (IFN)-γ, interleukin (IL)-1β, IL- 4, IL-6, IL-10, IL-12 and IL-17 were measured and cytokines profiles were computed. IL-6, IL-12, IL-17, IFN-γ and IL-10 levels were significantly higher in SLE, while IL-4 was lower in SLE. The Th1/Th2 and Th1+Th17/Th2 profiles were significantly higher in SLE than in healthy controls, whereas there were no significant differences in the proinflammatory cytokine profile (TNFα+IL-6+IL-1β). In total, 90.4% of all subjects were correctly classified using Th1+Th17 profile and IL-10 (positively associated) and IL-4 (negatively associated) as predictor variables (sensitivity=66.7% and specificity=96.9%). In all, 20.9% of the variance in the SLE Disease Activity Index was predicted by the Th1+Th17/Th2 ratio, IL-10 and BMI (all positively) and proinflammatory profile (inversely associated). B1/B1 genotype is accompanied by increased IL-17 and Th17/Th2 ratio, while B1/B2 genotype is accompanied by higher IL-4 and IFNγ values. 25-OH vitamin D was inversely associated with IFN-γ levels. SLE is accompanied by Th1, Th17 and Treg profile and lowered IL-4 production. Lowered vitamin D levels and B1/B1 genotype, but not BMI, contribute to changes in cytokines profiles. Future treatments should target Th1, Th2 and Th17 profiles rather than inflammatory cytokines.

Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

International Nuclear Information System (INIS)

Nigsch, Florian; Mitchell, John B.O.

2008-01-01

The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

Evolution of Cross-Shore Profile Models for Sustainable Coastal Design

Science.gov (United States)

Ismail, Nabil; El-Sayed, Mohamed

2014-05-01

Selection and evaluation of coastal structures are correlated with environmental wave and current parameters as well as cross shore profiles. The coupling between the environmental conditions and cross shore profiles necessitates the ability to predict reasonably the cross shore profiles. Results obtained from the validation of a cross-shore profile evolution model, Uniform Beach Sediment Transport-Time-Averaged Cross-Shore (UNIBEST-TC), were examined and further analyzed to reveal the reasons for the discrepancy between the model predictions of the field data at the surf zone of the Duck Beach in North Carolina, USA. The UNIBEST model was developed to predict the main cross shore parameters of wave height, direction, cross shore and long shore currents. However, the results of the model predictions are generally satisfactory for wave height and direction but not satisfactory for the remaining parameters. This research is focused on exploring the discrepancy between the model predictions and the field data of the Duck site, and conducting further analyses to recommend model refinements. The discrepancy is partially attributed due to the fact that the measured values, were taken close to the seabed, while the predicted values are the depth-averaged velocity. Further examination indicated that UNIBEST-TC model runs consider the RMS of the wave height spectrum with a constant gamma-value from the offshore wave spectrum at 8.0m depth. To confirm this argument, a Wavelet Analysis was applied to the time series of wave height and longshore current velocity parameters at the Duck site. The significant wave height ranged between 0.6m and 4.0m while the frequencies ranged between 0.08 to 0.2Hz at 8.0m water depth. Four cases corresponding to events of both high water level and low water level at Duck site were considered in this study. The results show that linear and non-linear interaction between wave height and long-shore current occur over the range of frequencies

Control of plasma profile in microwave discharges via inverse-problem approach

Directory of Open Access Journals (Sweden)

Yasuyoshi Yasaka

2013-12-01

Full Text Available In the manufacturing process of semiconductors, plasma processing is an essential technology, and the plasma used in the process is required to be of high density, low temperature, large diameter, and high uniformity. This research focuses on the microwave-excited plasma that meets these needs, and the research target is a spatial profile control. Two novel techniques are introduced to control the uniformity; one is a segmented slot antenna that can change radial distribution of the radiated field during operation, and the other is a hyper simulator that can predict microwave power distribution necessary for a desired radial density profile. The control system including these techniques provides a method of controlling radial profiles of the microwave plasma via inverse-problem approach, and is investigated numerically and experimentally.

Model Predictive Control for Connected Hybrid Electric Vehicles

Directory of Open Access Journals (Sweden)

Kaijiang Yu

2015-01-01

Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles.

Directory of Open Access Journals (Sweden)

Sarah K Meadows

2010-07-01

Full Text Available In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI. However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79-100% compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16-43%, suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.

The prediction of ground movements caused by mining

International Nuclear Information System (INIS)

Karmis, M.; Haycocks, C.; Holland, C.T.

1992-01-01

This paper reviews the fundamental concepts involved in the development, application and validation of ground movement prediction methods developed by Virginia Polytechnic Institute and State University (VPI and SU) over the past 12 years. Prediction techniques have included empirical or semi-empirical methods, such as the profile function, influence function and zone area methods, as well as numerical methods, based on a finite element formulation which utilizes field subsidence data. The former techniques have been integrated in the Surface Deformation Prediction System (SDPS) software package for personal computers, which allows for the calculation of any component of ground movement in any direction. Comparisons between measured and predicted subsidence and strain values are presented for a selection of case studies, which demonstrate the applicability, accuracy and regional validity of these methods for predicting surface deformations due to underground mining

Sonographic biophysical profile in detection of foetal hypoxia in 100 cases of suspected high risk pregnancy

International Nuclear Information System (INIS)

Ullah, N.; Khan, A.R.; Usman, M.

2010-01-01

Background: The foetus has become increasingly accessible and visible as a patient over the last two decades. Ultrasound imaging has broadened the scope of foetal assessment. Dynamic real time B-Mode ultrasound is used to monitor cluster of biophysical variables, both dynamic and static collectively termed as biophysical profile. The purpose of this study was to determine the effect of sonographic biophysical profile score on perinatal outcome in terms of mortality and morbidity. Methods: This descriptive study was carried on 100 randomly select ed high risk pregnant patients in Radiology Department PGMI, Government Lady Reading Hospital, Peshawar from December 2007 to June 2008. Manning biophysical profile including non-stress was employed for foetal screening, using Toshiba ultrasound machine model Nemio SSA-550A and 7.5 MHZ probe. Results: Out of 100 cases 79 (79%) had a normal biophysical profile in the last scan of 10/10 and had a normal perinatal outcome with 5 minutes Apgar score >7/10. In 13 (13%) cases Apgar score at 5 minute was < 7/10 and babies were shifted to nursery. There were 2 (2%) false positive cases that showed abnormal biophysical profile scores of 6/10 but babies were born with an Apgar score of 8/10 at 5 minutes. There were 2 (2%) neonatal deaths in this study group. The sensitivity of biophysical profile was 79.1%, specificity 92.9%. Predictive value for a positive test was 98.55%; predictive value for a negative test was 41.93%. Conclusion: Biophysical profile is highly accurate and reliable test of diagnosing foetal hypoxia. (author)

Insights from triangulation of two purchase choice elicitation methods to predict social decision making in healthcare.

Science.gov (United States)

Whitty, Jennifer A; Rundle-Thiele, Sharyn R; Scuffham, Paul A

2012-03-01

Discrete choice experiments (DCEs) and the Juster scale are accepted methods for the prediction of individual purchase probabilities. Nevertheless, these methods have seldom been applied to a social decision-making context. To gain an overview of social decisions for a decision-making population through data triangulation, these two methods were used to understand purchase probability in a social decision-making context. We report an exploratory social decision-making study of pharmaceutical subsidy in Australia. A DCE and selected Juster scale profiles were presented to current and past members of the Australian Pharmaceutical Benefits Advisory Committee and its Economic Subcommittee. Across 66 observations derived from 11 respondents for 6 different pharmaceutical profiles, there was a small overall median difference of 0.024 in the predicted probability of public subsidy (p = 0.003), with the Juster scale predicting the higher likelihood. While consistency was observed at the extremes of the probability scale, the funding probability differed over the mid-range of profiles. There was larger variability in the DCE than Juster predictions within each individual respondent, suggesting the DCE is better able to discriminate between profiles. However, large variation was observed between individuals in the Juster scale but not DCE predictions. It is important to use multiple methods to obtain a complete picture of the probability of purchase or public subsidy in a social decision-making context until further research can elaborate on our findings. This exploratory analysis supports the suggestion that the mixed logit model, which was used for the DCE analysis, may fail to adequately account for preference heterogeneity in some contexts.

Determination of optimum fin profile for a zero-G capillary drained condenser

Science.gov (United States)

Mccormick, John A.; Valenzuela, Javier A.; Choudhury, Dipanker

1990-01-01

This paper presents the analytical formulation and numerical results for heat transfer in a high heat flux condenser that relies on capillary flow along shaped fins (Gregorig surfaces) and a drainage network embedded in the condenser walls. Results are shown for a variety of fin profile shapes in order to show the geometric trade-offs involved in seeking a maximum effective heat transfer coefficient for the fin. Predictions of the model show excellent agreement with previously reported measurements for steam. Based on this work, a profile has been selected for a 2 kW ammonia condenser currently under development for use in space. In that design the fin half width is 0.5 mm and the model predicts a heat transfer coefficient referred to the base of the fin of 9 W/sq cm deg C for a heat flux of 10/W sq cm at the base.

Use of ground-based wind profiles in mesoscale forecasting

Science.gov (United States)

Schlatter, Thomas W.

1985-01-01

A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

Science.gov (United States)

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

2015-02-01

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have

Real Time Hybrid Model Predictive Control for the Current Profile of the Tokamak à Configuration Variable (TCV

Directory of Open Access Journals (Sweden)

Izaskun Garrido

2016-08-01

Full Text Available Plasma stability is one of the obstacles in the path to the successful operation of fusion devices. Numerical control-oriented codes as it is the case of the widely accepted RZIp may be used within Tokamak simulations. The novelty of this article relies in the hierarchical development of a dynamic control loop. It is based on a current profile Model Predictive Control (MPC algorithm within a multiloop structure, where a MPC is developed at each step so as to improve the Proportional Integral Derivative (PID global scheme. The inner control loop is composed of a PID-based controller that acts over the Multiple Input Multiple Output (MIMO system resulting from the RZIp plasma model of the Tokamak à Configuration Variable (TCV. The coefficients of this PID controller are initially tuned using an eigenmode reduction over the passive structure model. The control action corresponding to the state of interest is then optimized in the outer MPC loop. For the sake of comparison, both the traditionally used PID global controller as well as the multiloop enhanced MPC are applied to the same TCV shot. The results show that the proposed control algorithm presents a superior performance over the conventional PID algorithm in terms of convergence. Furthermore, this enhanced MPC algorithm contributes to extend the discharge length and to overcome the limited power availability restrictions that hinder the performance of advanced tokamaks.

