Structural features that predict real-value fluctuations of globular proteins.

Science.gov (United States)

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-05-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

Predictive value of early near-infrared spectroscopy monitoring of patients with traumatic brain injury.

Science.gov (United States)

Vilkė, Alina; Bilskienė, Diana; Šaferis, Viktoras; Gedminas, Martynas; Bieliauskaitė, Dalia; Tamašauskas, Arimantas; Macas, Andrius

2014-01-01

Traumatic brain injury (TBI) is the leading cause of death and disability in young adults. Study aimed to define the predictive value of early near-infrared spectroscopy (NIRS) monitoring of TBI patients in a Lithuanian clinical setting. Data of 61 patients was analyzed. Predictive value of early NIRS monitoring, computed tomography data and regular intensive care unit (ICU) parameters was investigated. Twenty-six patients expressed clinically severe TBI; 14 patients deceased. Patients who survived expressed higher NIRS values at the periods of admission to operative room (75.4%±9.8% vs. 71.0%±20.5%; P=0.013) and 1h after admission to ICU (74.7%±1.5% vs. 61.9%±19.4%; P=0.029). The NIRS values discriminated hospital mortality groups more accurately than admission GCS score, blood sugar or hemoglobin levels. Admission INR value and NIRS value at 1h after admission to ICU were selected by discriminant analysis into the optimal set of features when classifying hospital mortality groups. Average efficiency of classification using this method was 88.9%. When rsO2 values at 1h after admission to ICU did not exceed 68.0% in the left hemisphere and 68.3% in the right hemisphere, the hazard ratio for death increased by 17.7 times (Pbrain saturation monitoring provides accurate predictive data, which can improve the allocation of scarce medical resources, set the treatment goals and alleviate the early communication with patients' relatives. Copyright © 2014 Lithuanian University of Health Sciences. Production and hosting by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Developing hybrid approaches to predict pKa values of ionizable groups

Science.gov (United States)

Witham, Shawn; Talley, Kemper; Wang, Lin; Zhang, Zhe; Sarkar, Subhra; Gao, Daquan; Yang, Wei

2011-01-01

Accurate predictions of pKa values of titratable groups require taking into account all relevant processes associated with the ionization/deionization. Frequently, however, the ionization does not involve significant structural changes and the dominating effects are purely electrostatic in origin allowing accurate predictions to be made based on the electrostatic energy difference between ionized and neutral forms alone using a static structure. On another hand, if the change of the charge state is accompanied by a structural reorganization of the target protein, then the relevant conformational changes have to be taken into account in the pKa calculations. Here we report a hybrid approach that first predicts the titratable groups, which ionization is expected to cause conformational changes, termed “problematic” residues, then applies a special protocol on them, while the rest of the pKa’s are predicted with rigid backbone approach as implemented in multi-conformation continuum electrostatics (MCCE) method. The backbone representative conformations for “problematic” groups are generated with either molecular dynamics simulations with charged and uncharged amino acid or with ab-initio local segment modeling. The corresponding ensembles are then used to calculate the pKa of the “problematic” residues and then the results are averaged. PMID:21744395

Accurate Holdup Calculations with Predictive Modeling & Data Integration

Energy Technology Data Exchange (ETDEWEB)

Azmy, Yousry [North Carolina State Univ., Raleigh, NC (United States). Dept. of Nuclear Engineering; Cacuci, Dan [Univ. of South Carolina, Columbia, SC (United States). Dept. of Mechanical Engineering

2017-04-03

Bayes’ Theorem, one must have a model y(x) that maps the state variables x (the solution in this case) to the measurements y. In this case, the unknown state variables are the configuration and composition of the heldup SNM. The measurements are the detector readings. Thus, the natural model is neutral-particle radiation transport where a wealth of computational tools exists for performing these simulations accurately and efficiently. The combination of predictive model and Bayesian inference forms the Data Integration with Modeled Predictions (DIMP) method that serves as foundation for this project. The cost functional describing the model-to-data misfit is computed via a norm created by the inverse of the covariance matrix of the model parameters and responses. Since the model y(x) for the holdup problem is nonlinear, a nonlinear optimization on Q is conducted via Newton-type iterative methods to find the optimal values of the model parameters x. This project comprised a collaboration between NC State University (NCSU), the University of South Carolina (USC), and Oak Ridge National Laboratory (ORNL). The project was originally proposed in seven main tasks with an eighth contingency task to be performed if time and funding permitted; in fact time did not permit commencement of the contingency task and it was not performed. The remaining tasks involved holdup analysis with gamma detection strategies and separately with neutrons based on coincidence counting. Early in the project, and upon consultation with experts in coincidence counting it became evident that this approach is not viable for holdup applications and this task was replaced with an alternative, but valuable investigation that was carried out by the USC partner. Nevertheless, the experimental 4 measurements at ORNL of both gamma and neutron sources for the purpose of constructing Detector Response Functions (DRFs) with the associated uncertainties were indeed completed.

A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

DEFF Research Database (Denmark)

Græsbøll, Kaare; Kirkeby, Carsten Thure; Nielsen, Søren Saxmose

2017-01-01

The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction of the fut...

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

Prediction of strain values in reinforcements and concrete of a RC frame using neural networks

Science.gov (United States)

Vafaei, Mohammadreza; Alih, Sophia C.; Shad, Hossein; Falah, Ali; Halim, Nur Hajarul Falahi Abdul

2018-03-01

The level of strain in structural elements is an important indicator for the presence of damage and its intensity. Considering this fact, often structural health monitoring systems employ strain gauges to measure strains in critical elements. However, because of their sensitivity to the magnetic fields, inadequate long-term durability especially in harsh environments, difficulties in installation on existing structures, and maintenance cost, installation of strain gauges is not always possible for all structural components. Therefore, a reliable method that can accurately estimate strain values in critical structural elements is necessary for damage identification. In this study, a full-scale test was conducted on a planar RC frame to investigate the capability of neural networks for predicting the strain values. Two neural networks each of which having a single hidden layer was trained to relate the measured rotations and vertical displacements of the frame to the strain values measured at different locations of the frame. Results of trained neural networks indicated that they accurately estimated the strain values both in reinforcements and concrete. In addition, the trained neural networks were capable of predicting strains for the unseen input data set.

Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

Science.gov (United States)

Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

2018-02-01

Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

Science.gov (United States)

Rahmati, Mehdi

2017-08-01

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values.

Science.gov (United States)

Shackelford, Stacy; Yang, Shiming; Hu, Peter; Miller, Catriona; Anazodo, Amechi; Galvagno, Samuel; Wang, Yulei; Hartsky, Lauren; Fang, Raymond; Mackenzie, Colin

2015-10-01

Identification of hemorrhaging trauma patients and prediction of blood transfusion needs in near real time will expedite care of the critically injured. We hypothesized that automated analysis of pulse oximetry signals in combination with laboratory values and vital signs obtained at the time of triage would predict the need for blood transfusion with accuracy greater than that of triage vital signs or pulse oximetry analysis alone. Continuous pulse oximetry signals were recorded for directly admitted trauma patients with abnormal prehospital shock index (heart rate [HR] / systolic blood pressure) of 0.62 or greater. Predictions of blood transfusion within 24 hours were compared using Delong's method for area under the receiver operating characteristic (AUROC) curves to determine the optimal combination of triage vital signs (prehospital HR + systolic blood pressure), pulse oximetry features (40 waveform features, O2 saturation, HR), and laboratory values (hematocrit, electrolytes, bicarbonate, prothrombin time, international normalization ratio, lactate) in multivariate logistic regression models. We enrolled 1,191 patients; 339 were excluded because of incomplete data; 40 received blood within 3 hours; and 14 received massive transfusion. Triage vital signs predicted need for transfusion within 3 hours (AUROC, 0.59) and massive transfusion (AUROC, 0.70). Pulse oximetry for 15 minutes predicted transfusion more accurately than triage vital signs for both time frames (3-hour AUROC, 0.74; p = 0.004) (massive transfusion AUROC, 0.88; p transfusion prediction (3-hour AUROC, 0.84; p transfusion AUROC, 0.91; p blood transfusion during trauma resuscitation more accurately than triage vital signs or pulse oximetry analysis alone. Results suggest automated calculations from a noninvasive vital sign monitor interfaced with a point-of-care laboratory device may support clinical decisions by recognizing patients with hemorrhage sufficient to need transfusion. Epidemiologic

Predictive values of thermal and electrical dental pulp tests: a clinical study.

Science.gov (United States)

Villa-Chávez, Carlos E; Patiño-Marín, Nuria; Loyola-Rodríguez, Juan P; Zavala-Alonso, Norma V; Martínez-Castañón, Gabriel A; Medina-Solís, Carlo E

2013-08-01

For a diagnostic test to be useful, it is necessary to determine the probability that the test will provide the correct diagnosis. Therefore, it is necessary to calculate the predictive value of diagnostics. The aim of the present study was to identify the sensitivity, specificity, positive and negative predictive values, accuracy, and reproducibility of thermal and electrical tests of pulp sensitivity. The thermal tests studied were the 1, 1, 1, 2-tetrafluoroethane (cold) and hot gutta-percha (hot) tests. For the electrical test, the Analytic Technology Pulp Tester (Analytic Technology, Redmond, WA) was used. A total of 110 teeth were tested: 60 teeth with vital pulp and 50 teeth with necrotic pulps (disease prevalence of 45%). The ideal standard was established by direct pulp inspection. The sensitivities of the diagnostic tests were 0.88 for the cold test, 0.86 for the heat test, and 0.76 for the electrical test, and the specificity was 1.0 for all 3 tests. The negative predictive value was 0.90 for the cold test, 0.89 for the heat test, and 0.83 for the electrical test, and the positive predictive value was 1.0 for all 3 tests. The highest accuracy (0.94) and reproducibility (0.88) were observed for the cold test. The cold test was the most accurate method for diagnostic testing. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes.

Science.gov (United States)

Clark, Samuel A; Hickey, John M; Daetwyler, Hans D; van der Werf, Julius H J

2012-02-09

The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values. Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated. The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy. An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.

Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

Science.gov (United States)

Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

2018-06-01

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

DEFF Research Database (Denmark)

Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2008-01-01

Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate...

Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

Science.gov (United States)

Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

2015-09-01

There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

Energy Technology Data Exchange (ETDEWEB)

Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

2008-10-20

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

The reliability, validity, sensitivity, specificity and predictive values of the Chinese version of the Rowland Universal Dementia Assessment Scale.

Science.gov (United States)

Chen, Chia-Wei; Chu, Hsin; Tsai, Chia-Fen; Yang, Hui-Ling; Tsai, Jui-Chen; Chung, Min-Huey; Liao, Yuan-Mei; Chi, Mei-Ju; Chou, Kuei-Ru

2015-11-01

The purpose of this study was to translate the Rowland Universal Dementia Assessment Scale into Chinese and to evaluate the psychometric properties (reliability and validity) and the diagnostic properties (sensitivity, specificity and predictive values) of the Chinese version of the Rowland Universal Dementia Assessment Scale. The accurate detection of early dementia requires screening tools with favourable cross-cultural linguistic and appropriate sensitivity, specificity, and predictive values, particularly for Chinese-speaking populations. This was a cross-sectional, descriptive study. Overall, 130 participants suspected to have cognitive impairment were enrolled in the study. A test-retest for determining reliability was scheduled four weeks after the initial test. Content validity was determined by five experts, whereas construct validity was established by using contrasted group technique. The participants' clinical diagnoses were used as the standard in calculating the sensitivity, specificity, positive predictive value and negative predictive value. The study revealed that the Chinese version of the Rowland Universal Dementia Assessment Scale exhibited a test-retest reliability of 0.90, an internal consistency reliability of 0.71, an inter-rater reliability (kappa value) of 0.88 and a content validity index of 0.97. Both the patients and healthy contrast group exhibited significant differences in their cognitive ability. The optimal cut-off points for the Chinese version of the Rowland Universal Dementia Assessment Scale in the test for mild cognitive impairment and dementia were 24 and 22, respectively; moreover, for these two conditions, the sensitivities of the scale were 0.79 and 0.76, the specificities were 0.91 and 0.81, the areas under the curve were 0.85 and 0.78, the positive predictive values were 0.99 and 0.83 and the negative predictive values were 0.96 and 0.91 respectively. The Chinese version of the Rowland Universal Dementia Assessment Scale

A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

DEFF Research Database (Denmark)

Græsbøll, Kaare; Kirkeby, Carsten Thure; Nielsen, Søren Saxmose

2017-01-01

of the future value of a dairy cow requires further detailed knowledge of the costs associated with feed, management practices, production systems, and disease. Here, we present a method to predict the future value of the milk production of a dairy cow based on herd recording data only. The method consists......The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction...... of somatic cell count. We conclude that estimates of future average production can be used on a day-to-day basis to rank cows for culling, or can be implemented in simulation models of within-herd disease spread to make operational decisions, such as culling versus treatment. An advantage of the approach...

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

Directory of Open Access Journals (Sweden)

Jaroslav Bendl

2014-01-01

Full Text Available Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

Science.gov (United States)

Voorhies, Coerte V.; Conrad, Joy

1996-01-01

The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

A new, accurate predictive model for incident hypertension.

Science.gov (United States)

Völzke, Henry; Fung, Glenn; Ittermann, Till; Yu, Shipeng; Baumeister, Sebastian E; Dörr, Marcus; Lieb, Wolfgang; Völker, Uwe; Linneberg, Allan; Jørgensen, Torben; Felix, Stephan B; Rettig, Rainer; Rao, Bharat; Kroemer, Heyo K

2013-11-01

Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures. The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study. In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99. Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.

Predictive value of the present-on-admission indicator for hospital-acquired venous thromboembolism.

Science.gov (United States)

Khanna, Raman R; Kim, Sharon B; Jenkins, Ian; El-Kareh, Robert; Afsarmanesh, Nasim; Amin, Alpesh; Sand, Heather; Auerbach, Andrew; Chia, Catherine Y; Maynard, Gregory; Romano, Patrick S; White, Richard H

2015-04-01

Hospital-acquired venous thromboembolic (HA-VTE) events are an important, preventable cause of morbidity and death, but accurately identifying HA-VTE events requires labor-intensive chart review. Administrative diagnosis codes and their associated "present-on-admission" (POA) indicator might allow automated identification of HA-VTE events, but only if VTE codes are accurately flagged "not present-on-admission" (POA=N). New codes were introduced in 2009 to improve accuracy. We identified all medical patients with at least 1 VTE "other" discharge diagnosis code from 5 academic medical centers over a 24-month period. We then sampled, within each center, patients with VTE codes flagged POA=N or POA=U (insufficient documentation) and POA=Y or POA=W (timing clinically uncertain) and abstracted each chart to clarify VTE timing. All events that were not clearly POA were classified as HA-VTE. We then calculated predictive values of the POA=N/U flags for HA-VTE and the POA=Y/W flags for non-HA-VTE. Among 2070 cases with at least 1 "other" VTE code, we found 339 codes flagged POA=N/U and 1941 flagged POA=Y/W. Among 275 POA=N/U abstracted codes, 75.6% (95% CI, 70.1%-80.6%) were HA-VTE; among 291 POA=Y/W abstracted events, 73.5% (95% CI, 68.0%-78.5%) were non-HA-VTE. Extrapolating from this sample, we estimated that 59% of actual HA-VTE codes were incorrectly flagged POA=Y/W. POA indicator predictive values did not improve after new codes were introduced in 2009. The predictive value of VTE events flagged POA=N/U for HA-VTE was 75%. However, sole reliance on this flag may substantially underestimate the incidence of HA-VTE.

[Value of PUSSOM and P-POSSUM for the prediction of surgical operative risk in patients undergoing pancreaticoduodenectomy for periampullary tumors].