Molecular dynamics simulations of ion range profiles for heavy ions in light targets

Energy Technology Data Exchange (ETDEWEB)

Lan, C. [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996 (United States); State Key Laboratory of Nuclear Physics and Technology, Peking University, 100871 (China); Xue, J.M. [State Key Laboratory of Nuclear Physics and Technology, Peking University, 100871 (China); Zhang, Y., E-mail: Zhangy1@ornl.gov [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Morris, J.R. [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Zhu, Z. [Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Gao, Y.; Wang, Y.G.; Yan, S. [State Key Laboratory of Nuclear Physics and Technology, Peking University, 100871 (China); Weber, W.J. [Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)

2012-09-01

The determination of stopping powers for slow heavy ions in targets containing light elements is important to accurately describe ion-solid interactions, evaluate ion irradiation effects and predict ion ranges for device fabrication and nuclear applications. Recently, discrepancies of up to 40% between the experimental results and SRIM (Stopping and Range of Ions in Matter) predictions of ion ranges for heavy ions with medium and low energies (<{approx}25 keV/nucleon) in light elemental targets have been reported. The longer experimental ion ranges indicate that the stopping powers used in the SRIM code are overestimated. Here, a molecular dynamics simulation scheme is developed to calculate the ion ranges of heavy ions in light elemental targets. Electronic stopping powers generated from both a reciprocity approach and the SRIM code are used to investigate the influence of electronic stopping on ion range profiles. The ion range profiles for Au and Pb ions in SiC and Er ions in Si, with energies between 20 and 5250 keV, are simulated. The simulation results show that the depth profiles of implanted ions are deeper and in better agreement with the experiments when using the electronic stopping power values derived from the reciprocity approach. These results indicate that the origin of the discrepancy in ion ranges between experimental results and SRIM predictions in the low energy region may be an overestimation of the electronic stopping powers used in SRIM.

Evoked Emotions Predict Food Choice

OpenAIRE

Dalenberg, Jelle R.; Gutjar, Swetlana; ter Horst, Gert J.; de Graaf, Kees; Renken, Remco J.; Jager, Gerry

2014-01-01

In the current study we show that non-verbal food-evoked emotion scores significantly improve food choice prediction over merely liking scores. Previous research has shown that liking measures correlate with choice. However, liking is no strong predictor for food choice in real life environments. Therefore, the focus within recent studies shifted towards using emotion-profiling methods that successfully can discriminate between products that are equally liked. However, it is unclear how well ...

Analysis and Prediction of Electricity Consumption Using Smart Meter Data

OpenAIRE

Sauhats, A; Varfolomejeva, R; Linkevičs, O; Petričenko, R; Kuņickis, M; Balodis, M

2015-01-01

This paper is considering application of smart meter data to predict electricity consumption of household consumers. The availability and amount of data is suitable for in- depth statistical analysis of electricity consumption profiles and the study of consumer’s behavior. Prediction of electricity consumption is very important for electricity traders to balance their electricity purchase and sales portfolio, as well as to prepare optimal price products (offers) for their clients. Electricity...

Leaking privacy and shadow profiles in online social networks.

Science.gov (United States)

Garcia, David

2017-08-01

Social interaction and data integration in the digital society can affect the control that individuals have on their privacy. Social networking sites can access data from other services, including user contact lists where nonusers are listed too. Although most research on online privacy has focused on inference of personal information of users, this data integration poses the question of whether it is possible to predict personal information of nonusers. This article tests the shadow profile hypothesis, which postulates that the data given by the users of an online service predict personal information of nonusers. Using data from a disappeared social networking site, we perform a historical audit to evaluate whether personal data of nonusers could have been predicted with the personal data and contact lists shared by the users of the site. We analyze personal information of sexual orientation and relationship status, which follow regular mixing patterns in the social network. Going back in time over the growth of the network, we measure predictor performance as a function of network size and tendency of users to disclose their contact lists. This article presents robust evidence supporting the shadow profile hypothesis and reveals a multiplicative effect of network size and disclosure tendencies that accelerates the performance of predictors. These results call for new privacy paradigms that take into account the fact that individual privacy decisions do not happen in isolation and are mediated by the decisions of others.

Simulation of Cloud-aerosol Lidar with Orthogonal Polarization (CALIOP Attenuated Backscatter Profiles Using the Global Model of Aerosol Processes (GLOMAP

Directory of Open Access Journals (Sweden)

Young Stuart

2016-01-01

Full Text Available To permit the calculation of the radiative effects of atmospheric aerosols, we have linked our aerosol-chemical transport model (CTMGLOMAP to a new radiation module (UKCARADAER. In order to help assess and improve the accuracy of the radiation code, in particular the height dependence of the predicted scattering, we have developed a module that simulates attenuated backscatter (ABS profiles that would be measured by the satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP if it were to sample an atmosphere with the same aerosol loading as predicted by the CTM. Initial results of our comparisons of the predicted ABS profiles with actual CALIOP data are encouraging but some differences are noted, particularly in marine boundary layers where the scattering is currently under-predicted and in dust layers where it is often over-predicted. The sources of these differences are being investigated.

Modulating laser intensity profile ellipticity for microstructural control during metal additive manufacturing

International Nuclear Information System (INIS)

Roehling, Tien T.; Wu, Sheldon S.Q.; Khairallah, Saad A.; Roehling, John D.; Soezeri, S. Stefan; Crumb, Michael F.; Matthews, Manyalibo J.

2017-01-01

Additively manufactured (AM) metals are often highly textured, containing large columnar grains that initiate epitaxially under steep temperature gradients and rapid solidification conditions. These unique microstructures partially account for the massive property disparity existing between AM and conventionally processed alloys. Although equiaxed grains are desirable for isotropic mechanical behavior, the columnar-to-equiaxed transition remains difficult to predict for conventional solidification processes, and much more so for AM. In this study, the effects of laser intensity profile ellipticity on melt track macrostructures and microstructures were studied in 316L stainless steel. Experimental results were supported by temperature gradients and melt velocities simulated using the ALE3D multi-physics code. As a general trend, columnar grains preferentially formed with increasing laser power and scan speed for all beam profiles. However, when conduction mode laser heating occurs, scan parameters that result in coarse columnar microstructures using Gaussian profiles produce equiaxed or mixed equiaxed-columnar microstructures using elliptical profiles. By modulating spatial laser intensity profiles on the fly, site-specific microstructures and properties can be directly engineered into additively manufactured parts.

CPHmodels-3.0--remote homology modeling using structure-guided sequence profiles.

Science.gov (United States)

Nielsen, Morten; Lundegaard, Claus; Lund, Ole; Petersen, Thomas Nordahl

2010-07-01

CPHmodels-3.0 is a web server predicting protein 3D structure by use of single template homology modeling. The server employs a hybrid of the scoring functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A query sequence is first attempted modeled using the fast CPHmodels-2.0 profile-profile scoring function suitable for close homology modeling. The new computational costly remote homology-modeling algorithm is only engaged provided that no suitable PDB template is identified in the initial search. CPHmodels-3.0 was benchmarked in the CASP8 competition and produced models for 94% of the targets (117 out of 128), 74% were predicted as high reliability models (87 out of 117). These achieved an average RMSD of 4.6 A when superimposed to the 3D structure. The remaining 26% low reliably models (30 out of 117) could superimpose to the true 3D structure with an average RMSD of 9.3 A. These performance values place the CPHmodels-3.0 method in the group of high performing 3D prediction tools. Beside its accuracy, one of the important features of the method is its speed. For most queries, the response time of the server is web server is available at http://www.cbs.dtu.dk/services/CPHmodels/.

Important non-parental adults and positive youth development across mid- to late-adolescence: the moderating effect of parenting profiles.

Science.gov (United States)

Bowers, Edmond P; Johnson, Sara K; Buckingham, Mary H; Gasca, Santiago; Warren, Daniel J A; Lerner, Jacqueline V; Lerner, Richard M

2014-06-01

Both parents and important non-parental adults have influential roles in promoting positive youth development (PYD). Little research, however, has examined the simultaneous effects of both parents and important non-parental adults for PYD. We assessed the relationships among youth-reported parenting profiles and important non-parental adult relationships in predicting the Five Cs of PYD (competence, confidence, connection, character, and caring) in four cross-sectional waves of data from the 4-H Study of PYD (Grade 9: N = 975, 61.1% female; Grade 10: N = 1,855, 63.4% female; Grade 11: N = 983, 67.9% female; Grade 12: N = 703, 69.3% female). The results indicated the existence of latent profiles of youth-reported parenting styles based on maternal warmth, parental school involvement, and parental monitoring that were consistent with previously identified profiles (authoritative, authoritarian, permissive, and uninvolved) as well as reflecting several novel profiles (highly involved, integrative, school-focused, controlling). Parenting profile membership predicted mean differences in the Five Cs at each wave, and also moderated the relationships between the presence of an important non-parental adult and the Five Cs. In general, authoritative and highly involved parenting predicted higher levels of PYD and a higher likelihood of being connected to an important non-parental adult. We discuss the implications of these findings for future research on adult influences of youth development and for programs that involve adults in attempts to promote PYD.

Nonparametric estimates of drift and diffusion profiles via Fokker-Planck algebra.

Science.gov (United States)

Lund, Steven P; Hubbard, Joseph B; Halter, Michael

2014-11-06

Diffusion processes superimposed upon deterministic motion play a key role in understanding and controlling the transport of matter, energy, momentum, and even information in physics, chemistry, material science, biology, and communications technology. Given functions defining these random and deterministic components, the Fokker-Planck (FP) equation is often used to model these diffusive systems. Many methods exist for estimating the drift and diffusion profiles from one or more identifiable diffusive trajectories; however, when many identical entities diffuse simultaneously, it may not be possible to identify individual trajectories. Here we present a method capable of simultaneously providing nonparametric estimates for both drift and diffusion profiles from evolving density profiles, requiring only the validity of Langevin/FP dynamics. This algebraic FP manipulation provides a flexible and robust framework for estimating stationary drift and diffusion coefficient profiles, is not based on fluctuation theory or solved diffusion equations, and may facilitate predictions for many experimental systems. We illustrate this approach on experimental data obtained from a model lipid bilayer system exhibiting free diffusion and electric field induced drift. The wide range over which this approach provides accurate estimates for drift and diffusion profiles is demonstrated through simulation.

Estimating cumulative soil accumulation rates with in situ-produced cosmogenic nuclide depth profiles

International Nuclear Information System (INIS)

Phillips, William M.