Science.gov (United States)

Chen, Yingtai; Chu, Yunmian; Che, Xu; Lan, Zhongmin; Zhang, Jianwei; Wang, Chengfeng

2015-06-01

To investigate the value of Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) and a modification of the POSSUM system (P-P0SSUM) scoring system in predicting the surgical operative risk of pancreaticoduodenectomy for periampullary tumors. POSSUM and P-POSSUM scoring systems were used to retrospectively evaluate the clinical data of 432 patients with periampullar tumors who underwent pancreaticoduodenectomy in the Department of Abdominal Surgery, Cancer Hospital, Chinese Academy of Medical Sciences from January 1985 to December 2010. The predictive occurrence of postoperative complications and mortality rate were calculated according to the formula. ROC curve analysis and different group of risk factors were used to determine the discrimination ability of the two score systems, and to determine their predictive efficacy by comparing the actual and predictive complications and mortality rates, using Hosmer-Lemeshow test to determine the goodness of fit of the two scoring systems. The average physiological score of the 432 patients was 16.1 ± 3.5, and the average surgical severity score was 19.6 ± 2.7. ROC curve analysis showed that the area under ROC curve for mortality predicted by POSSUM and P-POSSUM were 0.893 and 0.888, showing a non-significant difference (P > 0.05) between them. The area under ROC curve for operative complications predicted by POSSUM scoring system was 0.575. The POSSUM score system was most accurate for the prediction of complication rates of 20%-40%, showing the O/E value of 0.81. Compared with the POSSUM score system, P-POSSUM had better ability in the prediction of postoperative mortality, when the predicted value of mortality was greater than 15%, the predictive result was more accurate, and the O/E value was 1.00. POSSUM and P-POSSUM scoring system have good value in predicting the mortality of patients with periampullary tumors undergoing pancreaticoduodenectomy, but a poorer value of

Accurate prediction of the dew points of acidic combustion gases by using an artificial neural network model

International Nuclear Information System (INIS)

ZareNezhad, Bahman; Aminian, Ali

2011-01-01

This paper presents a new approach based on using an artificial neural network (ANN) model for predicting the acid dew points of the combustion gases in process and power plants. The most important acidic combustion gases namely, SO 3 , SO 2 , NO 2 , HCl and HBr are considered in this investigation. Proposed Network is trained using the Levenberg-Marquardt back propagation algorithm and the hyperbolic tangent sigmoid activation function is applied to calculate the output values of the neurons of the hidden layer. According to the network's training, validation and testing results, a three layer neural network with nine neurons in the hidden layer is selected as the best architecture for accurate prediction of the acidic combustion gases dew points over wide ranges of acid and moisture concentrations. The proposed neural network model can have significant application in predicting the condensation temperatures of different acid gases to mitigate the corrosion problems in stacks, pollution control devices and energy recovery systems.

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.

Science.gov (United States)

Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

2015-07-07

Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

Directory of Open Access Journals (Sweden)

Xin Deng

2015-07-01

Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

International Nuclear Information System (INIS)

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-01-01

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

Rapid and accurate prediction and scoring of water molecules in protein binding sites.

Directory of Open Access Journals (Sweden)

Gregory A Ross

Full Text Available Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

Mini-Mental Status Examination: a short form of MMSE was as accurate as the original MMSE in predicting dementia

DEFF Research Database (Denmark)

Schultz-Larsen, Kirsten; Lomholt, Rikke Kirstine; Kreiner, Svend

2006-01-01

.4%), and positive predictive value (71.0%) but equal area under the receiver operating characteristic curve. Cross-validation on follow-up data confirmed the results. CONCLUSION: A short, valid MMSE, which is as sensitive and specific as the original MMSE for the screening of cognitive impairments and dementia......OBJECTIVES: This study assesses the properties of the Mini-Mental State Examination (MMSE) with the purpose of improving the efficiencies of the methods of screening for cognitive impairment and dementia. A specific purpose was to determine whether an abbreviated version would be as accurate...... is attractive for research and clinical practice, particularly if predictive power can be enhanced by combining the short MMSE with neuropsychological tests or informant reports....

Differential contribution of visual and auditory information to accurately predict the direction and rotational motion of a visual stimulus.

Science.gov (United States)

Park, Seoung Hoon; Kim, Seonjin; Kwon, MinHyuk; Christou, Evangelos A

2016-03-01

Vision and auditory information are critical for perception and to enhance the ability of an individual to respond accurately to a stimulus. However, it is unknown whether visual and auditory information contribute differentially to identify the direction and rotational motion of the stimulus. The purpose of this study was to determine the ability of an individual to accurately predict the direction and rotational motion of the stimulus based on visual and auditory information. In this study, we recruited 9 expert table-tennis players and used table-tennis service as our experimental model. Participants watched recorded services with different levels of visual and auditory information. The goal was to anticipate the direction of the service (left or right) and the rotational motion of service (topspin, sidespin, or cut). We recorded their responses and quantified the following outcomes: (i) directional accuracy and (ii) rotational motion accuracy. The response accuracy was the accurate predictions relative to the total number of trials. The ability of the participants to predict the direction of the service accurately increased with additional visual information but not with auditory information. In contrast, the ability of the participants to predict the rotational motion of the service accurately increased with the addition of auditory information to visual information but not with additional visual information alone. In conclusion, this finding demonstrates that visual information enhances the ability of an individual to accurately predict the direction of the stimulus, whereas additional auditory information enhances the ability of an individual to accurately predict the rotational motion of stimulus.

In vitro transcription accurately predicts lac repressor phenotype in vivo in Escherichia coli

Directory of Open Access Journals (Sweden)

Matthew Almond Sochor

2014-07-01

Full Text Available A multitude of studies have looked at the in vivo and in vitro behavior of the lac repressor binding to DNA and effector molecules in order to study transcriptional repression, however these studies are not always reconcilable. Here we use in vitro transcription to directly mimic the in vivo system in order to build a self consistent set of experiments to directly compare in vivo and in vitro genetic repression. A thermodynamic model of the lac repressor binding to operator DNA and effector is used to link DNA occupancy to either normalized in vitro mRNA product or normalized in vivo fluorescence of a regulated gene, YFP. An accurate measurement of repressor, DNA and effector concentrations were made both in vivo and in vitro allowing for direct modeling of the entire thermodynamic equilibrium. In vivo repression profiles are accurately predicted from the given in vitro parameters when molecular crowding is considered. Interestingly, our measured repressor–operator DNA affinity differs significantly from previous in vitro measurements. The literature values are unable to replicate in vivo binding data. We therefore conclude that the repressor-DNA affinity is much weaker than previously thought. This finding would suggest that in vitro techniques that are specifically designed to mimic the in vivo process may be necessary to replicate the native system.

Affective Value in the Predictive Mind

OpenAIRE

Van de Cruys, Sander

2017-01-01

Although affective value is fundamental in explanations of behavior, it is still a somewhat alien concept in cognitive science. It implies a normativity or directionality that mere information processing models cannot seem to provide. In this paper we trace how affective value can emerge from information processing in the brain, as described by predictive processing. We explain the grounding of predictive processing in homeostasis, and articulate the implications this has for the concept of r...

Measuring solar reflectance - Part I: Defining a metric that accurately predicts solar heat gain

Energy Technology Data Exchange (ETDEWEB)

Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul [Heat Island Group, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States)

2010-09-15

Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective ''cool colored'' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland US latitudes, this metric R{sub E891BN} can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {<=} 5:12 [23 ]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool roof net energy savings by as much as 23%. We define clear sky air mass one global horizontal (''AM1GH'') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer. (author)

Measuring solar reflectance Part I: Defining a metric that accurately predicts solar heat gain

Energy Technology Data Exchange (ETDEWEB)

Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

2010-05-14

Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective 'cool colored' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland U.S. latitudes, this metric RE891BN can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {le} 5:12 [23{sup o}]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool-roof net energy savings by as much as 23%. We define clear-sky air mass one global horizontal ('AM1GH') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer.

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry

DEFF Research Database (Denmark)

Mollerup, Christian Brinch; Mardal, Marie; Dalsgaard, Petur Weihe

2018-01-01

artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model......Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect...

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding.

Science.gov (United States)

Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

2017-01-01

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

A fast EM algorithm for BayesA-like prediction of genomic breeding values.

Directory of Open Access Journals (Sweden)

Xiaochen Sun

Full Text Available Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneously in a linear mixed model. Here we present an EM algorithm (termed "fastBayesA" without MCMC. This fastBayesA approach treats the variances of SNP effects as missing data and uses a joint posterior mode of effects compared to the commonly used BayesA which bases predictions on posterior means of effects. In each EM iteration, SNP effects are predicted as a linear combination of best linear unbiased predictions of breeding values from a mixed linear animal model that incorporates a weighted marker-based realized relationship matrix. Method fastBayesA converges after a few iterations to a joint posterior mode of SNP effects under the BayesA model. When applied to simulated quantitative traits with a range of genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. Method fastBayesA can be used as a computationally efficient substitute for BayesA, especially when an increasing number of markers bring unreasonable computational burden or slow convergence to MCMC approaches.

A Novel Fibrosis Index Comprising a Non-Cholesterol Sterol Accurately Predicts HCV-Related Liver Cirrhosis

DEFF Research Database (Denmark)

Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin

2014-01-01

of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive...

LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

DEFF Research Database (Denmark)

Will, Sebastian; Joshi, Tejal; Hofacker, Ivo L.

2012-01-01

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing...... methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based...... on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used nc...

Do Dual-Route Models Accurately Predict Reading and Spelling Performance in Individuals with Acquired Alexia and Agraphia?

OpenAIRE

Rapcsak, Steven Z.; Henry, Maya L.; Teague, Sommer L.; Carnahan, Susan D.; Beeson, Pélagie M.

2007-01-01

Coltheart and colleagues (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Castles, Bates, & Coltheart, 2006) have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult...

21 CFR 868.1890 - Predictive pulmonary-function value calculator.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Predictive pulmonary-function value calculator... SERVICES (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Diagnostic Devices § 868.1890 Predictive pulmonary-function value calculator. (a) Identification. A predictive pulmonary-function value calculator is...

Towards accurate performance prediction of a vertical axis wind turbine operating at different tip speed ratios

NARCIS (Netherlands)

Rezaeiha, A.; Kalkman, I.; Blocken, B.J.E.

2017-01-01

Accurate prediction of the performance of a vertical-axis wind turbine (VAWT) using CFD simulation requires the employment of a sufficiently fine azimuthal increment (dθ) combined with a mesh size at which essential flow characteristics can be accurately resolved. Furthermore, the domain size needs

Suitability of faecal near-infrared reflectance spectroscopy (NIRS) predictions for estimating gross calorific value

Energy Technology Data Exchange (ETDEWEB)

De la Roza-Delgado, B.; Modroño, S.; Vicente, F.; Martínez-Fernández, A.; Soldado, A.

2015-07-01

A total of 220 faecal pig and poultry samples, collected from different experimental trials were employed with the aim to demonstrate the suitability of Near Infrared Reflectance Spectroscopy (NIRS) technology for estimation of gross calorific value on faeces as output products in energy balances studies. NIR spectra from dried and grounded faeces samples were analyzed using a Foss NIRSystem 6500 instrument, scanning over the wavelength range 400-2500 nm. Validation studies for quantitative analytical models were carried out to estimate the relevance of method performance associated to reference values to obtain an appropriate, accuracy and precision. The results for prediction of gross calorific value (GCV) of NIRS calibrations obtained for individual species showed high correlation coefficients comparing chemical analysis and NIRS predictions, ranged from 0.92 to 0.97 for poultry and pig. For external validation, the ratio between the standard error of cross validation (SECV) and the standard error of prediction (SEP) varied between 0.73 and 0.86 for poultry and pig respectively, indicating a sufficiently precision of calibrations. In addition a global model to estimate GCV in both species was developed and externally validated. It showed correlation coefficients of 0.99 for calibration, 0.98 for cross-validation and 0.97 for external validation. Finally, relative uncertainty was calculated for NIRS developed prediction models with the final value when applying individual NIRS species model of 1.3% and 1.5% for NIRS global prediction. This study suggests that NIRS is a suitable and accurate method for the determination of GCV in faeces, decreasing cost, timeless and for convenient handling of unpleasant samples.. (Author)

Bayesian calibration of power plant models for accurate performance prediction

International Nuclear Information System (INIS)

Boksteen, Sowande Z.; Buijtenen, Jos P. van; Pecnik, Rene; Vecht, Dick van der

2014-01-01

Highlights: • Bayesian calibration is applied to power plant performance prediction. • Measurements from a plant in operation are used for model calibration. • A gas turbine performance model and steam cycle model are calibrated. • An integrated plant model is derived. • Part load efficiency is accurately predicted as a function of ambient conditions. - Abstract: Gas turbine combined cycles are expected to play an increasingly important role in the balancing of supply and demand in future energy markets. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance. In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction. The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. For this purpose, the Bayesian calibration framework of Kennedy and O’Hagan is used, which is especially suitable for high-dimensional industrial problems. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load. The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions

Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women

Science.gov (United States)

Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2015-01-01

Background Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Objectives Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Methods Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007–2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. Results CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman’s r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen’s kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. Conclusions The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements. Citation Nøst TH, Breivik K, Wania F

Accurate prediction of the enthalpies of formation for xanthophylls.

Science.gov (United States)

Lii, Jenn-Huei; Liao, Fu-Xing; Hu, Ching-Han

2011-11-30

This study investigates the applications of computational approaches in the prediction of enthalpies of formation (ΔH(f)) for C-, H-, and O-containing compounds. Molecular mechanics (MM4) molecular mechanics method, density functional theory (DFT) combined with the atomic equivalent (AE) and group equivalent (GE) schemes, and DFT-based correlation corrected atomization (CCAZ) were used. We emphasized on the application to xanthophylls, C-, H-, and O-containing carotenoids which consist of ∼ 100 atoms and extended π-delocaization systems. Within the training set, MM4 predictions are more accurate than those obtained using AE and GE; however a systematic underestimation was observed in the extended systems. ΔH(f) for the training set molecules predicted by CCAZ combined with DFT are in very good agreement with the G3 results. The average absolute deviations (AADs) of CCAZ combined with B3LYP and MPWB1K are 0.38 and 0.53 kcal/mol compared with the G3 data, and are 0.74 and 0.69 kcal/mol compared with the available experimental data, respectively. Consistency of the CCAZ approach for the selected xanthophylls is revealed by the AAD of 2.68 kcal/mol between B3LYP-CCAZ and MPWB1K-CCAZ. Copyright © 2011 Wiley Periodicals, Inc.

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

Directory of Open Access Journals (Sweden)

Yong-Bi Fu

2017-07-01

Full Text Available Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

Science.gov (United States)

Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

2017-01-01

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

Science.gov (United States)

Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

2013-12-23

Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

Efficacy and predictive value of clinical stage in non-surgical patients with esophageal cancer

International Nuclear Information System (INIS)

Liu Xiao; Wang Guiqi; He Shun

2014-01-01

Objective: To investigate the efficacy and predictive value of clinical stage in non-surgical patients with esophageal cancer (EC). Methods: A retrospective study was conducted in 358 EC patients who underwent radical surgery in our hospital from April 2003 to October 2010 and who had preoperative work-up including endoscopic esophageal ultrasound (EUS), esophagoscopy, thoracic CT scans,and contrast esophagography and had detailed information on postoperative pathological stages. The predictive value of preoperative clinical T/N stage based on EUS + CT for postoperative pathological stage was analyzed. The disease free survival (DFS) and overall survival (OS) were analyzed according to the UICC TNM classification (2002/ 2009) and the clinical stage based on imaging findings. Results: The median follow-up was 47 months.A total of 305 (85.2%) of all patients were analyzed by clinical stage based on EUS + CT.Among them, the predictive value of clinical T stage for pathological T stage was 0-88.6%, highest (88.6%) for T1 stage and lowest for T4 stage. The predictive value of clinical N stage (N 0 /N1) was 62.5-100%. The significant differences in OS and DFS rates based on both 2002 and 2009 UICC TNM classifications were noted (P=0.000 and 0.000). There were significant differences in OS between stage groups, except the comparison between two stage Ⅳ patients and other groups, according to 2002 UICC TNM classification. There were usually insignificant differences in OS between stage groups, according to 2009 UICC TNM classification. For the 305 patients staged clinically based on EUS and CT according to 2002 UICC TNM classification, significant differences in OS and DFS rates were noted (P=0.000 and 0.000). Conclusions: Imaging modalities show good predictive value for N stage (N0/N1),even though they cannot accurately provide the number of metastatic lymph nodes. The clinical stage based on EUS + CT can effectively predict the prognosis of non-surgical EC patients

Respiratory variation in peak aortic velocity accurately predicts fluid responsiveness in children undergoing neurosurgery under general anesthesia.

Science.gov (United States)

Morparia, Kavita G; Reddy, Srijaya K; Olivieri, Laura J; Spaeder, Michael C; Schuette, Jennifer J

2018-04-01

The determination of fluid responsiveness in the critically ill child is of vital importance, more so as fluid overload becomes increasingly associated with worse outcomes. Dynamic markers of volume responsiveness have shown some promise in the pediatric population, but more research is needed before they can be adopted for widespread use. Our aim was to investigate effectiveness of respiratory variation in peak aortic velocity and pulse pressure variation to predict fluid responsiveness, and determine their optimal cutoff values. We performed a prospective, observational study at a single tertiary care pediatric center. Twenty-one children with normal cardiorespiratory status undergoing general anesthesia for neurosurgery were enrolled. Respiratory variation in peak aortic velocity (ΔVpeak ao) was measured both before and after volume expansion using a bedside ultrasound device. Pulse pressure variation (PPV) value was obtained from the bedside monitor. All patients received a 10 ml/kg fluid bolus as volume expansion, and were qualified as responders if stroke volume increased >15% as a result. Utility of ΔVpeak ao and PPV and to predict responsiveness to volume expansion was investigated. A baseline ΔVpeak ao value of greater than or equal to 12.3% best predicted a positive response to volume expansion, with a sensitivity of 77%, specificity of 89% and area under receiver operating characteristic curve of 0.90. PPV failed to demonstrate utility in this patient population. Respiratory variation in peak aortic velocity is a promising marker for optimization of perioperative fluid therapy in the pediatric population and can be accurately measured using bedside ultrasonography. More research is needed to evaluate the lack of effectiveness of pulse pressure variation for this purpose.

Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

Directory of Open Access Journals (Sweden)

Aeriel Belk

2018-02-01

Full Text Available Death investigations often include an effort to establish the postmortem interval (PMI in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head, gene markers (16S ribosomal RNA (rRNA, 18S rRNA, internal transcribed spacer regions (ITS, and taxonomic levels (sequence variants, species, genus, etc.. We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI.

Towards Accurate Prediction of Unbalance Response, Oil Whirl and Oil Whip of Flexible Rotors Supported by Hydrodynamic Bearings

Directory of Open Access Journals (Sweden)

Rob Eling

2016-09-01

Full Text Available Journal bearings are used to support rotors in a wide range of applications. In order to ensure reliable operation, accurate analyses of these rotor-bearing systems are crucial. Coupled analysis of the rotor and the journal bearing is essential in the case that the rotor is flexible. The accuracy of prediction of the model at hand depends on its comprehensiveness. In this study, we construct three bearing models of increasing modeling comprehensiveness and use these to predict the response of two different rotor-bearing systems. The main goal is to evaluate the correlation with measurement data as a function of modeling comprehensiveness: 1D versus 2D pressure prediction, distributed versus lumped thermal model, Newtonian versus non-Newtonian fluid description and non-mass-conservative versus mass-conservative cavitation description. We conclude that all three models predict the existence of critical speeds and whirl for both rotor-bearing systems. However, the two more comprehensive models in general show better correlation with measurement data in terms of frequency and amplitude. Furthermore, we conclude that a thermal network model comprising temperature predictions of the bearing surroundings is essential to obtain accurate predictions. The results of this study aid in developing accurate and computationally-efficient models of flexible rotors supported by plain journal bearings.

Examining predictive relationships among consumer values: factors ...

African Journals Online (AJOL)

Examining predictive relationships among consumer values: factors influencing behavioural intentions in retail purchase in Ghana. ... Journal of Business Research ... effects of age and gender differentials on values among retail consumers.

Accurate wavelength prediction of photonic crystal resonant reflection and applications in refractive index measurement

DEFF Research Database (Denmark)

Hermannsson, Pétur Gordon; Vannahme, Christoph; Smith, Cameron L. C.

2014-01-01

and superstrate materials. The importance of accounting for material dispersion in order to obtain accurate simulation results is highlighted, and a method for doing so using an iterative approach is demonstrated. Furthermore, an application for the model is demonstrated, in which the material dispersion......In the past decade, photonic crystal resonant reflectors have been increasingly used as the basis for label-free biochemical assays in lab-on-a-chip applications. In both designing and interpreting experimental results, an accurate model describing the optical behavior of such structures...... is essential. Here, an analytical method for precisely predicting the absolute positions of resonantly reflected wavelengths is presented. The model is experimentally verified to be highly accurate using nanoreplicated, polymer-based photonic crystal grating reflectors with varying grating periods...

Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria

Directory of Open Access Journals (Sweden)

Sato Yoshiharu

2011-11-01

Full Text Available Abstract Background Many pathogens use a type III secretion system to translocate virulence proteins (called effectors in order to adapt to the host environment. To date, many prediction tools for effector identification have been developed. However, these tools are insufficiently accurate for producing a list of putative effectors that can be applied directly for labor-intensive experimental verification. This also suggests that important features of effectors have yet to be fully characterized. Results In this study, we have constructed an accurate approach to predicting secreted virulence effectors from Gram-negative bacteria. This consists of a support vector machine-based discriminant analysis followed by a simple criteria-based filtering. The accuracy was assessed by estimating the average number of true positives in the top-20 ranking in the genome-wide screening. In the validation, 10 sets of 20 training and 20 testing examples were randomly selected from 40 known effectors of Salmonella enterica serovar Typhimurium LT2. On average, the SVM portion of our system predicted 9.7 true positives from 20 testing examples in the top-20 of the prediction. Removal of the N-terminal instability, codon adaptation index and ProtParam indices decreased the score to 7.6, 8.9 and 7.9, respectively. These discrimination features suggested that the following characteristics of effectors had been uncovered: unstable N-terminus, non-optimal codon usage, hydrophilic, and less aliphathic. The secondary filtering process represented by coexpression analysis and domain distribution analysis further refined the average true positive counts to 12.3. We further confirmed that our system can correctly predict known effectors of P. syringae DC3000, strongly indicating its feasibility. Conclusions We have successfully developed an accurate prediction system for screening effectors on a genome-wide scale. We confirmed the accuracy of our system by external validation

Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value

Energy Technology Data Exchange (ETDEWEB)

Callejon-Ferre, A.J.; Lopez-Martinez, J.A.; Manzano-Agugliaro, F. [Departamento de Ingenieria Rural, Universidad de Almeria, Ctra. Sacramento s/n, La Canada de San Urbano, 04120 Almeria (Spain); Velazquez-Marti, B. [Departamento de Ingenieria Rural y Agroalimentaria, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia (Spain)

2011-02-15

Almeria, in southeastern Spain, generates some 1,086,261 t year{sup -1} (fresh weight) of greenhouse crop (Cucurbita pepo L., Cucumis sativus L., Solanum melongena L., Solanum lycopersicum L., Phaseoulus vulgaris L., Capsicum annuum L., Citrillus vulgaris Schrad. and Cucumis melo L.) residues. The energy potential of this biomass is unclear. The aim of the present work was to accurately quantify this variable, differentiating between crop species while taking into consideration the area they each occupy. This, however, required the direct analysis of the higher heating value (HHV) of these residues, involving very expensive and therefore not commonly available equipment. Thus, a further aim was to develop models for predicting the HHV of these residues, taking into account variables measured by elemental and/or proximate analysis, thus providing an economically attractive alternative to direct analysis. All the analyses in this work involved the use of worldwide-recognised standards and methods. The total energy potential for these plant residues, as determined by direct analysis, was 1,003,497.49 MW h year{sup -1}. Twenty univariate and multivariate equations were developed to predict the HHV. The R{sup 2} and adjusted R{sup 2} values obtained for the univariate and multivariate models were 0.909 and 0.946 or above respectively. In all cases, the mean absolute percentage error varied between 0.344 and 2.533. These results show that any of these 20 equations could be used to accurately predict the HHV of crop residues. The residues produced by the Almeria greenhouse industry would appear to be an interesting source of renewable energy. (author)

Prediction of cereal feed value using spectroscopy and chemometrics

DEFF Research Database (Denmark)

Jørgensen, Johannes Ravn; Gislum, René

2009-01-01

of EDOM, EDOMi, FEso and FEsv. The outcome of a successful NIRS calibration will be a relatively cheap tool to monitor, diversify and evaluate the quality of cereals for animal feed, a possible tool to assess the feed value of new varieties in the variety testing and a useful, cheap and rapid tool...... for cereal breeders. A collection of 1213 grain samples of wheat, triticale, barley and rye, and related chemical reference analyses to describe the feed value have been established. The samples originate from available field trials over a three-year period. The chemical reference analyses are dry matter...... value, the prediction error has to be compared with the error in the chemical analysis. Prediction error by NIRS prediction of feed value is above the error of the chemical measurement. The conclusion is that it is possible to predict the feed value in cereals with NIRS quickly and cheaply...

Accurate anisotropic material modelling using only tensile tests for hot and cold forming

Science.gov (United States)

Abspoel, M.; Scholting, M. E.; Lansbergen, M.; Neelis, B. M.

2017-09-01

Accurate material data for simulations require a lot of effort. Advanced yield loci require many different kinds of tests and a Forming Limit Curve (FLC) needs a large amount of samples. Many people use simple material models to reduce the effort of testing, however some models are either not accurate enough (i.e. Hill’48), or do not describe new types of materials (i.e. Keeler). Advanced yield loci describe the anisotropic materials behaviour accurately, but are not widely adopted because of the specialized tests, and data post-processing is a hurdle for many. To overcome these issues, correlations between the advanced yield locus points (biaxial, plane strain and shear) and mechanical properties have been investigated. This resulted in accurate prediction of the advanced stress points using only Rm, Ag and r-values in three directions from which a Vegter yield locus can be constructed with low effort. FLC’s can be predicted with the equations of Abspoel & Scholting depending on total elongation A80, r-value and thickness. Both predictive methods are initially developed for steel, aluminium and stainless steel (BCC and FCC materials). The validity of the predicted Vegter yield locus is investigated with simulation and measurements on both hot and cold formed parts and compared with Hill’48. An adapted specimen geometry, to ensure a homogeneous temperature distribution in the Gleeble hot tensile test, was used to measure the mechanical properties needed to predict a hot Vegter yield locus. Since for hot material, testing of stress states other than uniaxial is really challenging, the prediction for the yield locus adds a lot of value. For the hot FLC an A80 sample with a homogeneous temperature distribution is needed which is due to size limitations not possible in the Gleeble tensile tester. Heating the sample in an industrial type furnace and tensile testing it in a dedicated device is a good alternative to determine the necessary parameters for the FLC

Value of a new multiparametric score for prediction of microvascular obstruction lesions in ST-segment elevation myocardial infarction revascularized by percutaneous coronary intervention.

Science.gov (United States)

Amabile, Nicolas; Jacquier, Alexis; Gaudart, Jean; Sarran, Anthony; Shuaib, Anes; Panuel, Michel; Moulin, Guy; Bartoli, Jean-Michel; Paganelli, Franck

2010-10-01

Despite improvement in revascularization strategies, microvascular obstruction (MO) lesions remain associated with poor outcome after ST-segment elevation myocardial infarction (STEMI). To establish a bedside-available score for predicting MO lesions in STEMI, with cardiac magnetic resonance imaging (CMR) as the reference standard, and to test its prognostic value for clinical outcome. Patients with STEMI of4 accurately identified microcirculatory injuries (sensitivity 84%; specificity 82%) and independently predicted the presence of MO lesions on CMR. MO score>4 predicted adverse cardiovascular events during the first year after STEMI (relative risk 2.60 [1.10-6.60], p=0.03). MO lesions are frequent in PCI-treated STEMI and are associated with larger MIs. MO score accurately predicted MO lesions and identified patients with poor outcome post-STEMI. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Prediction of cereal feed value by near infrared spectroscopy

DEFF Research Database (Denmark)

Jørgensen, Johannes Ravn

. NIRS is therefore appropriate as a quick method for the determination of FEsv and FEso, since it is rapid (approximately 1 minute per measurement of a ground test) and cheap. The aim is to develop a rapid method to analyse grain feed value. This will contribute to highlight the opportunities...... feed, a possible tool to assess the feed value of new varieties in the variety testing and a useful, cheap and rapid tool for cereal breeders. A bank of 1213 grain samples of wheat, triticale, barley and rye, and related chemical reference analyses to describe the feed value have been established...... with the error in the chemical analysis. Prediction error by NIRS prediction of feed value has been shown to be above the error of the chemical measurement. The conclusion is that it has proved possible to predict the feed value in cereals with NIRS quickly and cheaply, but prediction error with this method...

Accurate Gas Phase Formation Enthalpies of Alloys and Refractories Decomposition Products

KAUST Repository

Minenkov, Yury

2017-01-17

Accurate gas phase formation enthalpies, ΔHf, of metal oxides and halides are critical for the prediction of the stability of high temperature materials used in the aerospace and nuclear industries. Unfortunately, the experimental ΔHf values of these compounds in the most used databases, such as the NIST-JANAF database, are often reported with large inaccuracy, while some other ΔHf values clearly differ from the value predicted by CCSD(T) methods. To address this point, in this work we systematically predicted the ΔHf values of a series of these compounds having a group 4, 6, or 14 metal. The ΔHf values in question were derived within a composite Feller-Dixon-Peterson (FDP) scheme based protocol that combines the DLPNO-CCSD(T) enthalpy of ad hoc designed reactions and the experimental ΔHf values of few reference complexes. In agreement with other theoretical studies, we predict the ΔHf values for TiOCl2, TiOF2, GeF2, and SnF4 to be significantly different from the values tabulated in NIST-JANAF and other sources, which suggests that the tabulated experimental values are inaccurate. Similarly, the predicted ΔHf values for HfCl2, HfBr2, HfI2, MoOF4, MoCl6, WOF4, WOCl4, GeO2, SnO2, PbBr4, PbI4, and PbO2 also clearly differ from the tabulated experimental values, again suggesting large inaccuracy in the experimental values. In the case when largely different experimental values are available, we point to the value that is in better agreement with our results. We expect the ΔHf values reported in this work to be quite accurate, and thus, they might be used in thermodynamic calculations, because the effects from core correlation, relativistic effects, and basis set incompleteness were included in the DLPNO-CCSD(T) calculations. T1 and T2 values were thoroughly monitored as indicators of the quality of the reference Hartree-Fock orbitals (T1) and potential multireference character of the systems (T2).

An application of a relational database system for high-throughput prediction of elemental compositions from accurate mass values.

Science.gov (United States)

Sakurai, Nozomu; Ara, Takeshi; Kanaya, Shigehiko; Nakamura, Yukiko; Iijima, Yoko; Enomoto, Mitsuo; Motegi, Takeshi; Aoki, Koh; Suzuki, Hideyuki; Shibata, Daisuke

2013-01-15

High-accuracy mass values detected by high-resolution mass spectrometry analysis enable prediction of elemental compositions, and thus are used for metabolite annotations in metabolomic studies. Here, we report an application of a relational database to significantly improve the rate of elemental composition predictions. By searching a database of pre-calculated elemental compositions with fixed kinds and numbers of atoms, the approach eliminates redundant evaluations of the same formula that occur in repeated calculations with other tools. When our approach is compared with HR2, which is one of the fastest tools available, our database search times were at least 109 times shorter than those of HR2. When a solid-state drive (SSD) was applied, the search time was 488 times shorter at 5 ppm mass tolerance and 1833 times at 0.1 ppm. Even if the search by HR2 was performed with 8 threads in a high-spec Windows 7 PC, the database search times were at least 26 and 115 times shorter without and with the SSD. These improvements were enhanced in a low spec Windows XP PC. We constructed a web service 'MFSearcher' to query the database in a RESTful manner. Available for free at http://webs2.kazusa.or.jp/mfsearcher. The web service is implemented in Java, MySQL, Apache and Tomcat, with all major browsers supported. sakurai@kazusa.or.jp Supplementary data are available at Bioinformatics online.

ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

Energy Technology Data Exchange (ETDEWEB)

Visel, Axel; Blow, Matthew J.; Li, Zirong; Zhang, Tao; Akiyama, Jennifer A.; Holt, Amy; Plajzer-Frick, Ingrid; Shoukry, Malak; Wright, Crystal; Chen, Feng; Afzal, Veena; Ren, Bing; Rubin, Edward M.; Pennacchio, Len A.

2009-02-01

A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. We tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.

The prognostic and predictive value of sstr_2-immunohistochemistry and sstr_2-targeted imaging in neuroendocrine tumors

International Nuclear Information System (INIS)

Brunner, Philippe; Joerg, Ann-Catherine; Mueller-Brand, Jan; Glatz, Katharina; Bubendorf, Lukas; Radojewski, Piotr; Umlauft, Maria; Spanjol, Petar-Marko; Krause, Thomas; Dumont, Rebecca A.; Walter, Martin A.; Marincek, Nicolas; Maecke, Helmut R.; Briel, Matthias; Schmitt, Anja; Perren, Aurel

2017-01-01

Our aim was to assess the prognostic and predictive value of somatostatin receptor 2 (sstr_2) in neuroendocrine tumors (NETs). We established a tissue microarray and imaging database from NET patients that received sstr_2-targeted radiopeptide therapy with yttrium-90-DOTATOC, lutetium-177-DOTATOC or alternative treatment. We used univariate and multivariate analyses to identify prognostic and predictive markers for overall survival, including sstr_2-imaging and sstr_2-immunohistochemistry. We included a total of 279 patients. In these patients, sstr_2-immunohistochemistry was an independent prognostic marker for overall survival (HR: 0.82, 95 % CI: 0.67 - 0.99, n = 279, p = 0.037). In DOTATOC patients, sstr_2-expression on immunohistochemistry correlated with tumor uptake on sstr_2-imaging (n = 170, p < 0.001); however, sstr_2-imaging showed a higher prognostic accuracy (positive predictive value: +27 %, 95 % CI: 3 - 56 %, p = 0.025). Sstr_2-expression did not predict a benefit of DOTATOC over alternative treatment (p = 0.93). Our results suggest sstr_2 as an independent prognostic marker in NETs. Sstr_2-immunohistochemistry correlates with sstr_2-imaging; however, sstr_2-imaging is more accurate for determining the individual prognosis. (orig.)

Predictive value of c-reactive protein for thrombolytic therapy in acute myocardial infarction

International Nuclear Information System (INIS)

Majeed, N.; Bashir, F.