2000-01-01

A numerical model relating spatially averaged rates of cumulative soil accumulation and hillslope erosion to cosmogenic nuclide distribution in depth profiles is presented. Model predictions are compared with cosmogenic 21 Ne and AMS radiocarbon data from soils of the Pajarito Plateau, New Mexico. Rates of soil accumulation and hillslope erosion estimated by cosmogenic 21 Ne are significantly lower than rates indicated by radiocarbon and regional soil-geomorphic studies. The low apparent cosmogenic erosion rates are artifacts of high nuclide inheritance in cumulative soil parent material produced from erosion of old soils on hillslopes. In addition, 21 Ne profiles produced under conditions of rapid accumulation (>0.1 cm/a) are difficult to distinguish from bioturbated soil profiles. Modeling indicates that while 10 Be profiles will share this problem, both bioturbation and anomalous inheritance can be identified with measurement of in situ-produced 14 C

Microarray-based cancer prediction using soft computing approach.

Science.gov (United States)

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

The Different Facets of Work Stress: A Latent Profile Analysis of Nurses' Work Demands.

Science.gov (United States)

Jenull, Brigitte B; Wiedermann, Wolfgang

2015-10-01

Work-related stress has been identified as a relevant problem leading to negative effects on health and quality of life. Using data from 844 nurses, latent profile analyses (LPA) were applied to identify distinct patterns of work stress. Several sociodemographic variables, including nurses' working and living conditions, as well as nurses' reactions to workload, were considered to predict respondents' profile membership. LPA revealed three distinct profiles that can be distinguished by a low, moderate, and higher stress level. Being financially secure is positively related to the low stress profile, whereas working in an urban area and having low job satisfaction increases the chance of belonging to the higher stress profile. Our results can be used as a basis to develop interventions to create a healthy nursing home environment by supporting the balance between family and work, providing access to job resources and optimizing recovery opportunities. © The Author(s) 2013.

Hydrogen Bond Basicity Prediction for Medicinal Chemistry Design.

Science.gov (United States)

Kenny, Peter W; Montanari, Carlos A; Prokopczyk, Igor M; Ribeiro, Jean F R; Sartori, Geraldo Rodrigues

2016-05-12

Hydrogen bonding is discussed in the context of medicinal chemistry design. Minimized molecular electrostatic potential (Vmin) is shown to be an effective predictor of hydrogen bond basicity (pKBHX), and predictive models are presented for a number of hydrogen bond acceptor types relevant to medicinal chemistry. The problems posed by the presence of nonequivalent hydrogen bond acceptor sites in molecular structures are addressed by using nonlinear regression to fit measured pKBHX to calculated Vmin. Predictions are made for hydrogen bond basicity of fluorine in situations where relevant experimental measurements are not available. It is shown how predicted pKBHX can be used to provide insight into the nature of bioisosterism and to profile heterocycles. Examples of pKBHX prediction for molecular structures with multiple, nonequivalent hydrogen bond acceptors are presented.

Design and implementation of the ITPA confinement profile database

Energy Technology Data Exchange (ETDEWEB)

Walters, Malcolm E-mail: malcolm.walters@ukaea.org.uk; Roach, Colin

2004-06-01

One key goal of the fusion program is to improve the accuracy of physics models in describing existing experiments, so as to make better predictions of the performance of future fusion devices. To support this goal, databases of experimental results from multiple machines have been assembled to facilitate the testing of physics models over a wide range of operating conditions and plasma parameters. One such database was the International Multi-Tokamak Profile Database. This database has more recently been substantially revamped to exploit newer technologies, and is now known as the ITPA confinement profile database http://www.tokamak-profiledb.ukaea.org.uk. The overall design of the updated system will be outlined and the implementation of the relational database part will be described in detail.

Effect of prenatal mindfulness training on depressive symptom severity through 18-months postpartum: A latent profile analysis.

Science.gov (United States)

Felder, Jennifer N; Roubinov, Danielle; Bush, Nicole R; Coleman-Phox, Kimberly; Vieten, Cassandra; Laraia, Barbara; Adler, Nancy E; Epel, Elissa

2018-02-28

We examined whether prenatal mindfulness training was associated with lower depressive symptoms through 18-months postpartum compared to treatment as usual (TAU). A controlled, quasi-experimental trial compared prenatal mindfulness training (MMT) to TAU. We collected depressive symptom data at post-intervention, 6-, and 18-months postpartum. Latent profile analysis identified depressive symptom profiles, and multinomial logistic regression examined whether treatment condition predicted profile. Three depressive symptom severity profiles emerged: none/minimal, mild, and moderate. Adjusting for relevant covariates, MMT participants were less likely than TAU participants to be in the moderate profile than the none/minimal profile (OR = 0.13, 95% CI = 0.03-0.54, p = .005). Prenatal mindfulness training may have benefits for depressive symptoms during the transition to parenthood. © 2018 Wiley Periodicals, Inc.

Questions of time and affect: a person’s affectivity profile, time perspective, and well-being

Science.gov (United States)

Sailer, Uta; Nima, Ali Al; Archer, Trevor

2016-01-01

Background. A “balanced” time perspective has been suggested to have a positive influence on well-being: a sentimental and positive view of the past (high Past Positive), a less pessimistic attitude toward the past (low Past Negative), the desire of experiencing pleasure with slight concern for future consequences (high Present Hedonistic), a less fatalistic and hopeless view of the future (low Present Fatalistic), and the ability to find reward in achieving specific long-term goals (high Future). We used the affective profiles model (i.e., combinations of individuals’ experience of high/low positive/negative affectivity) to investigate differences between individuals in time perspective dimensions and to investigate if the influence of time perspective dimensions on well-being was moderated by the individual’s type of profile. Method. Participants (N = 720) answered to the Positive Affect Negative Affect Schedule, the Zimbardo Time Perspective Inventory and two measures of well-being: the Temporal Satisfaction with Life Scale and Ryff’s Scales of Psychological Well-Being-short version. A Multivariate Analysis of Variance (MANOVA) was conducted to identify differences in time perspective dimensions and well-being among individuals with distinct affective profiles. Four structural equation models (SEM) were used to investigate which time perspective dimensions predicted well-being for individuals in each profile. Results. Comparisons between individuals at the extreme of the affective profiles model suggested that individuals with a self-fulfilling profile (high positive/low negative affect) were characterized by a “balanced” time perspective and higher well-being compared to individuals with a self-destructive profile (low positive/high negative affect). However, a different pattern emerged when individuals who differed in one affect dimension but matched in the other were compared to each other. For instance, decreases in the past negative time

Questions of time and affect: a person’s affectivity profile, time perspective, and well-being

Directory of Open Access Journals (Sweden)

Danilo Garcia

2016-03-01

Full Text Available Background. A “balanced” time perspective has been suggested to have a positive influence on well-being: a sentimental and positive view of the past (high Past Positive, a less pessimistic attitude toward the past (low Past Negative, the desire of experiencing pleasure with slight concern for future consequences (high Present Hedonistic, a less fatalistic and hopeless view of the future (low Present Fatalistic, and the ability to find reward in achieving specific long-term goals (high Future. We used the affective profiles model (i.e., combinations of individuals’ experience of high/low positive/negative affectivity to investigate differences between individuals in time perspective dimensions and to investigate if the influence of time perspective dimensions on well-being was moderated by the individual’s type of profile. Method. Participants (N = 720 answered to the Positive Affect Negative Affect Schedule, the Zimbardo Time Perspective Inventory and two measures of well-being: the Temporal Satisfaction with Life Scale and Ryff’s Scales of Psychological Well-Being-short version. A Multivariate Analysis of Variance (MANOVA was conducted to identify differences in time perspective dimensions and well-being among individuals with distinct affective profiles. Four structural equation models (SEM were used to investigate which time perspective dimensions predicted well-being for individuals in each profile. Results. Comparisons between individuals at the extreme of the affective profiles model suggested that individuals with a self-fulfilling profile (high positive/low negative affect were characterized by a “balanced” time perspective and higher well-being compared to individuals with a self-destructive profile (low positive/high negative affect. However, a different pattern emerged when individuals who differed in one affect dimension but matched in the other were compared to each other. For instance, decreases in the past negative

Human motion simulation predictive dynamics

CERN Document Server

Abdel-Malek, Karim

2013-01-01

Simulate realistic human motion in a virtual world with an optimization-based approach to motion prediction. With this approach, motion is governed by human performance measures, such as speed and energy, which act as objective functions to be optimized. Constraints on joint torques and angles are imposed quite easily. Predicting motion in this way allows one to use avatars to study how and why humans move the way they do, given specific scenarios. It also enables avatars to react to infinitely many scenarios with substantial autonomy. With this approach it is possible to predict dynamic motion without having to integrate equations of motion -- rather than solving equations of motion, this approach solves for a continuous time-dependent curve characterizing joint variables (also called joint profiles) for every degree of freedom. Introduces rigorous mathematical methods for digital human modelling and simulation Focuses on understanding and representing spatial relationships (3D) of biomechanics Develops an i...

Patent Analysis for Supporting Merger and Acquisition (M&A) Prediction: A Data Mining Approach

Science.gov (United States)

Wei, Chih-Ping; Jiang, Yu-Syun; Yang, Chin-Sheng

M&A plays an increasingly important role in the contemporary business environment. Companies usually conduct M&A to pursue complementarity from other companies for preserving and/or extending their competitive advantages. For the given bidder company, a critical first step to the success of M&A activities is the appropriate selection of target companies. However, existing studies on M&A prediction incur several limitations, such as the exclusion of technological variables in M&A prediction models and the omission of the profile of the respective bidder company and its compatibility with candidate target companies. In response to these limitations, we propose an M&A prediction technique which not only encompasses technological variables derived from patent analysis as prediction indictors but also takes into account the profiles of both bidder and candidate target companies when building an M&A prediction model. We collect a set of real-world M&A cases to evaluate the proposed technique. The evaluation results are encouraging and will serve as a basis for future studies.

Fatty acid methyl ester profiles of bat wing surface lipids.

Science.gov (United States)

Pannkuk, Evan L; Fuller, Nathan W; Moore, Patrick R; Gilmore, David F; Savary, Brett J; Risch, Thomas S

2014-11-01

Sebocytes are specialized epithelial cells that rupture to secrete sebaceous lipids (sebum) across the mammalian integument. Sebum protects the integument from UV radiation, and maintains host microbial communities among other functions. Native glandular sebum is composed primarily of triacylglycerides (TAG) and wax esters (WE). Upon secretion (mature sebum), these lipids combine with minor cellular membrane components comprising total surface lipids. TAG and WE are further cleaved to smaller molecules through oxidation or host enzymatic digestion, resulting in a complex mixture of glycerolipids (e.g., TAG), sterols, unesterified fatty acids (FFA), WE, cholesteryl esters, and squalene comprising surface lipid. We are interested if fatty acid methyl ester (FAME) profiling of bat surface lipid could predict species specificity to the cutaneous fungal disease, white nose syndrome (WNS). We collected sebaceous secretions from 13 bat spp. using Sebutape(®) and converted them to FAME with an acid catalyzed transesterification. We found that Sebutape(®) adhesive patches removed ~6× more total lipid than Sebutape(®) indicator strips. Juvenile eastern red bats (Lasiurus borealis) had significantly higher 18:1 than adults, but 14:0, 16:1, and 20:0 were higher in adults. FAME profiles among several bat species were similar. We concluded that bat surface lipid FAME profiling does not provide a robust model predicting species susceptibility to WNS. However, these results provide baseline data that can be used for lipid roles in future ecological studies, such as life history, diet, or migration.

Global mapping of vertical injection profiles of wild-fire emission

Science.gov (United States)

Sofiev, M.; Vankevich, R.; Ermakova, T.; Hakkarainen, J.

2012-08-01

A problem of a characteristic vertical profile of smoke released from wild-land fires is considered. A methodology for bottom-up evaluation of this profile is suggested and a corresponding global dataset is calculated. The profile estimation is based on: (i) a semi-empirical formula for plume-top height recently suggested by the authors, (ii) MODIS satellite observations of active wild-land fires, and (iii) meteorological conditions evaluated at each fireplace using output of ECMWF weather prediction model. Plumes from all fires recorded globally during two arbitrarily picked years 2001 and 2008 are evaluated and their smoke injection profiles are estimated with a time step of 3 h. The resulting 4-dimensional dataset is split to day- and night-time subsets. Each of the subsets is projected to global grid with resolution 1° × 1° × 500 m, averaged to monthly level, and normalised with total emission. Evaluation of the obtained dataset was performed at several levels. Firstly, the quality of the semi-empirical formula for plume-top computations was evaluated using recent additions to the MISR fire plume-height dataset. Secondly, the obtained maps of injection profiles are compared with another global distribution available from literature. Thirdly, the upper percentiles of the profiles are compared with an independent dataset of space-based lidar CALIOP. Finally, the stability of the calculated profiles with regard to inter-annual variations of the fire activity and meteorological conditions is roughly estimated by comparing the sub-sets for 2001 and 2008.

Profiling Occupant Behaviour in Danish Dwellings using Time Use Survey Data - Part II: Time-related Factors and Occupancy

DEFF Research Database (Denmark)

Barthelmes, V.M.; Li, R.; Andersen, R.K.

2018-01-01

Occupant behaviour has been shown to be one of the key driving factors of uncertainty in prediction of energy consumption in buildings. Building occupants affect building energy use directly and indirectly by interacting with building energy systems such as adjusting temperature set...... occupant profiles for prediction of energy use to reduce the gap between predicted and real building energy consumptions. In this study, we exploit diary-based Danish Time Use Surveys for understanding and modelling occupant behaviour in the residential sector in Denmark. This paper is a continuation......-points, switching lights on/off, using electrical devices and opening/closing windows. Furthermore, building inhabitants’ daily activity profiles clearly shape the timing of energy demand in households. Modelling energy-related human activities throughout the day, therefore, is crucial to defining more realistic...

He, U, and Th Depth Profiling of Apatite and Zircon Using Laser Ablation Noble Gas Mass Spectrometry and SIMS

Science.gov (United States)

Monteleone, B. D.; van Soest, M. C.; Hodges, K. V.; Hervig, R.; Boyce, J. W.

2008-12-01

Conventional (U-Th)/He thermochronology utilizes single or multiple grain analyses of U- and Th-bearing minerals such as apatite and zircon and does not allow for assessment of spatial variation in concentration of He, U, or Th within individual crystals. As such, age calculation and interpretation require assumptions regarding 4He loss through alpha ejection, diffusive redistribution of 4He, and U and Th distribution as an initial condition for these processes. Although models have been developed to predict 4He diffusion parameters, correct for the effect of alpha ejection on calculated cooling ages, and account for the effect of U and Th zonation within apatite and zircon, measurements of 4He, U, and Th distribution have not been combined within a single crystal. We apply ArF excimer laser ablation, combined with noble gas mass spectrometry, to obtain depth profiles within apatite and zircon crystals in order to assess variations in 4He concentration with depth. Our initial results from pre-cut, pre-heated slabs of Durango apatite, each subjected to different T-t schedules, suggest a general agreement of 4He profiles with those predicted by theoretical diffusion models (Farley, 2000). Depth profiles through unpolished grains give reproducible alpha ejection profiles in Durango apatite that deviate from alpha ejection profiles predicted for ideal, homogenous crystals. SIMS depth profiling utilizes an O2 primary beam capable of sputtering tens of microns and measuring sub-micron resolution variation in [U], [Th], and [Sm]. Preliminary results suggest that sufficient [U] and [Th] zonation is present in Durango apatite to influence the form of the 4He alpha ejection profile. Future work will assess the influence of measured [U] and [Th] zonation on previously measured 4He depth profiles. Farley, K.A., 2000. Helium diffusion from apatite; general behavior as illustrated by Durango fluorapatite. J. Geophys. Res., B Solid Earth Planets 105 (2), 2903-2914.

Femtosecond laser effect on the self-sealing properties of the corneal incision of various lengths and profile (experimental trial

Directory of Open Access Journals (Sweden)

Yulduz Shavkatovna Nizametdinova

2015-06-01

Full Text Available An experimental investigation was carried out to study self-sealing properties of corneal incisions of different profile and length carried out with femtosecond laser Victus (Technolas Perfect Vision/Bausch&Lomb. Using femtosecond laser for this purpose allows creating corneal incisions of high precision and predictability. Reproducibility and standardization of the incision profile and length are an advantage of this technology. Obtained results showed that single-profile incisions are less stable and safe when compared to multi-profile ones. It was noted that incision length increase promotes its self-sealing properties.

Optimization and control of plasma shape and current profile in non-circular cross-section tokamaks

International Nuclear Information System (INIS)

Moore, R.W.; Bernard, L.C.; Chan, V.S.

1981-01-01

Tokamaks with elongated, non-circular cross-sections are under consideration as fusion reactors because they have the potential for stable operation at high β. Ideal MHD theory, however, predicts that careful current profile control will be required to achieve the potential high-β advantages of non-circular cross-sections. In this paper, high-β equilibria which are stable to all ideal MHD modes are found by optimizing the plasma shape and current profile for doublets, up-down asymmetric dees, and symmetric dees. The ideal MHD stability of these equilibria for low toroidal mode number n is analysed with a global MHD stability code, GATO. The stability to high-n modes is analysed with a localized ballooning code, BLOON. The attainment of high β is facilitated by an automated optimization search on shape and current parameters. The equilibria are calculated with a free-boundary equilibrium code using coils appropriate for the Doublet III experimental device. The optimal equilibria are characterized by broad current profiles with values of βsub(poloidal) approximately equal to 1. Experimental realization of the shapes and current profiles giving the highest β limits is explored with a 1 1/2-D transport code, which simulates the time evolution of the 2-D MHD equilibrium while calculating consistent current profiles from a 1-D transport model. Transport simulations indicate that nearly optimal shapes may be obtained provided that the currents in the field-shaping coils are appropriately programmed and the plasma current profile is sufficiently broad. Obtaining broad current profiles is possible by current ramping, neutral-beam heating, and electron-cyclotron heating. With combinations of these techniques it is possible to approach the optimum β predicted by the MHD theory. (author)

Using the load-velocity relationship for 1RM prediction.

OpenAIRE

Jidovtseff, Boris; Harris, N. K.; Crielaard, Jean-Michel; Cronin, J. B.

2011-01-01

Jidovtseff, B, Harris, NK, Crielaard, J-M, and Cronin, JB. Using the load-velocity relationship for 1RM prediction. J Strength Cond Res 24(x): 000-000, 2009-The purpose of this study was to investigate the ability of the load-velocity relationship to accurately predict a bench press 1 repetition maximum (1RM). Data from 3 different bench press studies (n = 112) that incorporated both 1RM assessment and submaximal load-velocity profiling were analyzed. Individual regression analysis was perfor...

Predicting unsaturated zone nitrogen mass balances in agricultural settings of the United States

Science.gov (United States)

Nolan, Bernard T.; Puckett, Larry J.; Ma, Liwang; Green, Christopher T.; Bayless, E. Randall; Malone, Robert W.

2009-01-01

Unsaturated zone N fate and transport were evaluated at four sites to identify the predominant pathways of N cycling: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard and cornfield (Zea mays L.) in the lower Merced River study basin, California; and corn–soybean [Glycine max (L.) Merr.] rotations in study basins at Maple Creek, Nebraska, and at Morgan Creek, Maryland. We used inverse modeling with a new version of the Root Zone Water Quality Model (RZWQM2) to estimate soil hydraulic and nitrogen transformation parameters throughout the unsaturated zone; previous versions were limited to 3-m depth and relied on manual calibration. The overall goal of the modeling was to derive unsaturated zone N mass balances for the four sites. RZWQM2 showed promise for deeper simulation profiles. Relative root mean square error (RRMSE) values for predicted and observed nitrate concentrations in lysimeters were 0.40 and 0.52 for California (6.5 m depth) and Nebraska (10 m), respectively, and index of agreement (d) values were 0.60 and 0.71 (d varies between 0 and 1, with higher values indicating better agreement). For the shallow simulation profile (1 m) in Maryland, RRMSE and d for nitrate were 0.22 and 0.86, respectively. Except for Nebraska, predictions of average nitrate concentration at the bottom of the simulation profile agreed reasonably well with measured concentrations in monitoring wells. The largest additions of N were predicted to come from inorganic fertilizer (153–195 kg N ha−1 yr−1 in California) and N fixation (99 and 131 kg N ha−1 yr−1 in Maryland and Nebraska, respectively). Predicted N losses occurred primarily through plant uptake (144–237 kg N ha−1 yr−1) and deep seepage out of the profile (56–102 kg N ha−1 yr−1). Large reservoirs of organic N (up to 17,500 kg N ha−1 m−1 at Nebraska) were predicted to reside in the unsaturated zone, which has implications for potential future transfer of nitrate to groundwater.

Assessing the Stability and Robustness of Semantic Web Services Recommendation Algorithms Under Profile Injection Attacks

Directory of Open Access Journals (Sweden)

GRANDIN, P. H.

2014-06-01

Full Text Available Recommendation systems based on collaborative filtering are open by nature, what makes them vulnerable to profile injection attacks that insert biased evaluations in the system database in order to manipulate recommendations. In this paper we evaluate the stability and robustness of collaborative filtering algorithms applied to semantic web services recommendation when submitted to random and segment profile injection attacks. We evaluated four algorithms: (1 IMEAN, that makes predictions using the average of the evaluations received by the target item; (2 UMEAN, that makes predictions using the average of the evaluation made by the target user; (3 an algorithm based on the k-nearest neighbor (k-NN method and (4, an algorithm based on the k-means clustering method.The experiments showed that the UMEAN algorithm is not affected by the attacks and that IMEAN is the most vulnerable of all algorithms tested. Nevertheless, both UMEAN and IMEAN have little practical application due to the low precision of their predictions. Among the algorithms with intermediate tolerance to attacks but with good prediction performance, the algorithm based on k-nn proved to be more robust and stable than the algorithm based on k-means.