2014-01-01

The serum levels of C-reactive protein on admission may predict the efficacy of reperfusion in patients with acute myocardial infarction. Objectives: This study was conducted to know the predictive value of CRP for success of thrombolysis and to know the prognostic value of C-reactive protein in patients having acute myocardial infarction. Study Design: It was single center, open labeled cross sectional study. Materials and Methods: Sixty patients of acute myocardial infarction diagnosed on clinical and ECG criteria, who received thrombolytic therapy with strepto- kinase, were included in the study. The diagnosis of acute rnyocardial infarction was made on clinical para meters and ECG criteria. The ECG changes were noted before starting thrombolysis. The baseline sample for C-reactive protein (CRP,) was taken before starting thrombolysis. The time duration between onset of symptoms and start of thrombolysis was also noted. The thrombolysis was done with streptokinase infusion, 1.5 million units diluted in 100ml normal saline, intravenously over one hour. The ECG was repeated after six hours of completion of thrombolysis and, changes were noted and compared with ECG changes before thrombolysis. Now second sample for C-reactive protein (CRP2) was taken after six hours of completion of thrombolysis. CRP was measured by a high sensitivity assay which can accurately measure basal levels of CRP throughout the currently accepted cardiovascular risk assessment range (0.20 - 10.0 mg/L). According to ECG findings after thrombolysis, all patients were divided into two groups. Group A was considered as successful group to thrombolysis, in whom ECG changes were settled. Group B was considered as unsuccessful group to thrombolysis, in whom ECG changes remained same as before thrombolysis. Both values of C-reactive protein, CRP, and CRP2 were compared in both groups group A and group B. Results: Plasma CRP values before and after thrombolysis had strong predictive value for

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

International Nuclear Information System (INIS)

Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; Lilienfeld, O. Anatole von; Müller, Klaus-Robert; Tkatchenko, Alexandre

2015-01-01

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the 'holy grail' of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies

Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations?

Science.gov (United States)

Bianco, Nicholas A; Patten, Carolynn; Fregly, Benjamin J

2018-01-01

Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called "muscle excitations"), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called "included" muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called "excluded" muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called "synergy extrapolation"). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

Directory of Open Access Journals (Sweden)

Zhiheng Wang

Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

[Evaluation of thermal comfort in a student population: predictive value of an integrated index (Fanger's predicted mean value].

Science.gov (United States)

Catenacci, G; Terzi, R; Marcaletti, G; Tringali, S

1989-01-01

Practical applications and predictive values of a thermal comfort index (Fanger's PRV) were verified on a sample school population (1236 subjects) by studying the relationships between thermal sensations (subjective analysis), determined by means of an individual questionnaire, and the values of thermal comfort index (objective analysis) obtained by calculating the PMV index individually in the subjects under study. In homogeneous conditions of metabolic expenditure rate and thermal impedence from clothing, significant differences were found between the two kinds of analyses. At 22 degrees C mean radiant and operative temperature, the PMV values averaged 0 and the percentage of subjects who experienced thermal comfort did not exceed 60%. The high level of subjects who were dissatisfied with their environmental thermal conditions confirms the doubts regarding the use of the PMV index as a predictive indicator of thermal comfort, especially considering that the negative answers were not homogeneous nor attributable to the small thermal fluctuations (less than 0.5 degree C) measured in the classrooms.

Hounsfield unit density accurately predicts ESWL success.

Science.gov (United States)

Magnuson, William J; Tomera, Kevin M; Lance, Raymond S

2005-01-01

Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p ESWL for upper tract calculi between 5-15mm.

Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

Directory of Open Access Journals (Sweden)

Cecilia Noecker

2015-03-01

Full Text Available Upon infection of a new host, human immunodeficiency virus (HIV replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV. First, we found that the mode of virus production by infected cells (budding vs. bursting has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral

Prediction of pK_a values for druglike molecules using semiempirical quantum chemical methods

DEFF Research Database (Denmark)

Jensen, Jan Halborg; Swain, Christopher J; Olsen, Lars

2017-01-01

Rapid yet accurate pKa prediction for druglike molecules is a key challenge in computational chemistry. This study uses PM6-DH+/COSMO, PM6/COSMO, PM7/COSMO, PM3/COSMO, AM1/COSMO, PM3/SMD, AM1/SMD, and DFTB3/SMD to predict the pKa values of 53 amine groups in 48 druglike compounds. The approach uses...... uncertainties of ±0.2-0.3 pH units, which make them statistically equivalent. However, for all but PM3/SMD and AM1/SMD the RMSEs are dominated by a single outlier, cefadroxil, caused by proton transfer in the zwitterionic protonation state. If this outlier is removed, the RMSE values for PM3/COSMO and AM1/COSMO...... drop to 1.0 ± 0.2 and 1.1 ± 0.3, whereas PM3/SMD and AM1/SMD remain at 1.5 ± 0.3 and 1.6 ± 0.3/0.4 pH units, making the COSMO-based predictions statistically better than the SMD-based predictions. For pKa calculations where a zwitterionic state is not involved or proton transfer in a zwitterionic state...

Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry.

Science.gov (United States)

Mollerup, Christian Brinch; Mardal, Marie; Dalsgaard, Petur Weihe; Linnet, Kristian; Barron, Leon Patrick

2018-03-23

Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect and non-targeted screening. These allow for tentative identification of new compounds, and in-silico predicted reference values are used for improving confidence and filtering false-positive identifications. In this work, predictions of both RT and CCS values are performed with machine learning using artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model was investigated for the first time. The optimized combined RT-CCS model was a four-layered multi-layer perceptron ANN, and the 95th prediction error percentiles were within 2 min RT error and 5% relative CCS error for the external validation set (n = 36) and the full RT-CCS dataset (n = 357). 88.6% (n = 733) of predicted RTs were within 2 min error for the full dataset. Overall, when using 2 min RT error and 5% relative CCS error, 91.9% (n = 328) of compounds were retained, while 99.4% (n = 355) were retained when using at least one of these thresholds. This combined prediction approach can therefore be useful for rapid suspect/non-targeted screening involving HRMS, and will support current workflows. Copyright © 2018 Elsevier B.V. All rights reserved.

The determination of the pressure-viscosity coefficient of a lubricant through an accurate film thickness formula and accurate film thickness measurements : part 2 : high L values

NARCIS (Netherlands)

Leeuwen, van H.J.

2011-01-01

The pressure-viscosity coefficient of a traction fluid is determined by fitting calculation results on accurate film thickness measurements, obtained at different speeds, loads, and temperatures. Through experiments, covering a range of 5.6 values are

Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

Science.gov (United States)

Kim, Scott Y H

2014-04-01

The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

The MIDAS touch for Accurately Predicting the Stress-Strain Behavior of Tantalum

Energy Technology Data Exchange (ETDEWEB)

Jorgensen, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-03-02

Testing the behavior of metals in extreme environments is not always feasible, so material scientists use models to try and predict the behavior. To achieve accurate results it is necessary to use the appropriate model and material-specific parameters. This research evaluated the performance of six material models available in the MIDAS database [1] to determine at which temperatures and strain-rates they perform best, and to determine to which experimental data their parameters were optimized. Additionally, parameters were optimized for the Johnson-Cook model using experimental data from Lassila et al [2].

Predictive value of stroke discharge diagnoses in the Danish National Patient Register.

Science.gov (United States)

Lühdorf, Pernille; Overvad, Kim; Schmidt, Erik B; Johnsen, Søren P; Bach, Flemming W

2017-08-01

To determine the positive predictive values for stroke discharge diagnoses, including subarachnoidal haemorrhage, intracerebral haemorrhage and cerebral infarction in the Danish National Patient Register. Participants in the Danish cohort study Diet, Cancer and Health with a stroke discharge diagnosis in the National Patient Register between 1993 and 2009 were identified and their medical records were retrieved for validation of the diagnoses. A total of 3326 records of possible cases of stroke were reviewed. The overall positive predictive value for stroke was 69.3% (95% confidence interval (CI) 67.8-70.9%). The predictive values differed according to hospital characteristics, with the highest predictive value of 87.8% (95% CI 85.5-90.1%) found in departments of neurology and the lowest predictive value of 43.0% (95% CI 37.6-48.5%) found in outpatient clinics. The overall stroke diagnosis in the Danish National Patient Register had a limited predictive value. We therefore recommend the critical use of non-validated register data for research on stroke. The possibility of optimising the predictive values based on more advanced algorithms should be considered.

The Economic Value of Predicting Bond Risk Premia

DEFF Research Database (Denmark)

Sarno, Lucio; Schneider, Paul; Wagner, Christian

2016-01-01

evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information...... otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast...

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

Predictive Values of Electroencephalography (EEG) in Epilepsy ...

African Journals Online (AJOL)

Predictive Values of Electroencephalography (EEG) in Epilepsy Patients with Abnormal Behavioural Symptoms. OR Obiako, SO Adeyemi, TL Sheikh, LF Owolabi, MA Majebi, MO Gomina, F Adebayo, EU Iwuozo ...

Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia?

Science.gov (United States)

Rapcsak, Steven Z; Henry, Maya L; Teague, Sommer L; Carnahan, Susan D; Beeson, Pélagie M

2007-06-18

Coltheart and co-workers [Castles, A., Bates, T. C., & Coltheart, M. (2006). John Marshall and the developmental dyslexias. Aphasiology, 20, 871-892; Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256] have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper, we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing.

The Economic Value of Predicting Bond Risk Premia

DEFF Research Database (Denmark)

Sarno, Lucio; Schneider, Paul; Wagner, Christian

the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...

ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

Science.gov (United States)

Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

2016-05-01

At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

A Fisher’s Criterion-Based Linear Discriminant Analysis for Predicting the Critical Values of Coal and Gas Outbursts Using the Initial Gas Flow in a Borehole

Directory of Open Access Journals (Sweden)

Xiaowei Li

2017-01-01

Full Text Available The risk of coal and gas outbursts can be predicted using a method that is linear and continuous and based on the initial gas flow in the borehole (IGFB; this method is significantly superior to the traditional point prediction method. Acquiring accurate critical values is the key to ensuring accurate predictions. Based on ideal rock cross-cut coal uncovering model, the IGFB measurement device was developed. The present study measured the data of the initial gas flow over 3 min in a 1 m long borehole with a diameter of 42 mm in the laboratory. A total of 48 sets of data were obtained. These data were fuzzy and chaotic. Fisher’s discrimination method was able to transform these spatial data, which were multidimensional due to the factors influencing the IGFB, into a one-dimensional function and determine its critical value. Then, by processing the data into a normal distribution, the critical values of the outbursts were analyzed using linear discriminant analysis with Fisher’s criterion. The weak and strong outbursts had critical values of 36.63 L and 80.85 L, respectively, and the accuracy of the back-discriminant analysis for the weak and strong outbursts was 94.74% and 92.86%, respectively. Eight outburst tests were simulated in the laboratory, the reverse verification accuracy was 100%, and the accuracy of the critical value was verified.

Predictive value of pulse pressure variation for fluid responsiveness in septic patients using lung-protective ventilation strategies.

Science.gov (United States)

Freitas, F G R; Bafi, A T; Nascente, A P M; Assunção, M; Mazza, B; Azevedo, L C P; Machado, F R

2013-03-01

The applicability of pulse pressure variation (ΔPP) to predict fluid responsiveness using lung-protective ventilation strategies is uncertain in clinical practice. We designed this study to evaluate the accuracy of this parameter in predicting the fluid responsiveness of septic patients ventilated with low tidal volumes (TV) (6 ml kg(-1)). Forty patients after the resuscitation phase of severe sepsis and septic shock who were mechanically ventilated with 6 ml kg(-1) were included. The ΔPP was obtained automatically at baseline and after a standardized fluid challenge (7 ml kg(-1)). Patients whose cardiac output increased by more than 15% were considered fluid responders. The predictive values of ΔPP and static variables [right atrial pressure (RAP) and pulmonary artery occlusion pressure (PAOP)] were evaluated through a receiver operating characteristic (ROC) curve analysis. Thirty-four patients had characteristics consistent with acute lung injury or acute respiratory distress syndrome and were ventilated with high levels of PEEP [median (inter-quartile range) 10.0 (10.0-13.5)]. Nineteen patients were considered fluid responders. The RAP and PAOP significantly increased, and ΔPP significantly decreased after volume expansion. The ΔPP performance [ROC curve area: 0.91 (0.82-1.0)] was better than that of the RAP [ROC curve area: 0.73 (0.59-0.90)] and pulmonary artery occlusion pressure [ROC curve area: 0.58 (0.40-0.76)]. The ROC curve analysis revealed that the best cut-off for ΔPP was 6.5%, with a sensitivity of 0.89, specificity of 0.90, positive predictive value of 0.89, and negative predictive value of 0.90. Automatized ΔPP accurately predicted fluid responsiveness in septic patients ventilated with low TV.

Predicting Customer Lifetime Value in Multi-Service Industries

NARCIS (Netherlands)

A.C.D. Donkers (Bas); P.C. Verhoef (Peter); M.G. de Jong (Martijn)

2003-01-01

textabstractCustomer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in

A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits

Directory of Open Access Journals (Sweden)

Hayashi Takeshi

2013-01-01

Full Text Available Abstract Background Genomic selection is an effective tool for animal and plant breeding, allowing effective individual selection without phenotypic records through the prediction of genomic breeding value (GBV. To date, genomic selection has focused on a single trait. However, actual breeding often targets multiple correlated traits, and, therefore, joint analysis taking into consideration the correlation between traits, which might result in more accurate GBV prediction than analyzing each trait separately, is suitable for multi-trait genomic selection. This would require an extension of the prediction model for single-trait GBV to multi-trait case. As the computational burden of multi-trait analysis is even higher than that of single-trait analysis, an effective computational method for constructing a multi-trait prediction model is also needed. Results We described a Bayesian regression model incorporating variable selection for jointly predicting GBVs of multiple traits and devised both an MCMC iteration and variational approximation for Bayesian estimation of parameters in this multi-trait model. The proposed Bayesian procedures with MCMC iteration and variational approximation were referred to as MCBayes and varBayes, respectively. Using simulated datasets of SNP genotypes and phenotypes for three traits with high and low heritabilities, we compared the accuracy in predicting GBVs between multi-trait and single-trait analyses as well as between MCBayes and varBayes. The results showed that, compared to single-trait analysis, multi-trait analysis enabled much more accurate GBV prediction for low-heritability traits correlated with high-heritability traits, by utilizing the correlation structure between traits, while the prediction accuracy for uncorrelated low-heritability traits was comparable or less with multi-trait analysis in comparison with single-trait analysis depending on the setting for prior probability that a SNP has zero

Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone.

Science.gov (United States)

Tailly, Thomas; Larish, Yaniv; Nadeau, Brandon; Violette, Philippe; Glickman, Leonard; Olvera-Posada, Daniel; Alenezi, Husain; Amann, Justin; Denstedt, John; Razvi, Hassan

2016-04-01

The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.

Accurate Evaluation of Quantum Integrals

Science.gov (United States)

Galant, D. C.; Goorvitch, D.; Witteborn, Fred C. (Technical Monitor)

1995-01-01

Combining an appropriate finite difference method with Richardson's extrapolation results in a simple, highly accurate numerical method for solving a Schrodinger's equation. Important results are that error estimates are provided, and that one can extrapolate expectation values rather than the wavefunctions to obtain highly accurate expectation values. We discuss the eigenvalues, the error growth in repeated Richardson's extrapolation, and show that the expectation values calculated on a crude mesh can be extrapolated to obtain expectation values of high accuracy.

Genomic value prediction for quantitative traits under the epistatic model

Directory of Open Access Journals (Sweden)

Xu Shizhong

2011-01-01

Full Text Available Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL. The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects and marker pairs (epistatic effects to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive effects were used for prediction. When the interaction (epistatic effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding.

Accurate diffraction data integration by the EVAL15 profile prediction method : Application in chemical and biological crystallography

NARCIS (Netherlands)

Xian, X.

2009-01-01

Accurate integration of reflection intensities plays an essential role in structure determination of the crystallized compound. A new diffraction data integration method, EVAL15, is presented in this thesis. This method uses the principle of general impacts to predict ab inito three-dimensional

Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

Directory of Open Access Journals (Sweden)

Niklas Berliner

Full Text Available Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

What Is the Predictive Value of Animal Models for Vaccine Efficacy in Humans? Consideration of Strategies to Improve the Value of Animal Models.

Science.gov (United States)

Herati, Ramin Sedaghat; Wherry, E John

2018-04-02

Animal models are an essential feature of the vaccine design toolkit. Although animal models have been invaluable in delineating the mechanisms of immune function, their precision in predicting how well specific vaccines work in humans is often suboptimal. There are, of course, many obvious species differences that may limit animal models from predicting all details of how a vaccine works in humans. However, careful consideration of which animal models may have limitations should also allow more accurate interpretations of animal model data and more accurate predictions of what is to be expected in clinical trials. In this article, we examine some of the considerations that might be relevant to cross-species extrapolation of vaccine-related immune responses for the prediction of how vaccines will perform in humans. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments.