The relevance of light diffusion profiles for interstitial PDT using light-diffusing optical fibers

Science.gov (United States)

Stringasci, Mirian D.; Fortunato, Thereza C.; Moriyama, Lilian T.; Vollet Filho, José Dirceu; Bagnato, Vanderlei S.; Kurachi, Cristina

2017-02-01

Photodynamic therapy (PDT) is a technique used for several tumor types treatment. Light penetration on biological tissue is one limiting factor for PDT applied to large tumors. An alternative is using interstitial PDT, in which optical fibers are inserted into tumors. Cylindrical diffusers have been used in interstitial PDT. Light emission of different diffusers depends on the manufacturing process, size and optical properties of fibers, which make difficult to establish an adequate light dosimetry, since usually light profile is not designed for direct tissue-fiber contact. This study discusses the relevance of light distribution by a cylindrical diffuser into a turbid lipid emulsion solution, and how parts of a single diffuser contribute to illumination. A 2 cm-long cylindrical diffuser optical fiber was connected to a diode laser (630 nm), and the light spatial distribution was measured by scanning the solution with a collection probe. From the light field profile generated by a 1 mm-long intermediary element of a 20 mm-long cylindrical diffuser, recovery of light distribution for the entire diffuser was obtained. PDT was performed in rat healthy liver for a real treatment outcome analysis. By using computational tools, a typical necrosis profile generated by the irradiation with such a diffuser fiber was reconstructed. The results showed that it was possible predicting theoretically the shape of a necrosis profile in a healthy, homogeneous tissue with reasonable accuracy. The ability to predict the necrosis profile obtained from an interstitial illumination by optical diffusers has the potential improve light dosimetry for interstitial PDT.

Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia.

Science.gov (United States)

Kelly, Rachel S; Croteau-Chonka, Damien C; Dahlin, Amber; Mirzakhani, Hooman; Wu, Ann C; Wan, Emily S; McGeachie, Michael J; Qiu, Weiliang; Sordillo, Joanne E; Al-Garawi, Amal; Gray, Kathryn J; McElrath, Thomas F; Carey, Vincent J; Clish, Clary B; Litonjua, Augusto A; Weiss, Scott T; Lasky-Su, Jessica A

2017-01-01

Preeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive. This study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms. Metabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods. In total, 72 (0.9%) metabolite features were associated (pIntegration with the transcriptomic signature refined these results suggesting a particular role for lipid imbalance, immune function and the circulatory system. These findings suggest it is possible to develop a predictive metabolomic profile of preeclampsia. This profile is characterized by changes in lipid and amino acid metabolism and dysregulation of immune response and can be refined through interaction with transcriptomic data. However validation in larger and more diverse populations is required.

Data Profiling

OpenAIRE

Hladíková, Radka

2010-01-01

Title: Data Profiling Author: Radka Hladíková Department: Department of Software Engineering Supervisor: Ing. Vladimír Kyjonka Supervisor's e-mail address: Abstract: This thesis puts mind on problems with data quality and data profiling. This Work analyses and summarizes problems of data quality, data defects, process of data quality, data quality assessment and data profiling. The main topic is data profiling as a process of researching data available in existing...

Planck intermediate results: V. Pressure profiles of galaxy clusters from the Sunyaev-Zeldovich effect

DEFF Research Database (Denmark)

Bartlett, J.G.; Cardoso, J.-F.; Castex, G.

2013-01-01

that most clusters are individually detected at least out to R500. By stacking the radial profiles, we have statistically detected the radial SZ signal out to 3 × R500, i.e., at a density contrast of about 50-100, though the dispersion about the mean profile dominates the statistical errors across the whole......Taking advantage of the all-sky coverage and broadfrequency range of the Planck satellite, we study the Sunyaev-Zeldovich (SZ) and pressure profiles of 62 nearby massive clusters detected at high significance in the 14-month nominal survey. Careful reconstruction of the SZ signal indicates...... flatter than most predictions from numerical simulations. Combining the SZ and X-ray observed profiles into a joint fit to a generalised pressure profile gives best-fit parameters [P0,c500,γ, α,β] = [6.41,1.81,0.31,1.33,4.13]. Using a reasonable hypothesis for the gas temperature in the cluster outskirts...

Harnessing atomistic simulations to predict the rate at which dislocations overcome obstacles

Science.gov (United States)

Saroukhani, S.; Nguyen, L. D.; Leung, K. W. K.; Singh, C. V.; Warner, D. H.

2016-05-01

Predicting the rate at which dislocations overcome obstacles is key to understanding the microscopic features that govern the plastic flow of modern alloys. In this spirit, the current manuscript examines the rate at which an edge dislocation overcomes an obstacle in aluminum. Predictions were made using different popular variants of Harmonic Transition State Theory (HTST) and compared to those of direct Molecular Dynamics (MD) simulations. The HTST predictions were found to be grossly inaccurate due to the large entropy barrier associated with the dislocation-obstacle interaction. Considering the importance of finite temperature effects, the utility of the Finite Temperature String (FTS) method was then explored. While this approach was found capable of identifying a prominent reaction tube, it was not capable of computing the free energy profile along the tube. Lastly, the utility of the Transition Interface Sampling (TIS) approach was explored, which does not need a free energy profile and is known to be less reliant on the choice of reaction coordinate. The TIS approach was found capable of accurately predicting the rate, relative to direct MD simulations. This finding was utilized to examine the temperature and load dependence of the dislocation-obstacle interaction in a simple periodic cell configuration. An attractive rate prediction approach combining TST and simple continuum models is identified, and the strain rate sensitivity of individual dislocation obstacle interactions is predicted.

Refractive outcomes of an advanced aspherically optimized profile for myopia corrections by LASIK: a retrospective comparison with the standard aspherically optimized profile

Directory of Open Access Journals (Sweden)

Meyer B

2015-02-01

Full Text Available Bertram Meyer,1 Georg Sluyterman van Langeweyde,2 Matthias Wottke2 1Augencentrum Köln, Cologne, Germany; 2Carl Zeiss Meditec AG, Jena, Germany Purpose: A retrospective comparison of refractive outcomes of a new, aspherically optimized profile with an enhanced energy correction feature (Triple-A and the conventionally used aspherically optimized profile (ASA, or aberration smart ablation for correction of low-to-high myopia.Setting: Augen-OP-Centrum, Cologne, GermanyDesign: Retrospective nonrandomized comparative studyMethods: A central database at the Augen-OP-Centrum was used to gather retrospective data for low-to-high myopia (up to -10 D. One hundred and seven eyes (56 patients were treated with the ASA profile, and 79 eyes (46 patients were treated with the Triple-A profile. Postoperative outcomes were evaluated at 1 month, 3 months, 6 months, and 1 year follow-up time points.Results: The Triple-A profile showed better predictability indicated by a significantly lower standard deviation of residuals (0.32–0.34 vs 0.36–0.44, Triple-A vs ASA in the 6-month to 1-year period. The Triple-A group had better stability across all time intervals and achieved better postoperative astigmatism improvements with significantly lower scatter. This group achieved better safety at 1 year, with 100% of eyes showing no change or gain in Snellen lines, compared with 97% in the ASA group. A better safety index was observed for the Triple-A group at later time points. The Triple-A group had a better efficacy index and a higher percentage of eyes with an uncorrected Snellen visual acuity of 20/20 or greater at all investigated follow-up time points.Conclusion: The new aspherically optimized Triple-A profile can safely and effectively correct low-to-high myopia. It has demonstrated superiority over the ASA profile in most refractive outcomes. Keywords: Triple-A, wavefront measurements, corneal aberrations, corneal asphericity, ablation profile

Prognostic Gene Expression Profiles in Breast Cancer

DEFF Research Database (Denmark)

Sørensen, Kristina Pilekær

Each year approximately 4,800 Danish women are diagnosed with breast cancer. Several clinical and pathological factors are used as prognostic and predictive markers to categorize the patients into groups of high or low risk. Around 90% of all patients are allocated to the high risk group...... clinical courses, and they may be useful as novel prognostic biomarkers in breast cancer. The aim of the present project was to predict the development of metastasis in lymph node negative breast cancer patients by RNA profiling. We collected and analyzed 82 primary breast tumors from patients who...... and the time of event. Previous findings have shown that high expression of the lncRNA HOTAIR is correlated with poor survival in breast cancer. We validated this finding by demonstrating that high HOTAIR expression in our primary tumors was significantly associated with worse prognosis independent...

The deconvolution of sputter-etching surface concentration measurements to determine impurity depth profiles

International Nuclear Information System (INIS)

Carter, G.; Katardjiev, I.V.; Nobes, M.J.

1989-01-01

The quasi-linear partial differential continuity equations that describe the evolution of the depth profiles and surface concentrations of marker atoms in kinematically equivalent systems undergoing sputtering, ion collection and atomic mixing are solved using the method of characteristics. It is shown how atomic mixing probabilities can be deduced from measurements of ion collection depth profiles with increasing ion fluence, and how this information can be used to predict surface concentration evolution. Even with this information, however, it is shown that it is not possible to deconvolute directly the surface concentration measurements to provide initial depth profiles, except when only ion collection and sputtering from the surface layer alone occur. It is demonstrated further that optimal recovery of initial concentration depth profiles could be ensured if the concentration-measuring analytical probe preferentially sampled depths near and at the maximum depth of bombardment-induced perturbations. (author)

Segmenting by Risk Perceptions: Predicting Young Adults’ Genetic-Belief Profiles with Health and Opinion-Leader Covariates

Science.gov (United States)

Smith, Rachel A.; Greenberg, Marisa; Parrott, Roxanne L.

2014-01-01

With a growing interest in using genetic information to motivate young adults’ health behaviors, audience segmentation is needed for effective campaign design. Using latent class analysis, this study identifies segments based on young adults’ (N = 327) beliefs about genetic threats to their health and personal efficacy over genetic influences on their health. A four-class model was identified. The model indicators fit the risk perception attitude framework (Rimal & Real, 2003), but the covariates (e.g., current health behaviors) did not. In addition, opinion leader qualities covaried with one profile: those in this profile engaged in fewer preventative behaviors and more dangerous treatment options, and also liked to persuade others, making them a particularly salient group for campaign efforts. The implications for adult-onset disorders, like alpha-1 antitrypsin deficiency are discussed. PMID:24111749

Profiles of Psychological Well-being and Coping Strategies among University Students

Directory of Open Access Journals (Sweden)

Carlos Freire

2016-10-01

Full Text Available In the transactional model of stress, coping responses are the key to preventing the stress response. In this study, the possible role of psychological well-being as a personal determinant of coping strategies in the academic context was analyzed. Specifically, the study has two objectives: (a to identify different profiles of students according to their level of psychological well-being; and (b to analyze the differences between these profiles in the use of three coping strategies (positive reappraisal, support-seeking, and planning. Age, gender, and degree were estimated as covariables. A total of 1,072 university students participated in the study. Latent profile analysis was applied to four indices of psychological well-being: self-acceptance, environmental mastery, purpose in life, and personal growth. An optimal four-profile solution, reflecting significant incremental shifts from low to very high psychological well-being, was obtained. As predicted, the profile membership distinguished between participants in positive reappraisal, support-seeking, and planning. Importantly, the higher the profile of psychological well-being was, the higher the use of the three coping strategies. Gender differences in coping strategies were observed, but no interaction effects with psychological well-being were found. Age and degree were not relevant in explaining the use of coping strategies. These results suggest that psychological well-being stands as an important personal resource to favor adaptive coping strategies for academic stress.