Science.gov (United States)

Heskes, Tom; Eisinga, Rob; Breitling, Rainer

2014-11-21

The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution. We derive strict lower and upper bounds to the exact p-value along with an accurate approximation that can be used to assess the significance of the rank product statistic in a computationally fast manner. The bounds and the proposed approximation are shown to provide far better accuracy over existing approximate methods in determining tail probabilities, with the slightly conservative upper bound protecting against false positives. We illustrate the proposed method in the context of a recently published analysis on transcriptomic profiling performed in blood. We provide a method to determine upper bounds and accurate approximate p-values of the rank product statistic. The proposed algorithm provides an order of magnitude increase in throughput as compared with current approaches and offers the opportunity to explore new application domains with even larger multiple testing issue. The R code is published in one of the Additional files and is available at http://www.ru.nl/publish/pages/726696/rankprodbounds.zip .

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

Directory of Open Access Journals (Sweden)

Young Bin Kim

Full Text Available In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs. Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

Science.gov (United States)

Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

2015-01-01

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it. PMID:26241496

The prognostic and predictive value of sstr{sub 2}-immunohistochemistry and sstr{sub 2}-targeted imaging in neuroendocrine tumors

Energy Technology Data Exchange (ETDEWEB)

Brunner, Philippe [University Hospital Basel, Institute of Pathology (Switzerland); University Hospital Basel, Institute of Nuclear Medicine (Switzerland); Joerg, Ann-Catherine; Mueller-Brand, Jan [University Hospital Basel, Institute of Nuclear Medicine (Switzerland); Glatz, Katharina; Bubendorf, Lukas [University Hospital Basel, Institute of Pathology (Switzerland); Radojewski, Piotr; Umlauft, Maria; Spanjol, Petar-Marko; Krause, Thomas; Dumont, Rebecca A.; Walter, Martin A. [University Hospital Bern, Institute of Nuclear Medicine (Switzerland); Marincek, Nicolas [University Hospital Basel, Institute of Nuclear Medicine (Switzerland); University Hospital Bern, Institute of Nuclear Medicine (Switzerland); Maecke, Helmut R. [University Hospital Basel, Division of Radiological Chemistry (Switzerland); Briel, Matthias [University Hospital Basel, Basel Institute for Clinical Epidemiology and Biostatistics (Switzerland); McMaster University, Department of Clinical Epidemiology and Biostatistics, Hamilton (Canada); Schmitt, Anja; Perren, Aurel [University Bern, Institute of Pathology, Bern (Switzerland)

2017-03-15

Our aim was to assess the prognostic and predictive value of somatostatin receptor 2 (sstr{sub 2}) in neuroendocrine tumors (NETs). We established a tissue microarray and imaging database from NET patients that received sstr{sub 2}-targeted radiopeptide therapy with yttrium-90-DOTATOC, lutetium-177-DOTATOC or alternative treatment. We used univariate and multivariate analyses to identify prognostic and predictive markers for overall survival, including sstr{sub 2}-imaging and sstr{sub 2}-immunohistochemistry. We included a total of 279 patients. In these patients, sstr{sub 2}-immunohistochemistry was an independent prognostic marker for overall survival (HR: 0.82, 95 % CI: 0.67 - 0.99, n = 279, p = 0.037). In DOTATOC patients, sstr{sub 2}-expression on immunohistochemistry correlated with tumor uptake on sstr{sub 2}-imaging (n = 170, p < 0.001); however, sstr{sub 2}-imaging showed a higher prognostic accuracy (positive predictive value: +27 %, 95 % CI: 3 - 56 %, p = 0.025). Sstr{sub 2}-expression did not predict a benefit of DOTATOC over alternative treatment (p = 0.93). Our results suggest sstr{sub 2} as an independent prognostic marker in NETs. Sstr{sub 2}-immunohistochemistry correlates with sstr{sub 2}-imaging; however, sstr{sub 2}-imaging is more accurate for determining the individual prognosis. (orig.)

The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making.

Science.gov (United States)

Canullo, Luigi; Radovanović, Sandro; Delibasic, Boris; Blaya, Juan Antonio; Penarrocha, David; Rakic, Mia

2017-05-01

The primary aim of this study was to evaluate 23 pathogens associated with peri-implantitis at inner part of implant connections, in peri-implant and periodontal pockets between patients suffering peri-implantitis and participants with healthy peri-implant tissues; the secondary aim was to estimate the predictive value of microbiological profile in patients wearing dental implants using data mining methods. Fifty participants included in the present case─control study were scheduled for collection of plaque samples from the peri-implant pockets, internal connection, and periodontal pocket. Real-time polymerase chain reaction was performed to quantify 23 pathogens. Three predictive models were developed using C4.5 decision trees to estimate the predictive value of microbiological profile between three experimental sites. The final sample included 47 patients (22 healthy controls and 25 diseased cases), 90 implants (43 with healthy peri-implant tissues and 47 affected by peri-implantitis). Total and mean pathogen counts at inner portions of the implant connection, in peri-implant and periodontal pockets were generally increased in peri-implantitis patients when compared to healthy controls. The inner portion of the implant connection, the periodontal pocket and peri-implant pocket, respectively, presented a predictive value of microbiologic profile of 82.78%, 94.31%, and 97.5% of accuracy. This study showed that microbiological profile at all three experimental sites is differently characterized between patients suffering peri-implantitis and healthy controls. Data mining analysis identified Parvimonas micra as a highly accurate predictor of peri-implantitis when present in peri-implant pocket while this method generally seems to be promising for diagnosis of such complex infections. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Anatomically accurate, finite model eye for optical modeling.

Science.gov (United States)

Liou, H L; Brennan, N A

1997-08-01

There is a need for a schematic eye that models vision accurately under various conditions such as refractive surgical procedures, contact lens and spectacle wear, and near vision. Here we propose a new model eye close to anatomical, biometric, and optical realities. This is a finite model with four aspheric refracting surfaces and a gradient-index lens. It has an equivalent power of 60.35 D and an axial length of 23.95 mm. The new model eye provides spherical aberration values within the limits of empirical results and predicts chromatic aberration for wavelengths between 380 and 750 nm. It provides a model for calculating optical transfer functions and predicting optical performance of the eye.

Predictive value and construct validity of the work functioning screener-healthcare (WFS-H).

Science.gov (United States)

Boezeman, Edwin J; Nieuwenhuijsen, Karen; Sluiter, Judith K

2016-05-25

To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (ppredictive value and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers.

Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

Science.gov (United States)

Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

2017-09-01

Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

An accurate model for numerical prediction of piezoelectric energy harvesting from fluid structure interaction problems

International Nuclear Information System (INIS)

Amini, Y; Emdad, H; Farid, M

2014-01-01

Piezoelectric energy harvesting (PEH) from ambient energy sources, particularly vibrations, has attracted considerable interest throughout the last decade. Since fluid flow has a high energy density, it is one of the best candidates for PEH. Indeed, a piezoelectric energy harvesting process from the fluid flow takes the form of natural three-way coupling of the turbulent fluid flow, the electromechanical effect of the piezoelectric material and the electrical circuit. There are some experimental and numerical studies about piezoelectric energy harvesting from fluid flow in literatures. Nevertheless, accurate modeling for predicting characteristics of this three-way coupling has not yet been developed. In the present study, accurate modeling for this triple coupling is developed and validated by experimental results. A new code based on this modeling in an openFOAM platform is developed. (paper)

Diagnostic value of newborn foot length to predict gestational age

Directory of Open Access Journals (Sweden)

Mutia Farah Fawziah

2017-08-01

Full Text Available Background  Identification of gestational age, especially within 48 hours of birth, is crucial for newborns, as the earlier preterm status is detected, the earlier the child can receive optimal management. Newborn foot length is an anthropometric measurement which is easy to perform, inexpensive, and potentially efficient for predicting gestational age. Objective  To analyze the diagnostic value of newborn foot length in predicting gestational age. Methods  This diagnostic study was performed between October 2016 and February 2017 in the High Care Unit of Neonates at Dr. Moewardi General Hospital, Surakarta. A total of 152 newborns were consecutively selected and underwent right foot length measurements before 96 hours of age. The correlation between newborn foot length to classify as full term and gestational age was analyzed with Spearman’s correlation test because of non-normal data distribution. The cut-off point of newborn foot length was calculated by receiver operating characteristic (ROC curve and diagnostic values of newborn foot length were analyzed by 2 x 2 table with SPSS 21.0 software. Results There were no significant differences between male and female newborns in terms of gestational age, birth weight, choronological age, and newborn foot length (P>0.05. Newborn foot length and gestational age had a significant correlation (r=0.53; P=0.000. The optimal cut-off newborn foot length to predict full term status was 7.1 cm. Newborn foot length below 7.1 cm had sensitivity 75%, specificity 98%, positive predictive value 94.3%, negative predictive value 90.6%, positive likelihood ratio 40.5, negative likelihood ratio 0.25, and post-test probability 94.29%, to predict preterm status in newborns. Conclusion  Newborn foot length can be used to predict gestational age, especially for the purpose of differentiating between preterm and full term newborns.

Predictive value and construct validity of the work functioning screener-healthcare (WFS-H)

Science.gov (United States)

Boezeman, Edwin J.; Nieuwenhuijsen, Karen; Sluiter, Judith K.

2016-01-01

Objectives: To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Methods: Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. Results: The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (pvalue and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers. PMID:27010085

Genomic breeding value prediction:methods and procedures

NARCIS (Netherlands)

Calus, M.P.L.

2010-01-01

Animal breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the

Accurate Energies and Structures for Large Water Clusters Using the X3LYP Hybrid Density Functional

OpenAIRE

Su, Julius T.; Xu, Xin; Goddard, William A., III

2004-01-01

We predict structures and energies of water clusters containing up to 19 waters with X3LYP, an extended hybrid density functional designed to describe noncovalently bound systems as accurately as covalent systems. Our work establishes X3LYP as the most practical ab initio method today for calculating accurate water cluster structures and energies. We compare X3LYP/aug-cc-pVTZ energies to the most accurate theoretical values available (n = 2−6, 8), MP2 with basis set superposition error (BSSE)...

Markovian prediction of future values for food grains in the economic survey

Science.gov (United States)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

Earned Value Management

CERN Document Server

Ferguson, J

2002-01-01

Earned Value Management is a methodology used to measure and communicate the real physical progress of a project and show its true cost situation. This tool was developed by the US Department of Defense in 1967 and later used successfully for monitoring DOE projects, in particular the US LHC accelerator project. A clear distinction must be made between an earned value management system and other tools under consideration or already existing at CERN which permit accurate predictions of the amount and date of future payments or a detailed follow up of contracts.

Highly accurate prediction of food challenge outcome using routinely available clinical data.

Science.gov (United States)

DunnGalvin, Audrey; Daly, Deirdre; Cullinane, Claire; Stenke, Emily; Keeton, Diane; Erlewyn-Lajeunesse, Mich; Roberts, Graham C; Lucas, Jane; Hourihane, Jonathan O'B

2011-03-01

Serum specific IgE or skin prick tests are less useful at levels below accepted decision points. We sought to develop and validate a model to predict food challenge outcome by using routinely collected data in a diverse sample of children considered suitable for food challenge. The proto-algorithm was generated by using a limited data set from 1 service (phase 1). We retrospectively applied, evaluated, and modified the initial model by using an extended data set in another center (phase 2). Finally, we prospectively validated the model in a blind study in a further group of children undergoing food challenge for peanut, milk, or egg in the second center (phase 3). Allergen-specific models were developed for peanut, egg, and milk. Phase 1 (N = 429) identified 5 clinical factors associated with diagnosis of food allergy by food challenge. In phase 2 (N = 289), we examined the predictive ability of 6 clinical factors: skin prick test, serum specific IgE, total IgE minus serum specific IgE, symptoms, sex, and age. In phase 3 (N = 70), 97% of cases were accurately predicted as positive and 94% as negative. Our model showed an advantage in clinical prediction compared with serum specific IgE only, skin prick test only, and serum specific IgE and skin prick test (92% accuracy vs 57%, and 81%, respectively). Our findings have implications for the improved delivery of food allergy-related health care, enhanced food allergy-related quality of life, and economized use of health service resources by decreasing the number of food challenges performed. Copyright © 2011 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Exploring the knowledge behind predictions in everyday cognition: an iterated learning study.

Science.gov (United States)

Stephens, Rachel G; Dunn, John C; Rao, Li-Lin; Li, Shu

2015-10-01

Making accurate predictions about events is an important but difficult task. Recent work suggests that people are adept at this task, making predictions that reflect surprisingly accurate knowledge of the distributions of real quantities. Across three experiments, we used an iterated learning procedure to explore the basis of this knowledge: to what extent is domain experience critical to accurate predictions and how accurate are people when faced with unfamiliar domains? In Experiment 1, two groups of participants, one resident in Australia, the other in China, predicted the values of quantities familiar to both (movie run-times), unfamiliar to both (the lengths of Pharaoh reigns), and familiar to one but unfamiliar to the other (cake baking durations and the lengths of Beijing bus routes). While predictions from both groups were reasonably accurate overall, predictions were inaccurate in the selectively unfamiliar domains and, surprisingly, predictions by the China-resident group were also inaccurate for a highly familiar domain: local bus route lengths. Focusing on bus routes, two follow-up experiments with Australia-resident groups clarified the knowledge and strategies that people draw upon, plus important determinants of accurate predictions. For unfamiliar domains, people appear to rely on extrapolating from (not simply directly applying) related knowledge. However, we show that people's predictions are subject to two sources of error: in the estimation of quantities in a familiar domain and extension to plausible values in an unfamiliar domain. We propose that the key to successful predictions is not simply domain experience itself, but explicit experience of relevant quantities.

Probabilistic maximum-value wind prediction for offshore environments

DEFF Research Database (Denmark)

Staid, Andrea; Pinson, Pierre; Guikema, Seth D.

2015-01-01

statistical models to predict the full distribution of the maximum-value wind speeds in a 3 h interval. We take a detailed look at the performance of linear models, generalized additive models and multivariate adaptive regression splines models using meteorological covariates such as gust speed, wind speed......, convective available potential energy, Charnock, mean sea-level pressure and temperature, as given by the European Center for Medium-Range Weather Forecasts forecasts. The models are trained to predict the mean value of maximum wind speed, and the residuals from training the models are used to develop...... the full probabilistic distribution of maximum wind speed. Knowledge of the maximum wind speed for an offshore location within a given period can inform decision-making regarding turbine operations, planned maintenance operations and power grid scheduling in order to improve safety and reliability...

Diagnostic value of apparent diffusion coefficient value in prediction of grade for neuroepithelial tumors

International Nuclear Information System (INIS)

Chen Zhiye; Ma Lin

2009-01-01

Objective: To investigate the predictive value of ADC value in grading of neuroepithelial tumors. Methods: The clinical data and images of 70 patients with neuroepithelial tumors pathologically proven were collected and analyzed retrospectively. All the patients were classified into low (WHO I or II) and high (WHO III or IV) grade groups which included 40 and 30 cases respectively according to the 2007 WHO classification of tumours of the central nervous system. All the patients underwent plain and contrast-enhanced MR scan and DWI before surgery. The minimum ADC (MinADC) value was measured postoperatively on ADC maps. The Ki-67 labeling index (Ki-67 LI) of tumor tissue was determined by immunohistochemistry. MinADC values for two groups were analyzed using student t test, while the age and Ki-67 LI for the two groups was analyzed using Mann-Whitney test (P -3 mm 2 /s] of the low grade group was significantly higher than that [(0.74±0.18) x 10 -3 mm 2 /s] of the high grade group (t=5.42, P -3 mm 2 /s for the differentiation between high and low grade neuroepithelial tumors provided the best combination of sensitivity (90.0%) and specificity (77.5%) (receiver operating characteristic analysis). Conclusion: MinADC value is helpful for prediction of neuroepithelial tumor grade.. (authors)

Value-at-Risk and Extreme Returns

NARCIS (Netherlands)

J. Daníelsson (Jón); C.G. de Vries (Casper)

1997-01-01

textabstractAccurate prediction of the frequency of extreme events is of primary importance in many financial applications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaR evaluation. The largest risks are modelled parametrically, while smaller risks are captured by

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Science.gov (United States)

Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

2018-04-01

Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the

Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

Science.gov (United States)

Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

2011-01-01

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

Science.gov (United States)

Rey, M.; Nikitin, A. V.; Tyuterev, V.

2014-06-01

Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

Which is more useful in predicting hospital mortality--dichotomised blood test results or actual test values? A retrospective study in two hospitals.