Optimization design of airfoil profiles based on the noise of wind turbines

DEFF Research Database (Denmark)

Cheng, Jiangtao; Chen, Jin; Cheng, Jiangtao

2012-01-01

Based on design theory of airfoil profiles and airfoil self-noise prediction model, a new method with the target of the airfoil average efficiency-noise ratio of design ranges for angle of attack had been developed for designing wind turbine airfoils. The airfoil design method was optimized for a...

Analyses of plasma parameter profiles in JT-60U

Energy Technology Data Exchange (ETDEWEB)

Shirai, Hiroshi; Shimizu, Katsuhiro; Hayashi, Nobuhiko [Japan Atomic Energy Research Inst., Naka, Ibaraki (Japan). Naka Fusion Research Establishment; Itakura, Hirofumi; Takase, Keizou [CSK Co. Ltd., Tokyo (Japan)

2001-01-01

The methods how diagnostics data are treated as the surface quantity of magnetic surface and processed to the profile data in the JT-60U plasmas are summarized. The MHD equilibrium obtained by solving Grad-Shafranov equation on the MHD equilibrium calculation and registration software FBEQU are saved shot by shot as a database. Various experimental plasma data measured at various geometrical positions on JT-60 are mapped onto the MHD equilibrium and treated as functions of the volume averaged minor radius {rho} on the experimental data time slice monitoring software SLICE. Experimental data are integrated and edited on SLICE. The experimental data measured as the line integral values are transformed by Able inversion. The mapped data are fitted to a functional form and saved to the profile database MAP-DB. SLICE can also read data from MAP-DB and redisplay and transform them. In addition, SLICE can generate the profile data TOKRD as run data for orbit following Monte-Carlo (OFMC) code, analyzer for current drive consistent with MHD equilibrium (ACCOME) code and tokamak predictive and interpretive code system (TOPICS). (author)

Analyses of plasma parameter profiles in JT-60U

International Nuclear Information System (INIS)

Shirai, Hiroshi; Shimizu, Katsuhiro; Hayashi, Nobuhiko

2001-01-01

The methods how diagnostics data are treated as the surface quantity of magnetic surface and processed to the profile data in the JT-60U plasmas are summarized. The MHD equilibrium obtained by solving Grad-Shafranov equation on the MHD equilibrium calculation and registration software FBEQU are saved shot by shot as a database. Various experimental plasma data measured at various geometrical positions on JT-60 are mapped onto the MHD equilibrium and treated as functions of the volume averaged minor radius ρ on the experimental data time slice monitoring software SLICE. Experimental data are integrated and edited on SLICE. The experimental data measured as the line integral values are transformed by Able inversion. The mapped data are fitted to a functional form and saved to the profile database MAP-DB. SLICE can also read data from MAP-DB and redisplay and transform them. In addition, SLICE can generate the profile data TOKRD as run data for orbit following Monte-Carlo (OFMC) code, analyzer for current drive consistent with MHD equilibrium (ACCOME) code and tokamak predictive and interpretive code system (TOPICS). (author)

Synergism between profile and cross section shape optimization for negative central shear advanced tokamaks

International Nuclear Information System (INIS)

Turnbull, A.D.; Taylor, T.S.; Lao, L.L.

1996-01-01

The Advanced Tokamak (AT) concept is aimed at achieving high beta, high confinement, and a well aligned high bootstrap current fraction in a tokamak configuration consistent with steady state operation. The required improvements over the simple O-D scaling laws, normally used to predict standard, pulsed tokamak performance, axe obtained by taking into account the dependence of the stability and confinement on the 2-D equilibrium; the planned TPX experiment was designed to take full advantage of both advanced profiles and advanced cross-section shaping. Systematic stability studies of the promising Negative Central Shear (NCS) configuration have been performed for a wide variety of cross-section shapes and profile variations. The ideal MHD beta limit is found to be strongly dependent on both and, in fact, there is a clear synergistic relationship between the gains in beta from optimizing the profiles and optimizing the shape. Specifically, for a circular cross-section with highly peaked profiles, β is limited to normalized β values of β N = β/(I/aB) ∼ 2% (mT/MA). A small gain in beta can be achieved by broadening the pressure; however, the root-mean-square beta (β*) is slightly reduced. With peaked pressure profiles, a small increase in β N over that in a circular cross-section is also obtained by strong shaping. At fixed q, this translates to a much larger gain in β and β*. With both optimal profiles and strong shaping, however, the gain in all the relevant fusion performance parameters is dramatic; β and β* can be increased a factor 5 for example. Moreover, the bootstrap alignment is improved. For an optimized strongly shaped configuration, confinement, beta values, and bootstrap alignment adequate for a practical AT power plant appear to be realizable. Data from DIII-D supports these predictions and analysis of the DIII-D data will be presented

Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

Directory of Open Access Journals (Sweden)

S. Sadesh

2015-01-01

Full Text Available Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio.

Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

Energy Technology Data Exchange (ETDEWEB)

Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M.; Young, Vincent B.; Jansson, Janet K.; Fredricks, David N.; Borenstein, Elhanan; Sanchez, Laura M.

2015-12-22

Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in

Profile of capillary bridges between two vertically stacked cylindrical fibers under gravitational effect

Science.gov (United States)

Sun, Xiaohang; Lee, Hoon Joo; Michielsen, Stephen; Wilusz, Eugene

2018-05-01

Although profiles of axisymmetric capillary bridges between two cylindrical fibers have been extensively studied, little research has been reported on capillary bridges under external forces such as the gravitational force. This is because external forces add significant complications to the Laplace-Young equation, making it difficult to predict drop profiles based on analytical approaches. In this paper, simulations of capillary bridges between two vertically stacked cylindrical fibers with gravitational effect taken into consideration are studied. The asymmetrical structure of capillary bridges that are hard to predict based on analytical approaches was studied via a numerical approach based on Surface Evolver (SE). The axial and the circumferential spreading of liquids on two identical fibers in the presence of gravitational effects are predicted to determine when the gravitational effects are significant or can be neglected. The effect of liquid volume, equilibrium contact angle, the distance between two fibers and fiber radii. The simulation results were verified by comparing them with experimental measurements. Based on SE simulations, curves representing the spreading of capillary bridges along the two cylindrical fibers were obtained. The gravitational effect was scaled based on the difference of the spreading on upper and lower fibers.

Modelling the pultrusion process of an industrial L-shaped composite profile

DEFF Research Database (Denmark)

Baran, Ismet; Akkerman, Remko; Hattel, Jesper Henri

2014-01-01

A numerical process simulation tool is developed for the pultrusion of an industrial L-shaped profile. The composite contains the combination of uni-directional (UD) roving and continuous filament mat (CFM) layers impregnated by a polyester resin system specifically prepared for the process. The ...... inside the part such that the UD and CFM layers have different stress levels at the end of the process. The predicted stress pattern is verified by performing a stress calculation using the classical laminate theory (CLT).......A numerical process simulation tool is developed for the pultrusion of an industrial L-shaped profile. The composite contains the combination of uni-directional (UD) roving and continuous filament mat (CFM) layers impregnated by a polyester resin system specifically prepared for the process....... The chemo-rheology and elastic behavior of the resin are obtained by applying a differential scanning calorimetry (DSC) and a dynamic mechanical analyser (DMA), respectively. The process induced stresses and shape distortions are predicted in a 2D quasi-static mechanical analysis. The numerical process...

Prognostic Impact of Array-based Genomic Profiles in Esophageal Squamous Cell Cancer

International Nuclear Information System (INIS)

Carneiro, Ana; Isinger, Anna; Karlsson, Anna; Johansson, Jan; Jönsson, Göran; Bendahl, Pär-Ola; Falkenback, Dan; Halvarsson, Britta; Nilbert, Mef

2008-01-01

Esophageal squamous cell carcinoma (ESCC) is a genetically complex tumor type and a major cause of cancer related mortality. Although distinct genetic alterations have been linked to ESCC development and prognosis, the genetic alterations have not gained clinical applicability. We applied array-based comparative genomic hybridization (aCGH) to obtain a whole genome copy number profile relevant for identifying deranged pathways and clinically applicable markers. A 32 k aCGH platform was used for high resolution mapping of copy number changes in 30 stage I-IV ESCC. Potential interdependent alterations and deranged pathways were identified and copy number changes were correlated to stage, differentiation and survival. Copy number alterations affected median 19% of the genome and included recurrent gains of chromosome regions 5p, 7p, 7q, 8q, 10q, 11q, 12p, 14q, 16p, 17p, 19p, 19q, and 20q and losses of 3p, 5q, 8p, 9p and 11q. High-level amplifications were observed in 30 regions and recurrently involved 7p11 (EGFR), 11q13 (MYEOV, CCND1, FGF4, FGF3, PPFIA, FAD, TMEM16A, CTTS and SHANK2) and 11q22 (PDFG). Gain of 7p22.3 predicted nodal metastases and gains of 1p36.32 and 19p13.3 independently predicted poor survival in multivariate analysis. aCGH profiling verified genetic complexity in ESCC and herein identified imbalances of multiple central tumorigenic pathways. Distinct gains correlate with clinicopathological variables and independently predict survival, suggesting clinical applicability of genomic profiling in ESCC

Developing a forecast model of solar proton flux profiles for well-connected events

Science.gov (United States)

Ji, E. Y.; Moon, Y. J.; Park, J.