Science.gov (United States)

Mohammed, Mohammed A; Rudge, Gavin; Wood, Gordon; Smith, Gary; Nangalia, Vishal; Prytherch, David; Holder, Roger; Briggs, Jim

2012-01-01

Routine blood tests are an integral part of clinical medicine and in interpreting blood test results clinicians have two broad options. (1) Dichotomise the blood tests into normal/abnormal or (2) use the actual values and overlook the reference values. We refer to these as the "binary" and the "non-binary" strategy respectively. We investigate which strategy is better at predicting the risk of death in hospital based on seven routinely undertaken blood tests (albumin, creatinine, haemoglobin, potassium, sodium, urea, and white blood cell count) using tree models to implement the two strategies. A retrospective database study of emergency admissions to an acute hospital during April 2009 to March 2010, involving 10,050 emergency admissions with routine blood tests undertaken within 24 hours of admission. We compared the area under the Receiver Operating Characteristics (ROC) curve for predicting in-hospital mortality using the binary and non-binary strategy. The mortality rate was 6.98% (701/10050). The mean predicted risk of death in those who died was significantly (p-value non-binary strategy (risk = 0.222 95%CI: 0.194 to 0.251), representing a risk difference of 28.74 deaths in the deceased patients (n = 701). The binary strategy had a significantly (p-value non-binary strategy (0.853 95% CI: 0.840 to 0.867). Similar results were obtained using data from another hospital. Dichotomising routine blood test results is less accurate in predicting in-hospital mortality than using actual test values because it underestimates the risk of death in patients who died. Further research into the use of actual blood test values in clinical decision making is required especially as the infrastructure to implement this potentially promising strategy already exists in most hospitals.

Which Is More Useful in Predicting Hospital Mortality -Dichotomised Blood Test Results or Actual Test Values? A Retrospective Study in Two Hospitals

Science.gov (United States)

Mohammed, Mohammed A.; Rudge, Gavin; Wood, Gordon; Smith, Gary; Nangalia, Vishal; Prytherch, David; Holder, Roger; Briggs, Jim

2012-01-01

Background Routine blood tests are an integral part of clinical medicine and in interpreting blood test results clinicians have two broad options. (1) Dichotomise the blood tests into normal/abnormal or (2) use the actual values and overlook the reference values. We refer to these as the “binary” and the “non-binary” strategy respectively. We investigate which strategy is better at predicting the risk of death in hospital based on seven routinely undertaken blood tests (albumin, creatinine, haemoglobin, potassium, sodium, urea, and white blood cell count) using tree models to implement the two strategies. Methodology A retrospective database study of emergency admissions to an acute hospital during April 2009 to March 2010, involving 10,050 emergency admissions with routine blood tests undertaken within 24 hours of admission. We compared the area under the Receiver Operating Characteristics (ROC) curve for predicting in-hospital mortality using the binary and non-binary strategy. Results The mortality rate was 6.98% (701/10050). The mean predicted risk of death in those who died was significantly (p-value non-binary strategy (risk = 0.222 95%CI: 0.194 to 0.251), representing a risk difference of 28.74 deaths in the deceased patients (n = 701). The binary strategy had a significantly (p-value non-binary strategy (0.853 95% CI: 0.840 to 0.867). Similar results were obtained using data from another hospital. Conclusions Dichotomising routine blood test results is less accurate in predicting in-hospital mortality than using actual test values because it underestimates the risk of death in patients who died. Further research into the use of actual blood test values in clinical decision making is required especially as the infrastructure to implement this potentially promising strategy already exists in most hospitals. PMID:23077528

JCZS: An Intermolecular Potential Database for Performing Accurate Detonation and Expansion Calculations

Energy Technology Data Exchange (ETDEWEB)

Baer, M.R.; Hobbs, M.L.; McGee, B.C.

1998-11-03

Exponential-13,6 (EXP-13,6) potential pammeters for 750 gases composed of 48 elements were determined and assembled in a database, referred to as the JCZS database, for use with the Jacobs Cowperthwaite Zwisler equation of state (JCZ3-EOS)~l) The EXP- 13,6 force constants were obtained by using literature values of Lennard-Jones (LJ) potential functions, by using corresponding states (CS) theory, by matching pure liquid shock Hugoniot data, and by using molecular volume to determine the approach radii with the well depth estimated from high-pressure isen- tropes. The JCZS database was used to accurately predict detonation velocity, pressure, and temperature for 50 dif- 3 Accurate predictions were also ferent explosives with initial densities ranging from 0.25 glcm3 to 1.97 g/cm . obtained for pure liquid shock Hugoniots, static properties of nitrogen, and gas detonations at high initial pressures.

A New Approach for Accurate Prediction of Liquid Loading of Directional Gas Wells in Transition Flow or Turbulent Flow

Directory of Open Access Journals (Sweden)

Ruiqing Ming

2017-01-01

Full Text Available Current common models for calculating continuous liquid-carrying critical gas velocity are established based on vertical wells and laminar flow without considering the influence of deviation angle and Reynolds number on liquid-carrying. With the increase of the directional well in transition flow or turbulent flow, the current common models cannot accurately predict the critical gas velocity of these wells. So we built a new model to predict continuous liquid-carrying critical gas velocity for directional well in transition flow or turbulent flow. It is shown from sensitivity analysis that the correction coefficient is mainly influenced by Reynolds number and deviation angle. With the increase of Reynolds number, the critical liquid-carrying gas velocity increases first and then decreases. And with the increase of deviation angle, the critical liquid-carrying gas velocity gradually decreases. It is indicated from the case calculation analysis that the calculation error of this new model is less than 10%, where accuracy is much higher than those of current common models. It is demonstrated that the continuous liquid-carrying critical gas velocity of directional well in transition flow or turbulent flow can be predicted accurately by using this new model.

High-order accurate numerical algorithm for three-dimensional transport prediction

Energy Technology Data Exchange (ETDEWEB)

Pepper, D W [Savannah River Lab., Aiken, SC; Baker, A J

1980-01-01

The numerical solution of the three-dimensional pollutant transport equation is obtained with the method of fractional steps; advection is solved by the method of moments and diffusion by cubic splines. Topography and variable mesh spacing are accounted for with coordinate transformations. First estimate wind fields are obtained by interpolation to grid points surrounding specific data locations. Numerical results agree with results obtained from analytical Gaussian plume relations for ideal conditions. The numerical model is used to simulate the transport of tritium released from the Savannah River Plant on 2 May 1974. Predicted ground level air concentration 56 km from the release point is within 38% of the experimentally measured value.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

Science.gov (United States)

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Development of a tool for prediction of ovarian cancer in patients with adnexal masses: Value of plasma fibrinogen.

Directory of Open Access Journals (Sweden)

Veronika Seebacher

Full Text Available To develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.We performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA was then used to evaluate the clinical net benefit of the prediction model.Ovarian cancer including borderline tumours was found in 241 (26.6% patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87-0.94. DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.We confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.

Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

Science.gov (United States)

Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

2015-01-01

Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

Science.gov (United States)

Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

2013-01-01

Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

The predictive value of bronchial histamine challenge in the diagnosis of bronchial asthma

DEFF Research Database (Denmark)

Madsen, F; Holstein-Rathlou, N H; Mosbech, H

1985-01-01

as asthmatics (n = 97) or non-asthmatics (n = 54). The diagnostic properties of the challenge were calculated using the statement of Baye. Considering PC20 values below 4.00 mg/ml as positive, the predictive value of a positive test was about 0.80 and the predictive value of a negative about 0.76. When PC20...

Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

Science.gov (United States)

Ben Ali, Jaouher; Chebel-Morello, Brigitte; Saidi, Lotfi; Malinowski, Simon; Fnaiech, Farhat

2015-05-01

Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.

Microvascular remodelling in preeclampsia: quantifying capillary rarefaction accurately and independently predicts preeclampsia.

Science.gov (United States)

Antonios, Tarek F T; Nama, Vivek; Wang, Duolao; Manyonda, Isaac T

2013-09-01

Preeclampsia is a major cause of maternal and neonatal mortality and morbidity. The incidence of preeclampsia seems to be rising because of increased prevalence of predisposing disorders, such as essential hypertension, diabetes, and obesity, and there is increasing evidence to suggest widespread microcirculatory abnormalities before the onset of preeclampsia. We hypothesized that quantifying capillary rarefaction could be helpful in the clinical prediction of preeclampsia. We measured skin capillary density according to a well-validated protocol at 5 consecutive predetermined visits in 322 consecutive white women, of whom 16 subjects developed preeclampsia. We found that structural capillary rarefaction at 20-24 weeks of gestation yielded a sensitivity of 0.87 with a specificity of 0.50 at the cutoff of 2 capillaries/field with the area under the curve of the receiver operating characteristic value of 0.70, whereas capillary rarefaction at 27-32 weeks of gestation yielded a sensitivity of 0.75 and a higher specificity of 0.77 at the cutoff of 8 capillaries/field with area under the curve of the receiver operating characteristic value of 0.82. Combining capillary rarefaction with uterine artery Doppler pulsatility index increased the sensitivity and specificity of the prediction. Multivariable analysis shows that the odds of preeclampsia are increased in women with previous history of preeclampsia or chronic hypertension and in those with increased uterine artery Doppler pulsatility index, but the most powerful and independent predictor of preeclampsia was capillary rarefaction at 27-32 weeks. Quantifying structural rarefaction of skin capillaries in pregnancy is a potentially useful clinical marker for the prediction of preeclampsia.

Predicting Breeding Values in Animals by Kalman Filter

DEFF Research Database (Denmark)

Karacaoren, B; Janss, L L G; Kadarmideen, H N

2012-01-01

The aim of this study was to investigate usefulness of Kalman Filter (KF) Random Walk methodology (KF-RW) for prediction of breeding values in animals. We used body condition score (BCS) from dairy cattle for illustrating use of KF-RW. BCS was measured by Swiss Holstein Breeding Association during...

Size matters. The width and location of a ureteral stone accurately predict the chance of spontaneous passage

Energy Technology Data Exchange (ETDEWEB)

Jendeberg, Johan; Geijer, Haakan; Alshamari, Muhammed; Liden, Mats [Oerebro University Hospital, Department of Radiology, Faculty of Medicine and Health, Oerebro (Sweden); Cierzniak, Bartosz [Oerebro University, Department of Surgery, Faculty of Medicine and Health, Oerebro (Sweden)

2017-11-15

To determine how to most accurately predict the chance of spontaneous passage of a ureteral stone using information in the diagnostic non-enhanced computed tomography (NECT) and to create predictive models with smaller stone size intervals than previously possible. Retrospectively 392 consecutive patients with ureteric stone on NECT were included. Three radiologists independently measured the stone size. Stone location, side, hydronephrosis, CRP, medical expulsion therapy (MET) and all follow-up radiology until stone expulsion or 26 weeks were recorded. Logistic regressions were performed with spontaneous stone passage in 4 weeks and 20 weeks as the dependent variable. The spontaneous passage rate in 20 weeks was 312 out of 392 stones, 98% in 0-2 mm, 98% in 3 mm, 81% in 4 mm, 65% in 5 mm, 33% in 6 mm and 9% in ≥6.5 mm wide stones. The stone size and location predicted spontaneous ureteric stone passage. The side and the grade of hydronephrosis only predicted stone passage in specific subgroups. Spontaneous passage of a ureteral stone can be predicted with high accuracy with the information available in the NECT. We present a prediction method based on stone size and location. (orig.)

Does Spontaneous Favorability to Power (vs. Universalism) Values Predict Spontaneous Prejudice and Discrimination?

Science.gov (United States)

Souchon, Nicolas; Maio, Gregory R; Hanel, Paul H P; Bardin, Brigitte

2017-10-01

We conducted five studies testing whether an implicit measure of favorability toward power over universalism values predicts spontaneous prejudice and discrimination. Studies 1 (N = 192) and 2 (N = 86) examined correlations between spontaneous favorability toward power (vs. universalism) values, achievement (vs. benevolence) values, and a spontaneous measure of prejudice toward ethnic minorities. Study 3 (N = 159) tested whether conditioning participants to associate power values with positive adjectives and universalism values with negative adjectives (or inversely) affects spontaneous prejudice. Study 4 (N = 95) tested whether decision bias toward female handball players could be predicted by spontaneous attitude toward power (vs. universalism) values. Study 5 (N = 123) examined correlations between spontaneous attitude toward power (vs. universalism) values, spontaneous importance toward power (vs. universalism) values, and spontaneous prejudice toward Black African people. Spontaneous positivity toward power (vs. universalism) values was associated with spontaneous negativity toward minorities and predicted gender bias in a decision task, whereas the explicit measures did not. These results indicate that the implicit assessment of evaluative responses attached to human values helps to model value-attitude-behavior relations. © 2016 The Authors. Journal of Personality Published by Wiley Periodicals, Inc.

Predicting quantitative and qualitative values of recreation participation

Science.gov (United States)

Elwood L., Jr. Shafer; George Moeller

1971-01-01

If future recreation consumption and associated intangible values can be predicted, the problem of rapid decision making in recreation-resource management can be reduced, and the problems of implementing those decisions can be anticipated. Management and research responsibilities for meeting recreation demand are discussed, and proved methods for forecasting recreation...

Predictive Value of Respiratory Rate Thresholds in Pneumonia ...

African Journals Online (AJOL)

A study was carried out to determine the predictive value of respiratory rate in the clinical diagnosis of pneumonia in 101 children with respiratory symptoms of <28 days duration. Clinical, demographic and anthropometric variables were obtained at presentation while confirmation of the diagnosis was by a chest x-ray in ...

Predictive value of preoperative tests in estimating difficult intubation in patients who underwent direct laryngoscopy in ear, nose, and throat surgery

Directory of Open Access Journals (Sweden)

Osman Karakus

2015-04-01

Full Text Available BACKGROUND AND OBJECTIVES: Predictive value of preoperative tests in estimating difficult intubation may differ in the laryngeal pathologies. Patients who had undergone direct laryngoscopy (DL were reviewed, and predictive value of preoperative tests in estimating difficult intubation was investigated. METHODS: Preoperative, and intraoperative anesthesia record forms, and computerized system of the hospital were screened. RESULTS: A total of 2611 patients were assessed. In 7.4% of the patients, difficult intubations were detected. Difficult intubations were encountered in some of the patients with Mallampati scoring (MS system Class 4 (50%, Cormack-Lehane classification (CLS Grade 4 (95.7%, previous knowledge of difficult airway (86.2%, restricted neck movements (cervical ROM (75.8%, short thyromental distance (TMD (81.6%, vocal cord mass (49.5% as indicated in parentheses (p < 0.0001. MS had a low sensitivity, while restricted cervical ROM, presence of a vocal cord mass, short thyromental distance, and MS each had a relatively higher positive predictive value. Incidence of difficult intubations increased 6.159 and 1.736-fold with each level of increase in CLS grade and MS class, respectively. When all tests were considered in combination difficult intubation could be classified accurately in 96.3% of the cases. CONCLUSION: Test results predicting difficult intubations in cases with DL had observedly overlapped with the results provided in the literature for the patient populations in general. Differences in some test results when compared with those of the general population might stem from the concomitant underlying laryngeal pathological conditions in patient populations with difficult intubation.

[Predictive value of preoperative tests in estimating difficult intubation in patients who underwent direct laryngoscopy in ear, nose, and throat surgery].

Science.gov (United States)

Karakus, Osman; Kaya, Cengiz; Ustun, Faik Emre; Koksal, Ersin; Ustun, Yasemin Burcu

2015-01-01

Predictive value of preoperative tests in estimating difficult intubation may differ in the laryngeal pathologies. Patients who had undergone direct laryngoscopy (DL) were reviewed, and predictive value of preoperative tests in estimating difficult intubation was investigated. Preoperative, and intraoperative anesthesia record forms, and computerized system of the hospital were screened. A total of 2611 patients were assessed. In 7.4% of the patients, difficult intubations were detected. Difficult intubations were encountered in some of the patients with Mallampati scoring (MS) system Class 4 (50%), Cormack-Lehane classification (CLS) Grade 4 (95.7%), previous knowledge of difficult airway (86.2%), restricted neck movements (cervical ROM) (75.8%), short thyromental distance (TMD) (81.6%), vocal cord mass (49.5%) as indicated in parentheses (p<0.0001). MS had a low sensitivity, while restricted cervical ROM, presence of a vocal cord mass, short thyromental distance, and MS each had a relatively higher positive predictive value. Incidence of difficult intubations increased 6.159 and 1.736-fold with each level of increase in CLS grade and MS class, respectively. When all tests were considered in combination difficult intubation could be classified accurately in 96.3% of the cases. Test results predicting difficult intubations in cases with DL had observedly overlapped with the results provided in the literature for the patient populations in general. Differences in some test results when compared with those of the general population might stem from the concomitant underlying laryngeal pathological conditions in patient populations with difficult intubation. Copyright © 2014 Sociedade Brasileira de Anestesiologia. Publicado por Elsevier Editora Ltda. All rights reserved.

An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

Science.gov (United States)

De Vore, Karl W; Fatahi, Nadia M; Sass, John E

2016-08-01

Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

Does the emergency surgery score accurately predict outcomes in emergent laparotomies?

Science.gov (United States)

Peponis, Thomas; Bohnen, Jordan D; Sangji, Naveen F; Nandan, Anirudh R; Han, Kelsey; Lee, Jarone; Yeh, D Dante; de Moya, Marc A; Velmahos, George C; Chang, David C; Kaafarani, Haytham M A

2017-08-01

The emergency surgery score is a mortality-risk calculator for emergency general operation patients. We sought to examine whether the emergency surgery score predicts 30-day morbidity and mortality in a high-risk group of patients undergoing emergent laparotomy. Using the 2011-2012 American College of Surgeons National Surgical Quality Improvement Program database, we identified all patients who underwent emergent laparotomy using (1) the American College of Surgeons National Surgical Quality Improvement Program definition of "emergent," and (2) all Current Procedural Terminology codes denoting a laparotomy, excluding aortic aneurysm rupture. Multivariable logistic regression analyses were performed to measure the correlation (c-statistic) between the emergency surgery score and (1) 30-day mortality, and (2) 30-day morbidity after emergent laparotomy. As sensitivity analyses, the correlation between the emergency surgery score and 30-day mortality was also evaluated in prespecified subgroups based on Current Procedural Terminology codes. A total of 26,410 emergent laparotomy patients were included. Thirty-day mortality and morbidity were 10.2% and 43.8%, respectively. The emergency surgery score correlated well with mortality (c-statistic = 0.84); scores of 1, 11, and 22 correlated with mortalities of 0.4%, 39%, and 100%, respectively. Similarly, the emergency surgery score correlated well with morbidity (c-statistic = 0.74); scores of 0, 7, and 11 correlated with complication rates of 13%, 58%, and 79%, respectively. The morbidity rates plateaued for scores higher than 11. Sensitivity analyses demonstrated that the emergency surgery score effectively predicts mortality in patients undergoing emergent (1) splenic, (2) gastroduodenal, (3) intestinal, (4) hepatobiliary, or (5) incarcerated ventral hernia operation. The emergency surgery score accurately predicts outcomes in all types of emergent laparotomy patients and may prove valuable as a bedside decision

Determinants of work ability and its predictive value for disability.

Science.gov (United States)

Alavinia, S M; de Boer, A G E M; van Duivenbooden, J C; Frings-Dresen, M H W; Burdorf, A

2009-01-01

Maintaining the ability of workers to cope with physical and psychosocial demands at work becomes increasingly important in prolonging working life. To analyse the effects of work-related factors and individual characteristics on work ability and to determine the predictive value of work ability on receiving a work-related disability pension. A longitudinal study was conducted among 850 construction workers aged 40 years and older, with average follow-up period of 23 months. Disability was defined as receiving a disability pension, granted to workers unable to continue working in their regular job. Work ability was assessed using the work ability index (WAI). Associations between work-related factors and individual characteristics with work ability at baseline were evaluated using linear regression analysis, and Cox regression analysis was used to evaluate the predictive value of work ability for disability. Work-related factors were associated with a lower work ability at baseline, but had little prognostic value for disability during follow-up. The hazard ratios for disability among workers with a moderate and poor work ability at baseline were 8 and 32, respectively. All separate scales in the WAI had predictive power for future disability with the highest influence of current work ability in relation to job demands and lowest influence of diseases diagnosed by a physician. A moderate or poor work ability was highly predictive for receiving a disability pension. Preventive measures should facilitate a good balance between work performance and health in order to prevent quitting labour participation.

Predictive value of upper-limb accelerometry in acute stroke with hemiparesis

NARCIS (Netherlands)

Gebruers, Nick; Truijen, Steven; Engelborghs, Sebastiaan; De Deyn, Peter P.

2013-01-01

Few studies have investigated how well early activity measurements by accelerometers predict recovery after stroke. First, we assessed the predictive value of accelerometer-based measurements of upper-limb activity in patients with acute stroke with a hemiplegic arm. Second, we established the

A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

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Meysam Bastani

Full Text Available BACKGROUND: Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. METHODS: To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. RESULTS: This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. CONCLUSIONS: Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions.

Predictive values of symptoms in relation to cancer diagnosis

DEFF Research Database (Denmark)

Krasnik, Ivan; Andersen, John Sahl

a manual describing the symptoms that should engender reasonable suspicion of malignancy (“alarm symptoms”) to the general practitioner. Objectives: To investigate the evidence in the literature of the predictive value (PPV) placed on the”alarm symptoms” for colon cancer, breast cancer, prostate cancer...... years (6,6%-21,2%), but much lower in younger age groups. ”Change in bowel habits” and ”Significant general symptoms” are more uncertain (3,5%-8,5%). Breast cancer: ”Palpable suspect tumor” is well supported (8,1%-24%). The predictive value of ”Pitting of the skin”, ”Papil-areola eczema......Background/significance: Poorer prognosis for cancer patients in Denmark than in comparable countries has been shown and contributed to the introduction of accelerated diagnostic trajectories for patients suspicious for cancer in 2008. For all types of cancers the National Board of Health developed...

Prediction of postoperative pain: a systematic review of predictive experimental pain studies

DEFF Research Database (Denmark)

Werner, Mads Utke; Mjöbo, Helena N; Nielsen, Per R

2010-01-01

Quantitative testing of a patient's basal pain perception before surgery has the potential to be of clinical value if it can accurately predict the magnitude of pain and requirement of analgesics after surgery. This review includes 14 studies that have investigated the correlation between...... preoperative responses to experimental pain stimuli and clinical postoperative pain and demonstrates that the preoperative pain tests may predict 4-54% of the variance in postoperative pain experience depending on the stimulation methods and the test paradigm used. The predictive strength is much higher than...

Accurate electrostatic and van der Waals pull-in prediction for fully clamped nano/micro-beams using linear universal graphs of pull-in instability

Science.gov (United States)

Tahani, Masoud; Askari, Amir R.

2014-09-01

In spite of the fact that pull-in instability of electrically actuated nano/micro-beams has been investigated by many researchers to date, no explicit formula has been presented yet which can predict pull-in voltage based on a geometrically non-linear and distributed parameter model. The objective of present paper is to introduce a simple and accurate formula to predict this value for a fully clamped electrostatically actuated nano/micro-beam. To this end, a non-linear Euler-Bernoulli beam model is employed, which accounts for the axial residual stress, geometric non-linearity of mid-plane stretching, distributed electrostatic force and the van der Waals (vdW) attraction. The non-linear boundary value governing equation of equilibrium is non-dimensionalized and solved iteratively through single-term Galerkin based reduced order model (ROM). The solutions are validated thorough direct comparison with experimental and other existing results reported in previous studies. Pull-in instability under electrical and vdW loads are also investigated using universal graphs. Based on the results of these graphs, non-dimensional pull-in and vdW parameters, which are defined in the text, vary linearly versus the other dimensionless parameters of the problem. Using this fact, some linear equations are presented to predict pull-in voltage, the maximum allowable length, the so-called detachment length, and the minimum allowable gap for a nano/micro-system. These linear equations are also reduced to a couple of universal pull-in formulas for systems with small initial gap. The accuracy of the universal pull-in formulas are also validated by comparing its results with available experimental and some previous geometric linear and closed-form findings published in the literature.

Predictive value of early near-infrared spectroscopy monitoring of patients with traumatic brain injury

Directory of Open Access Journals (Sweden)

Alina Vilkė

2014-01-01

Conclusions: NIRS plays an important role in the clinical care of TBI patients. Regional brain saturation monitoring provides accurate predictive data, which can improve the allocation of scarce medical resources, set the treatment goals and alleviate the early communication with patients’ relatives.

Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey

International Nuclear Information System (INIS)

Günay, M. Erdem

2016-01-01

In this work, the annual gross electricity demand of Turkey was modeled by multiple linear regression and artificial neural networks as a function population, gross domestic product per capita, inflation percentage, unemployment percentage, average summer temperature and average winter temperature. Among these, the unemployment percentage and the average winter temperature were found to be insignificant to determine the demand for the years between 1975 and 2013. Next, the future values of the statistically significant variables were predicted by time series ANN models, and these were simulated in a multilayer perceptron ANN model to forecast the future annual electricity demand. The results were validated with a very high accuracy for the years that the electricity demand was known (2007–2013), and they were also superior to the official predictions (done by Ministry of Energy and Natural Resources of Turkey). The model was then used to forecast the annual gross electricity demand for the future years, and it was found that, the demand will be doubled reaching about 460 TW h in the year 2028. Finally, it was concluded that the approach applied in this work can easily be implemented for other countries to make accurate predictions for the future. - Highlights: • Electricity demand of Turkey increased from 15.6 to 246.4 TW h in 1975–2013 period. • Population, GDP per capita, inflation and average summer temperature influence demand. • Future values of descriptor variables can be predicted by time series ANN models. • ANN model simulated by the predicted values of descriptors can forecast the demand. • Demand is forecasted to be doubled reaching about 460 TW h in the year 2028.

Applying the Expectancy-Value Model to understand health values.

Science.gov (United States)

Zhang, Xu-Hao; Xie, Feng; Wee, Hwee-Lin; Thumboo, Julian; Li, Shu-Chuen

2008-03-01

% and 28% in separate MLR models (P values became small and explanatory power of EVM was reduced to a range between 8% and 23%. EVM was useful in explaining variances of health values and predicting important factors. Its power to explain small variances might be restricted due to limitations of 7-point Likert scale to measure AAs accurately. With further improvement and validation of a compatible continuous scale for more accurate measurement, EVM is expected to explain health values to a larger extent.

Predicting entrepreneurial career intentions: Values and the theory of planned behavior.

NARCIS (Netherlands)

M.J. Gorgievski-Duijvesteijn (Marjan); U. Stephan (Ute); M. Laguna (Mariola); J.A. Moriano (Juan)

2017-01-01

textabstractIntegrating predictions from the theory of human values with the theory of planned behavior (TPB), our primary goal is to investigate mechanisms through which individual values are related to entrepreneurial career intentions using a sample of 823 students from four European countries.

The value of computed tomography-urography in predicting the ...

African Journals Online (AJOL)

Background The natural course of pelviureteric junction (PUJ) obstruction is variable. Of those who require surgical intervention, there is no definite reliable preoperative predictor of the likely postoperative outcome. We evaluated the value of preoperative computed tomography (CT)-urography in predicting the ...

Hemostatic system changes predictive value in patients with ischemic brain disorders

Directory of Open Access Journals (Sweden)

Raičević Ranko

2002-01-01

Full Text Available The aim of this research was to determine the importance of tracking the dynamics of changes of the hemostatic system factors (aggregation of thrombocytes, D-dimer, PAI-1, antithrombin III, protein C and protein S, factor VII and factor VIII, fibrin degradation products, euglobulin test and the activated partial thromboplastin time – aPTPV in relation to the level of the severity of ischemic brain disorders (IBD and the level of neurological and functional deficiency in the beginning of IBD manifestation from 7 to 10 days, 19 to 21 day, and after 3 to 6 months. The research results confirmed significant predictive value of changes of hemostatic system with the predomination of procoagulant factors, together with the insufficiency of fibrinolysis. Concerning the IBD severity and it's outcome, the significant predictive value was shown in the higher levels of PAI-1 and the lower level of antithrombin III, and borderline significant value was shown in the accelerated aggregation of thrombocytes and the increased concentration of D-dimer. It could be concluded that the tracking of the dynamics of changes in parameters of hemostatic system proved to be an easily accessible method with the significant predictive value regarding the development of more severe. IBD cases and the outcome of the disease itself.

Accuracy of Clinicians in Predicting the Bacterial Cause of Clinical Bovine Mastitis

OpenAIRE

White, Maurice E.; Glickman, Lawrence T.; Barnes-Pallesen, Frances D.; Stem, Edgar S.; Dinsmore, Page; Powers, Michael S.; Powers, Pamela; Smith, Mary C.; Jasko, David

1986-01-01

We examined the ability of clinicians to predict the causative organism of bovine mastitis in our practice. We obtained 118 milk culture results from 112 mastitic cows and compared the culture results to the predictions of clinicians at the time of milk sample collection. Sixty of 118 culture results were accurately predicted. The positive predictive value for coliform mastitis was 42% and the negative predictive value was 79% in a study population with a 31% prevalence of coliform mastitis. ...

Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

Science.gov (United States)

Kountouris, Petros; Hirst, Jonathan D

2010-07-31

Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

Positive predictive value of infective endocarditis in the Danish National Patient Registry: a validation study.

Science.gov (United States)

Østergaard, Lauge; Adelborg, Kasper; Sundbøll, Jens; Pedersen, Lars; Loldrup Fosbøl, Emil; Schmidt, Morten

2018-05-30

The positive predictive value of an infective endocarditis diagnosis is approximately 80% in the Danish National Patient Registry. However, since infective endocarditis is a heterogeneous disease implying long-term intravenous treatment, we hypothesiszed that the positive predictive value varies by length of hospital stay. A total of 100 patients with first-time infective endocarditis in the Danish National Patient Registry were identified from January 2010 - December 2012 at the University hospital of Aarhus and regional hospitals of Herning and Randers. Medical records were reviewed. We calculated the positive predictive value according to admission length, and separately for patients with a cardiac implantable electronic device and a prosthetic heart valve using the Wilson score method. Among the 92 medical records available for review, the majority of the patients had admission length ⩾2 weeks. The positive predictive value increased with length of admission. In patients with admission length value was 65% while it was 90% for admission length ⩾2 weeks. The positive predictive value was 81% for patients with a cardiac implantable electronic device and 87% for patients with a prosthetic valve. The positive predictive value of the infective endocarditis diagnosis in the Danish National Patient Registry is high for patients with admission length ⩾2 weeks. Using this algorithm, the Danish National Patient Registry provides a valid source for identifying infective endocarditis for research.

Predicting Falls in People with Multiple Sclerosis: Fall History Is as Accurate as More Complex Measures

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Michelle H. Cameron

2013-01-01

Full Text Available Background. Many people with MS fall, but the best method for identifying those at increased fall risk is not known. Objective. To compare how accurately fall history, questionnaires, and physical tests predict future falls and injurious falls in people with MS. Methods. 52 people with MS were asked if they had fallen in the past 2 months and the past year. Subjects were also assessed with the Activities-specific Balance Confidence, Falls Efficacy Scale-International, and Multiple Sclerosis Walking Scale-12 questionnaires, the Expanded Disability Status Scale, Timed 25-Foot Walk, and computerized dynamic posturography and recorded their falls daily for the following 6 months with calendars. The ability of baseline assessments to predict future falls was compared using receiver operator curves and logistic regression. Results. All tests individually provided similar fall prediction (area under the curve (AUC 0.60–0.75. A fall in the past year was the best predictor of falls (AUC 0.75, sensitivity 0.89, specificity 0.56 or injurious falls (AUC 0.69, sensitivity 0.96, specificity 0.41 in the following 6 months. Conclusion. Simply asking people with MS if they have fallen in the past year predicts future falls and injurious falls as well as more complex, expensive, or time-consuming approaches.

PREDICTION OF BULLS’ SLAUGHTER VALUE FROM GROWTH DATA USING ARTIFICIAL NEURAL NETWORK

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Krzysztof ADAMCZYK

2006-02-01

Full Text Available The objective of this research was to investigate the usefulness of artifi cial neural network (ANN in the prediction of slaughter value of young crossbred bulls based on growth data. The studies were carried out on 104 bulls fattened from 120 days of life until the weight of 500 kg. The bulls were group fed using mainly farm feeds. After slaughter the carcasses were dissected and meat was subjected to physico-chemical and organoleptic analyses. The obtained data were used for the development of an artifi cial neural network model of slaughter value prediction. It was found that some slaughter value traits (hot carcass, cold half-carcass, neck and round weights, bone content in dissected elements in half-carcass, meat pH, dry-matter and protein contents in meat and meat tenderness and juiciness can be predicted with a considerably high accuracy using the artifi cial neural network.

Three-dimensional computed tomographic volumetry precisely predicts the postoperative pulmonary function.

Science.gov (United States)

Kobayashi, Keisuke; Saeki, Yusuke; Kitazawa, Shinsuke; Kobayashi, Naohiro; Kikuchi, Shinji; Goto, Yukinobu; Sakai, Mitsuaki; Sato, Yukio

2017-11-01

It is important to accurately predict the patient's postoperative pulmonary function. The aim of this study was to compare the accuracy of predictions of the postoperative residual pulmonary function obtained with three-dimensional computed tomographic (3D-CT) volumetry with that of predictions obtained with the conventional segment-counting method. Fifty-three patients scheduled to undergo lung cancer resection, pulmonary function tests, and computed tomography were enrolled in this study. The postoperative residual pulmonary function was predicted based on the segment-counting and 3D-CT volumetry methods. The predicted postoperative values were compared with the results of postoperative pulmonary function tests. Regarding the linear correlation coefficients between the predicted postoperative values and the measured values, those obtained using the 3D-CT volumetry method tended to be higher than those acquired using the segment-counting method. In addition, the variations between the predicted and measured values were smaller with the 3D-CT volumetry method than with the segment-counting method. These results were more obvious in COPD patients than in non-COPD patients. Our findings suggested that the 3D-CT volumetry was able to predict the residual pulmonary function more accurately than the segment-counting method, especially in patients with COPD. This method might lead to the selection of appropriate candidates for surgery among patients with a marginal pulmonary function.