2014-12-01

We have developed a forecast model of solar proton flux profile (> 10 MeV channel) for well-connected events. Among 136 solar proton events (SPEs) from 1986 to 2006, we select 49 well-connected ones that are all associated with single X-ray flares stronger than M1 class and start to increase within four hours after their X-ray peak times. These events show rapid increments in proton flux. By comparing several empirical functions, we select a modified Weibull curve function to approximate a SPE flux profile, which is similar to the particle injection rate. The parameters (peak value, rise time and decay time) of this function are determined by the relationship between X-ray flare parameters (peak flux, impulsive time, and emission measure) and SPE parameters. For 49 well-connected SPEs, the linear correlation between the predicted proton peak flux and the observed proton peak fluxes is 0.65 with the RMS error of 0.55 pfu in the log10. In addition, we have developed another forecast model based on flare and CME parameters using 22 SPEs. The used CME parameters are linear speed and angular width. As a result, we find that the linear correlation between the predicted proton peak flux and the observed proton peak fluxes is 0.83 with the RMS error of 0.35 pfu in the log10. From the relationship between the model error and CME acceleration, we find that CME acceleration is also an important factor for predicting proton flux profiles.

Investigation of Range Profiles from a Simplified Ship on Rough Sea Surface and Its Multipath Imaging Mechanisms

Directory of Open Access Journals (Sweden)

Siyuan He

2012-01-01

Full Text Available The range profiles of a two-dimension (2 D perfect electric conductor (PEC ship on a wind-driven rough sea surface are derived by performing an inverse discrete Fourier transform (IDFT on the wide band backscattered field. The rough sea surface is assuming to be a PEC surface. The back scattered field is computed based on EM numerical simulation when the frequencies are sampled between 100 MHz and 700 MHz. Considering the strong coupling interactions between the ship and sea, the complicated multipath effect to the range profile characteristics is fully analyzed based on the multipath imaging mechanisms. The coupling mechanisms could be explained by means of ray theory prediction and numerical extraction of the coupling currents. The comparison of the range profile locations between ray theory prediction and surface current simulation is implemented and analyzed in this paper. Finally, the influence of different sea states on the radar target signatures has been examined and discussed.

Size effects in winding roll formed profiles: A study of carcass production for flexible pipes in offshore industry

DEFF Research Database (Denmark)

Nielsen, Peter Søe; Nielsen, Morten Storgaard; Bay, Niels

2013-01-01

neutral plane. Other parameters such as profile entry angle on the mandrel and spiral pitch are likely to have significant importance. Proper dividing point position is shown to be obtainable by adjusting the profile in the roll forming stage. The profile rolling is successfully modeled by Finite Element......Carcass production of flexible offshore oil and gas pipes implies winding and interlocking of a roll formed stainless steel profile around a mandrel in a spiral shape. The location of the dividing point between the left and right half of the s-shaped profile in the finished carcass is very...... Analysis (FEA), whereas a simplified FE-model of the subsequent winding operation shows that full interlock modeling is required for proper prediction of profile deformation. © (2013) Trans Tech Publications....

A Social Psychological Model for Predicting Sexual Harassment.

Science.gov (United States)

Pryor, John B.; And Others

1995-01-01

Presents a Person X Situation (PXS) model of sexual harassment suggesting that sexually harassing behavior may be predicted from an analysis of social situational and personal factors. Research on sexual harassment proclivities in men is reviewed, and a profile of men who have a high a likelihood to sexually harass is discussed. Possible PXS…

Incorporation of Resonance Upscattering and Intra-Pellet Power Profile in Direct Whole Core Calculation

International Nuclear Information System (INIS)

Lim, Chang Hyun; Jung Yeon Sang; Joo Han Gyu

2012-01-01

It was generally known that the Doppler feedback effect computed by most industrial reactor analysis codes is underestimated than the actual values. Part of the underestimation was attributed to the neglect of the resonance upscattering during the slowing down calculation. On the contrary, the edge peaked power profile noted in burned fuel pins due to more plutonium buildup at the periphery of fuel pellets might lead to smaller power defects than the predicted values obtained with a flat profile. This work is to mitigate these problems with a direct whole core calculation code nTRACER which is capable of handling ringwise depletion as well as incorporating nonuniform power profiles inside a fuel pellet

[The value of fasting plasma glucose and lipid profiles between 7 and 15 gestational weeks in the prediction of gestational diabetes mellitus].

Science.gov (United States)

Zhao, M; Li, G H

2016-11-25

Objective: To explore the value of using fasting plasma glucose (FPG) and lipid profiles between 7 and 15 gestational weeks to predict gestational diabetes mellitus (GDM). Methods: The medical records of 2 138 pregnant women who had prenatal care in Beijing Obstetrics and Gynecology Hospital from August 2011 to February 2012 were analyzed retrospectively. According to results of the oral glucose tolerance tests, women were devided into the GDM group ( n =240) and the normal group ( n= 1 898). Maternal characteristics, FPG and lipid levels between 7 and 15 gestational weeks were compared between the two groups. Logistic regression analysis and receiver operator characteristics(ROC) curve were used in the analysis. Results: Potential markers for the prediction of GDM included total cholesterol, triglyceride (TG) , low-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratios (LDL-C/HDL-C) , triglyceride to high-density lipoprotein cholesterol ratios (TG/HDL-C) and FPG. After adjustment of confounding factors, age ( OR= 1.046, 95% CI: 1.003-1.090), pre- pregnancy body mass index ( OR= 1.104, 95% CI: 1.049-1.161), gravidity>3 ( OR= 1.768, 95% CI: 1.071-2.920), FPG ( OR= 8.137, 95% CI: 5.412-12.236), TG ( OR= 1.460, 95% CI: 1.148-1.858) were independently associated with the risk of developing GDM. Equation, P GDM =1/{1+exp[-(-16.542+0.045×age+0.103×pre-pregnancy body mass index+0.551×gravidity>3+2.110×FPG+0.372×TG)]}, was constructed by the logistic regression analysis. Sensitivity (67.5%) and specificity (70.5%) were determined by the calculated risk score, with a cut-off value of 0.11 (area under the curve: 0.751, 95% CI: 0.718-0.783, P< 0.001). Conclusions: FPG and TG, together with clinical characteristics may have a better predictive value for the risk of GDM.

tRNA modification profiles of the fast-proliferating cancer cells

Energy Technology Data Exchange (ETDEWEB)

Dong, Chao; Niu, Leilei; Song, Wei [State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Department of Obstetrics and Gynecology, Peking University Third Hospital, Peking University, Beijing 100191 (China); Xiong, Xin; Zhang, Xianhua [Departmentof Pharmacy, Peking University Third Hospital, Peking University, Beijing 100191 (China); Zhang, Zhenxi; Yang, Yi; Yi, Fan [State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Department of Obstetrics and Gynecology, Peking University Third Hospital, Peking University, Beijing 100191 (China); Zhan, Jun; Zhang, Hongquan [Department of Anatomy, Histology and Embryology, Laboratory of Molecular Cell Biology and Tumor Biology, Peking University, Beijing 100191 (China); Yang, Zhenjun; Zhang, Li-He [State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Department of Obstetrics and Gynecology, Peking University Third Hospital, Peking University, Beijing 100191 (China); Zhai, Suodi [Departmentof Pharmacy, Peking University Third Hospital, Peking University, Beijing 100191 (China); Li, Hua, E-mail: huali88@sina.com [State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Department of Obstetrics and Gynecology, Peking University Third Hospital, Peking University, Beijing 100191 (China); Ye, Min, E-mail: yemin@bjmu.edu.cn [State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Department of Obstetrics and Gynecology, Peking University Third Hospital, Peking University, Beijing 100191 (China); Du, Quan, E-mail: quan.du@pku.edu.cn [State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Department of Obstetrics and Gynecology, Peking University Third Hospital, Peking University, Beijing 100191 (China)

2016-08-05

Despite the recent progress in RNA modification study, a comprehensive modification profile is still lacking for mammalian cells. Using a quantitative HPLC/MS/MS assay, we present here a study where RNA modifications are examined in term of the major RNA species. With paired slow- and fast-proliferating cell lines, distinct RNA modification profiles are first revealed for diverse RNA species. Compared to mRNAs, increased ribose and nucleobase modifications are shown for the highly-structured tRNAs and rRNAs, lending support to their contribution to the formation of high-order structures. This study also reveals a dynamic tRNA modification profile in the fast-proliferating cells. In addition to cultured cells, this unique tRNA profile has been further confirmed with endometrial cancers and their adjacent normal tissues. Taken together, the results indicate that tRNA is a actively regulated RNA species in the fast-proliferating cancer cells, and suggest that they may play a more active role in biological process than expected. -- Highlights: •RNA modifications were first examined in term of the major RNA species. •A dynamic tRNA modifications was characterized for the fast-proliferating cells. •The unique tRNA profile was confirmed with endometrial cancers and their adjacent normal tissues. •tRNA was predicted as an actively regulated RNA species in the fast-proliferating cancer cells.

tRNA modification profiles of the fast-proliferating cancer cells

International Nuclear Information System (INIS)

Dong, Chao; Niu, Leilei; Song, Wei; Xiong, Xin; Zhang, Xianhua; Zhang, Zhenxi; Yang, Yi; Yi, Fan; Zhan, Jun; Zhang, Hongquan; Yang, Zhenjun; Zhang, Li-He; Zhai, Suodi; Li, Hua; Ye, Min; Du, Quan

2016-01-01

Despite the recent progress in RNA modification study, a comprehensive modification profile is still lacking for mammalian cells. Using a quantitative HPLC/MS/MS assay, we present here a study where RNA modifications are examined in term of the major RNA species. With paired slow- and fast-proliferating cell lines, distinct RNA modification profiles are first revealed for diverse RNA species. Compared to mRNAs, increased ribose and nucleobase modifications are shown for the highly-structured tRNAs and rRNAs, lending support to their contribution to the formation of high-order structures. This study also reveals a dynamic tRNA modification profile in the fast-proliferating cells. In addition to cultured cells, this unique tRNA profile has been further confirmed with endometrial cancers and their adjacent normal tissues. Taken together, the results indicate that tRNA is a actively regulated RNA species in the fast-proliferating cancer cells, and suggest that they may play a more active role in biological process than expected. -- Highlights: •RNA modifications were first examined in term of the major RNA species. •A dynamic tRNA modifications was characterized for the fast-proliferating cells. •The unique tRNA profile was confirmed with endometrial cancers and their adjacent normal tissues. •tRNA was predicted as an actively regulated RNA species in the fast-proliferating cancer cells.

On the same wavelength: predictable language enhances speaker-listener brain-to-brain synchrony in posterior superior temporal gyrus.

Science.gov (United States)

Dikker, Suzanne; Silbert, Lauren J; Hasson, Uri; Zevin, Jason D

2014-04-30

Recent research has shown that the degree to which speakers and listeners exhibit similar brain activity patterns during human linguistic interaction is correlated with communicative success. Here, we used an intersubject correlation approach in fMRI to test the hypothesis that a listener's ability to predict a speaker's utterance increases such neural coupling between speakers and listeners. Nine subjects listened to recordings of a speaker describing visual scenes that varied in the degree to which they permitted specific linguistic predictions. In line with our hypothesis, the temporal profile of listeners' brain activity was significantly more synchronous with the speaker's brain activity for highly predictive contexts in left posterior superior temporal gyrus (pSTG), an area previously associated with predictive auditory language processing. In this region, predictability differentially affected the temporal profiles of brain responses in the speaker and listeners respectively, in turn affecting correlated activity between the two: whereas pSTG activation increased with predictability in the speaker, listeners' pSTG activity instead decreased for more predictable sentences. Listeners additionally showed stronger BOLD responses for predictive images before sentence onset, suggesting that highly predictable contexts lead comprehenders to preactivate predicted words.

Predictive integrated modelling for ITER scenarios

International Nuclear Information System (INIS)

Artaud, J.F.; Imbeaux, F.; Aniel, T.; Basiuk, V.; Eriksson, L.G.; Giruzzi, G.; Hoang, G.T.; Huysmans, G.; Joffrin, E.; Peysson, Y.; Schneider, M.; Thomas, P.

2005-01-01

The uncertainty on the prediction of ITER scenarios is evaluated. 2 transport models which have been extensively validated against the multi-machine database are used for the computation of the transport coefficients. The first model is GLF23, the second called Kiauto is a model in which the profile of dilution coefficient is a gyro Bohm-like analytical function, renormalized in order to get profiles consistent with a given global energy confinement scaling. The package of codes CRONOS is used, it gives access to the dynamics of the discharge and allows the study of interplay between heat transport, current diffusion and sources. The main motivation of this work is to study the influence of parameters such plasma current, heat, density, impurities and toroidal moment transport. We can draw the following conclusions: 1) the target Q = 10 can be obtained in ITER hybrid scenario at I p = 13 MA, using either the DS03 two terms scaling or the GLF23 model based on the same pedestal; 2) I p = 11.3 MA, Q = 10 can be reached only assuming a very peaked pressure profile and a low pedestal; 3) at fixed Greenwald fraction, Q increases with density peaking; 4) achieving a stationary q-profile with q > 1 requires a large non-inductive current fraction (80%) that could be provided by 20 to 40 MW of LHCD; and 5) owing to the high temperature the q-profile penetration is delayed and q = 1 is reached about 600 s in ITER hybrid scenario at I p = 13 MA, in the absence of active q-profile control. (A.C.)

Genome-enabled prediction models for yield related traits in chickpea

Science.gov (United States)

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped...

Towards cycle-accurate performance predictions for real-time embedded systems

NARCIS (Netherlands)

Triantafyllidis, K.; Bondarev, E.; With, de P.H.N.; Arabnia, H.R.; Deligiannidis, L.; Jandieri, G.

2013-01-01

In this paper we present a model-based performance analysis method for component-based real-time systems, featuring cycle-accurate predictions of latencies and enhanced system robustness. The method incorporates the following phases: (a) instruction-level profiling of SW components, (b) modeling the

THE DARK MATTER DENSITY PROFILE OF THE FORNAX DWARF

International Nuclear Information System (INIS)

Jardel, John R.; Gebhardt, Karl

2012-01-01

We construct axisymmetric Schwarzschild models to measure the mass profile of the Local Group dwarf galaxy Fornax. These models require no assumptions to be made about the orbital anisotropy of the stars, as is the case for commonly used Jeans models. We test a variety of parameterizations of dark matter density profiles and find cored models with uniform density ρ c = (1.6 ± 0.1) × 10 –2 M ☉ pc –3 fit significantly better than the cuspy halos predicted by cold dark matter simulations. We also construct models with an intermediate-mass black hole, but are unable to make a detection. We place a 1σ upper limit on the mass of a potential intermediate-mass black hole at M . ≤ 3.2 × 10 4 M ☉ .

Reliability Assessment of Transformerless PV Inverters Considering Mission Profiles

DEFF Research Database (Denmark)

Yang, Yongheng; Wang, Huai; Blaabjerg, Frede

2015-01-01

reliability of three transformerless inverters under a yearly mission profile (i.e., solar irradiance and ambient temperature). The mission profile is translated to device thermal loading, which is used for lifetime prediction. Compar¬ison results reveal the lifetime mismatches among the power switching......Due to the small volume and high efficiency, transformerless inverters have gained much popularity in grid-connected PV applications, where minimizing leakage current injection is mandatory. This can be achieved either by modifying the modulation schemes or adding extra power switching devices......, resulting in an uneven distribution of the power losses on the switching devices. Consequently, the device thermal loading is redis-tributed, and thus may alter the entire inverter reliability performance, especially under a long-term operation. In this consideration, this paper assesses the device...

Preliminary results of spatially resolved ECR ion beam profile investigations

International Nuclear Information System (INIS)

Panitzsch, L.; Stalder, M.; Wimmer-Schweingruber, R.F.

2012-01-01

The profile of an ion beam produced in an Electron Cyclotron Resonance Ion Source (ECRIS) can vary greatly depending on the source settings and the ion-optical tuning. Strongly focussed ion beams form circular structures (hollow beams) as predicted by simulations and observed in experiments. Each of the rings is predicted to be dominated by ions with same or at least similar m/q-ratios due to ion-optical effects. To check this we performed a series of preliminary investigations to test the required tuning capabilities of our ion source. This includes beam focussing (A) and beam steering (B) using a 3D-movable extraction. Having tuned the source to deliver a beam of strongly focussed ions of different ion species and having steered this beam to match the transmittance area of the sector magnet we also recorded the ion charge state distribution of the strongly focussed beam profile at different, spatially limited positions (C). The preliminary results will be introduced within this paper: it appears that our 3D-movable extraction is very efficient to steer and to focus the beam strongly. The paper is followed by the slides of the presentation. (authors)

Merlin : microsimulation system for predicting leisure activity-travel patterns

NARCIS (Netherlands)

Middelkoop, van M.; Borgers, A.W.J.; Timmermans, H.J.P.

2004-01-01

Development of a model of annual activity-travel patterns of leisure and vacation travel is reported. The simulation system, called Merlin, is a hybrid model system consisting of discrete choice models and rule-based models. It predicts the annual number of day trips and vacations, and the profile

Molecular prediction of adjuvant cisplatin efficacy in Non-Small Cell Lung Cancer (NSCLC)—validation in two independent cohorts

DEFF Research Database (Denmark)

Buhl, Ida Kappel; Santoni Rugiu, Eric; Ravn, Jesper

2018-01-01

Introduction Effective predictive biomarkers for selection of patients benefiting from adjuvant platinum-based chemotherapy in non-small cell lung cancer (NSCLC) are needed. Based on a previously validated methodology, molecular profiles of predicted sensitivity in two patient cohorts are presented....... Methods The profiles are correlations between in vitro sensitivity to cisplatin and vinorelbine and baseline mRNA expression of the 60 cell lines in the National Cancer Institute panel. An applied clinical samples filter focused the profiles to clinically relevant genes. The profiles were tested on 1......) univariate HR of 0.37 (95% CI:0.12–1.15, p = 0.09) in the ACV cohort and 2) univariate HR of 0.14 (95% CI:0.03–0.59, p = 0.0076) to three years. Functional analysis on the cisplatin profile revealed a group of upregulated genes related to RNA splicing as a part of DNA damage repair and apoptosis. Conclusions...

Settlement Prediction of Footings Using VS

Directory of Open Access Journals (Sweden)

Hyung Ik CHO

2017-10-01

Full Text Available The shear wave velocity (VS is a key parameter for estimating the deformation characteristics of soil. In order to predict the settlement of shallow footings in granular soil, the VS and the concept of Schmertmann’s framework were adopted. The VS was utilized to represent soil stiffness instead of cone tip resistance (qc because the VS can be directly related to the small-strain shear modulus. By combining the VS measured in the field and the modulus reduction curve measured in the laboratory, the deformation characteristics of soil can be reliably estimated. Vertical stress increments were determined using two different profiles of the strain influence factor (Iz proposed in Schmertmann’s method and that calculated from the theory of elasticity. The corresponding modulus variation was determined by considering the stress level and strain at each depth. This state-dependent stress-strain relationship was utilized to calculate the settlement of footings based on the theory of elasticity. To verify the developed method, geotechnical centrifuge tests were carried out. The VS profiles were measured before each loading test, and the load-settlement curves were obtained during the tests. Comparisons between the measured and estimated load-settlement curves showed that the developed method adequately predicts the settlement of footings, especially for over-consolidated ground conditions.

Halo scale predictions of symmetron modified gravity

Energy Technology Data Exchange (ETDEWEB)

Clampitt, Joseph; Jain, Bhuvnesh; Khoury, Justin, E-mail: clampitt@sas.upenn.edu, E-mail: bjain@physics.upenn.edu, E-mail: jkhoury@sas.upenn.edu [Center for Particle Cosmology and Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd St., Philadelphia, PA 19104 (United States)

2012-01-01

We offer predictions of symmetron modified gravity in the neighborhood of realistic dark matter halos. The predictions for the fifth force are obtained by solving the nonlinear symmetron equation of motion in the spherical NFW approximation. In addition, we compare the three major known screening mechanisms: Vainshtein, Chameleon, and Symmetron around such dark matter halos, emphasizing the significant differences between them and highlighting observational tests which exploit these differences. Finally, we demonstrate the host halo environmental screening effect (''blanket screening'') on smaller satellite halos by solving for the modified forces around a density profile which is the sum of satellite and approximate host components.

Modeled Radar Attenuation Rate Profile at the Vostok 5G Ice Core Site, Antarctica, Version 1

Data.gov (United States)

National Aeronautics and Space Administration — This data set provides a modeled radar attenuation rate profile, showing the predicted contributions from pure ice and impurities to radar attenuation at the Vostok...

Shave-off depth profiling: Depth profiling with an absolute depth scale

International Nuclear Information System (INIS)

Nojima, M.; Maekawa, A.; Yamamoto, T.; Tomiyasu, B.; Sakamoto, T.; Owari, M.; Nihei, Y.

2006-01-01

Shave-off depth profiling provides profiling with an absolute depth scale. This method uses a focused ion beam (FIB) micro-machining process to provide the depth profile. We show that the shave-off depth profile of a particle reflected the spherical shape of the sample and signal intensities had no relationship to the depth. Through the introduction of FIB micro-sampling, the shave-off depth profiling of a dynamic random access memory (DRAM) tip was carried out. The shave-off profile agreed with a blue print from the manufacturing process. Finally, shave-off depth profiling is discussed with respect to resolutions and future directions

Building a profile of subjective well-being for social media users

Science.gov (United States)

Kosinski, Michal; Stillwell, David; Davidson, Robert L.

2017-01-01

Subjective well-being includes ‘affect’ and ‘satisfaction with life’ (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users’ affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p social media language. PMID:29135991

The effect of mandrel configuration on the warpage in pultrusion of rectangular hollow profiles