Corrosion pit depth extreme value prediction from limited inspection data

International Nuclear Information System (INIS)

Najjar, D.; Bigerelle, M.; Iost, A.; Bourdeau, L.; Guillou, D.

2004-01-01

Passive alloys like stainless steels are prone to localized corrosion in chlorides containing environments. The greater the depth of the localized corrosion phenomenon, the more dramatic the related damage that can lead to a structure weakening by fast perforation. In practical situations, because measurements are time consuming and expensive, the challenge is usually to predict the maximum pit depth that could be found in a large scale installation from the processing of a limited inspection data. As far as the parent distribution of pit depths is assumed to be of exponential type, the most successful method was found in the application of the statistical extreme-value analysis developed by Gumbel. This study aims to present a new and alternative methodology to the Gumbel approach with a view towards accurately estimating the maximum pit depth observed on a ferritic stainless steel AISI 409 subjected to an accelerated corrosion test (ECC1) used in automotive industry. This methodology consists in characterising and modelling both the morphology of pits and the statistical distribution of their depths from a limited inspection dataset. The heart of the data processing is based on the combination of two recent statistical methods that avoid making any choice about the type of the theoretical underlying parent distribution of pit depths: the Generalized Lambda Distribution (GLD) is used to model the distribution of pit depths and the Bootstrap technique to determine a confidence interval on the maximum pit depth. (authors)

Searching for Symbolic Value of Cattle: Tropical Livestock Units, Market Price, and Cultural Value of Maasai Livestock

Directory of Open Access Journals (Sweden)

Robert J. Quinlan

2016-10-01

Full Text Available We examine metabolic, market, and symbolic values of livestock relative to cultural “positioning” by gender, marriage, and household production among Maasai people in Simanjiro, Tanzania to assess local “proximate currencies” relevant for “cultural success.” Data from mixed methods ethnographic research include qualitative interviews since 2012, observation of 85 livestock market sales in 2013 and 2015, and 37 short key informant interviews in 2015. We examine fit between market values, Tropical Livestock Units (TLU, weight-based species exchange ratio, and perceived value from interviews for moran (unmarried men, muruo (married men, and tɔmɔnɔ́k (married women. Hedonic regression using livestock species, sex, maturity, and size accounted for 90% of the local market price of livestock. We compared the market-based exchange ratio between cattle and smallstock (sheep and goats to TLU and perceived values situating symbolic value of cattle in terms of Maasai household production schema. One TLU model accurately predicted market exchange ratios, while another predicted hypothetical exchanges, suggesting need for improved livestock wealth estimation for pastoralists. Ritual context, subsistence work, and cultural position influenced perceived values: Moran overvalued cattle by 100% of the local market value. Tɔmɔnɔ́k accurately perceived the market exchange ratio despite never directly engaging in livestock market transactions. Muruo perceived exchange ratios intermediate between moran and tɔmɔnɔ́k. We argue that these perceptions of value reflect distinct labor responsibilities of moran, muruo, and tɔmɔnɔ́k in livestock management, differential value of bridewealth, and control of meat and milk.Attention to value of different livestock species in cultural models of production may prove useful for development efforts.

Statistical analysis of accurate prediction of local atmospheric optical attenuation with a new model according to weather together with beam wandering compensation system: a season-wise experimental investigation

Science.gov (United States)

Arockia Bazil Raj, A.; Padmavathi, S.

2016-07-01

Atmospheric parameters strongly affect the performance of Free Space Optical Communication (FSOC) system when the optical wave is propagating through the inhomogeneous turbulent medium. Developing a model to get an accurate prediction of optical attenuation according to meteorological parameters becomes significant to understand the behaviour of FSOC channel during different seasons. A dedicated free space optical link experimental set-up is developed for the range of 0.5 km at an altitude of 15.25 m. The diurnal profile of received power and corresponding meteorological parameters are continuously measured using the developed optoelectronic assembly and weather station, respectively, and stored in a data logging computer. Measured meteorological parameters (as input factors) and optical attenuation (as response factor) of size [177147 × 4] are used for linear regression analysis and to design the mathematical model that is more suitable to predict the atmospheric optical attenuation at our test field. A model that exhibits the R2 value of 98.76% and average percentage deviation of 1.59% is considered for practical implementation. The prediction accuracy of the proposed model is investigated along with the comparative results obtained from some of the existing models in terms of Root Mean Square Error (RMSE) during different local seasons in one-year period. The average RMSE value of 0.043-dB/km is obtained in the longer range dynamic of meteorological parameters variations.

Predictive value of impaired evacuation at proctography in diagnosing anismus.

Science.gov (United States)

Halligan, S; Malouf, A; Bartram, C I; Marshall, M; Hollings, N; Kamm, M A

2001-09-01

We aimed to determine the positive predictive value of impaired evacuation during evacuation proctography for the subsequent diagnosis of anismus. Thirty-one adults with signs of impaired evacuation (defined as the inability to evacuate two thirds of a 120 mL contrast enema within 30 sec) during evacuation proctography underwent subsequent anorectal physiologic testing for anismus. A physiologic diagnosis of anismus was based on a typical clinical history of the condition combined with impaired rectal balloon expulsion or abnormal surface electromyogram. Twenty-eight (90%) of the 31 patients with impaired proctographic evacuation were found to have anismus at subsequent physiologic testing. Among the 28 were all 10 patients who evacuated no contrast medium and all 11 patients with inadequate pelvic floor descent, giving evacuation proctography a positive predictive value of 90% for the diagnosis of anismus. A prominent puborectal impression was seen in only three subjects during proctography, one of whom subsequently showed no physiologic sign of anismus. Impaired evacuation during evacuation proctography is highly predictive for diagnosis of anismus.

Do MCI criteria in drug trials accurately identify subjects with predementia Alzheimer's disease?

Science.gov (United States)

Visser, P; Scheltens, P; Verhey, F

2005-01-01

Background: Drugs effective in Alzheimer-type dementia have been tested in subjects with mild cognitive impairment (MCI) because these are supposed to have Alzheimer's disease in the predementia stage. Objectives: To investigate whether MCI criteria used in these drug trials can accurately diagnose subjects with predementia Alzheimer's disease. Methods: MCI criteria of the Gal-Int 11 study, InDDEx study, ADCS memory impairment study, ampakine CX 516 study, piracetam study, and Merck rofecoxib study were applied retrospectively in a cohort of 150 non-demented subjects from a memory clinic. Forty two had progressed to Alzheimer type dementia during a five year follow up period and were considered to have predementia Alzheimer's disease at baseline. Outcome measures were the odds ratio, sensitivity, specificity, and positive and negative predictive value. Results: The odds ratio of the MCI criteria for predementia Alzheimer's disease varied between 0.84 and 11. Sensitivity varied between 0.46 and 0.83 and positive predictive value between 0.43 and 0.76. None of the criteria combined a high sensitivity with a high positive predictive value. Exclusion criteria for depression led to an increase in positive predictive value and specificity at the cost of sensitivity. In subjects older than 65 years the positive predictive value was higher than in younger subjects. Conclusions: The diagnostic accuracy of MCI criteria used in trials for predementia Alzheimer's disease is low to moderate. Their use may lead to inclusion of many patients who do not have predementia Alzheimer's disease or to exclusion of many who do. Subjects with moderately severe depression should not be excluded from trials in order not to reduce the sensitivity. PMID:16170074

Predictive value of digital subtraction angiography in patients with tuberculous meningitis

International Nuclear Information System (INIS)

Rojas-Echeverri, L.A.; Soto-Hernandez, J.L.; Garza, S.; Martinez-Zubieta, R.; Miranda, L.I.; Garcia-Ramos, G.; Zenteno, M.

1996-01-01

Digital subtraction angiography (DSA) was performed in 24 adults with tuberculous meningitis (TBM) and results were correlated with 24 admission and 16 follow-up CT examinations. 19 MRI studies and clinical outcome at a mean follow-up of 44 weeks. DSA was abnormal in 11 patients. Abnormal DSA was associated with advenced clinical stages of the Medical Research Council classification, admission CT with hydrocephalus or gyral cortical enhancement. MRI disclosed brain infarcts not seen on initial CT in 8 cases. Of seven patients who died, 4 had abnormal and 3 normal DSA. Among patients who survived, those with normal DSA had a better functional outcome by Karnofsky scores. During follow-up infarcts were evident in 16 patients. Abnormal DSA in relation to brain infarcts had a sensitivity of 0.56, specificity 0.75, positive predictive value 0.82 and negative predictive value 0.46. A single arteriogram does not predict the outcome in patients with TBM and its value is limited in the assessment of vascular complications of TBM. Angiography in TBM is justified only in specific clinical trials to assess new therapeutic modalities against infarcts. (orig.)

Predicting Malaysian palm oil price using Extreme Value Theory

OpenAIRE

Chuangchid, K; Sriboonchitta, S; Rahman, S; Wiboonpongse, A

2013-01-01

This paper uses the extreme value theory (EVT) to predict extreme price events of Malaysian palm oil in the future, based on monthly futures price data for a 25 year period (mid-1986 to mid-2011). Model diagnostic has confirmed non-normal distribution of palm oil price data, thereby justifying the use of EVT. Two principal approaches to model extreme values – the Block Maxima (BM) and Peak-Over- Threshold (POT) models – were used. Both models revealed that the palm oil price will peak at ...

Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

International Nuclear Information System (INIS)

Richieri, Raphaelle; Lancon, Christophe; Boyer, Laurent; Farisse, Jean; Colavolpe, Cecile; Mundler, Olivier; Guedj, Eric

2011-01-01

The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of 99m Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

A weighted generalized score statistic for comparison of predictive values of diagnostic tests.

Science.gov (United States)

Kosinski, Andrzej S

2013-03-15

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations that are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we presented, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic that incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, always reduces to the score statistic in the independent samples situation, and preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe that the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the WGS test statistic in a general GEE setting. Copyright © 2012 John Wiley & Sons, Ltd.

Albumin-Bilirubin and Platelet-Albumin-Bilirubin Grades Accurately Predict Overall Survival in High-Risk Patients Undergoing Conventional Transarterial Chemoembolization for Hepatocellular Carcinoma.

Science.gov (United States)

Hansmann, Jan; Evers, Maximilian J; Bui, James T; Lokken, R Peter; Lipnik, Andrew J; Gaba, Ron C; Ray, Charles E

2017-09-01

To evaluate albumin-bilirubin (ALBI) and platelet-albumin-bilirubin (PALBI) grades in predicting overall survival in high-risk patients undergoing conventional transarterial chemoembolization for hepatocellular carcinoma (HCC). This single-center retrospective study included 180 high-risk patients (142 men, 59 y ± 9) between April 2007 and January 2015. Patients were considered high-risk based on laboratory abnormalities before the procedure (bilirubin > 2.0 mg/dL, albumin 1.2 mg/dL); presence of ascites, encephalopathy, portal vein thrombus, or transjugular intrahepatic portosystemic shunt; or Model for End-Stage Liver Disease score > 15. Serum albumin, bilirubin, and platelet values were used to determine ALBI and PALBI grades. Overall survival was stratified by ALBI and PALBI grades with substratification by Child-Pugh class (CPC) and Barcelona Liver Clinic Cancer (BCLC) stage using Kaplan-Meier analysis. C-index was used to determine discriminatory ability and survival prediction accuracy. Median survival for 79 ALBI grade 2 patients and 101 ALBI grade 3 patients was 20.3 and 10.7 months, respectively (P  .05). ALBI and PALBI grades are accurate survival metrics in high-risk patients undergoing conventional transarterial chemoembolization for HCC. Use of these scores allows for more refined survival stratification within CPC and BCLC stage. Copyright © 2017 SIR. Published by Elsevier Inc. All rights reserved.

Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

Energy Technology Data Exchange (ETDEWEB)

Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier [Centre Hospitalo-Universitaire de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Assistance Publique des Hopitaux de Marseille, Marseille Cedex 5 (France); Niboyet, Jean [Clinique La Phoceanne, Unite d' Etude et de Traitement de la Douleur, Marseille (France)

2007-08-15

Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 {+-} 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 {+-} 4.2 before ketamine and 31.8 {+-} 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

International Nuclear Information System (INIS)

Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier; Niboyet, Jean

2007-01-01

Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 ± 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 ± 4.2 before ketamine and 31.8 ± 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

Predictive value of proteinuria in adult dengue severity.

Directory of Open Access Journals (Sweden)

Farhad F Vasanwala

2014-02-01

Full Text Available BACKGROUND: Dengue is an important viral infection with different presentations. Predicting disease severity is important in triaging patients requiring hospital care. We aim to study the value of proteinuria in predicting the development of dengue hemorrhagic fever (DHF, utility of urine dipstick test as a rapid prognostic tool. METHODOLOGY AND PRINCIPAL FINDINGS: Adult patients with undifferentiated fever (n = 293 were prospectively enrolled at the Infectious Disease Research Clinic at Tan Tock Seng Hospital, Singapore from January to August 2012. Dengue infection was confirmed in 168 (57% by dengue RT-PCR or NS1 antigen detection. Dengue cases had median fever duration of 6 days at enrollment. DHF was diagnosed in 34 cases according to the WHO 1997 guideline. Dengue fever (DF patients were predominantly younger and were mostly seen in the outpatient setting with higher platelet level. Compared to DF, DHF cases had significantly higher peak urine protein creatinine ratio (UPCR during clinical course (26 vs. 40 mg/mmol; p<0.001. We obtained a UPCR cut-off value of 29 mg/mmol based on maximum AUC in ROC curves of peak UPCR for DF versus DHF, corresponding to 76% sensitivity and 60% specificity. Multivariate analysis with other readily available clinical and laboratory variables increased the AUC to 0.91 with 92% sensitivity and 80% specificity. Neither urine dipstick at initial presentation nor peak urine dipstick value during the entire illness was able to discriminate between DF and DHF. CONCLUSIONS: Proteinuria measured by a laboratory-based UPCR test may be sensitive and specific in prognosticating adult dengue patients.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Accurate Determination of the Values of Fundamental Physical Constants: The Basis of the New "Quantum" SI Units

Science.gov (United States)

Karshenboim, S. G.

2018-03-01

The metric system appeared as the system of units designed for macroscopic (laboratory scale) measurements. The progress in accurate determination of the values of quantum constants (such as the Planck constant) in SI units shows that the capabilities in high-precision measurement of microscopic and macroscopic quantities in terms of the same units have increased substantially recently. At the same time, relative microscopic measurements (for example, the comparison of atomic transition frequencies or atomic masses) are often much more accurate than relative measurements of macroscopic quantities. This is the basis for the strategy to define units in microscopic phenomena and then use them on the laboratory scale, which plays a crucial role in practical methodological applications determined by everyday life and technologies. The international CODATA task group on fundamental constants regularly performs an overall analysis of the precision world data (the so-called Adjustment of the Fundamental Constants) and publishes their recommended values. The most recent evaluation was based on the data published by the end of 2014; here, we review the corresponding data and results. The accuracy in determination of the Boltzmann constant has increased, the consistency of the data on determination of the Planck constant has improved; it is these two dimensional constants that will be used in near future as the basis for the new definition of the kelvin and kilogram, respectively. The contradictions in determination of the Rydberg constant and the proton charge radius remain. The accuracy of determination of the fine structure constant and relative atomic weight of the electron has improved. Overall, we give a detailed review of the state of the art in precision determination of the values of fundamental constants. The mathematical procedure of the Adjustment, the new data and results are considered in detail. The limitations due to macroscopic properties of material

Predictability of bone density at posterior mandibular implant sites using cone-beam computed tomography intensity values.

Science.gov (United States)

Alkhader, Mustafa; Hudieb, Malik; Khader, Yousef

2017-01-01

The aim of this study was to investigate the predictability of bone density at posterior mandibular implant sites using cone-beam computed tomography (CBCT) intensity values. CBCT cross-sectional images for 436 posterior mandibular implant sites were selected for the study. Using Invivo software (Anatomage, San Jose, California, USA), two observers classified the bone density into three categories: low, intermediate, and high, and CBCT intensity values were generated. Based on the consensus of the two observers, 15.6% of sites were of low bone density, 47.9% were of intermediate density, and 36.5% were of high density. Receiver-operating characteristic analysis showed that CBCT intensity values had a high predictive power for predicting high density sites (area under the curve [AUC] =0.94, P < 0.005) and intermediate density sites (AUC = 0.81, P < 0.005). The best cut-off value for intensity to predict intermediate density sites was 218 (sensitivity = 0.77 and specificity = 0.76) and the best cut-off value for intensity to predict high density sites was 403 (sensitivity = 0.93 and specificity = 0.77). CBCT intensity values are considered useful for predicting bone density at posterior mandibular implant sites.

Methods of developing core collections based on the predicted genotypic value of rice ( Oryza sativa L.).

Science.gov (United States)

Li, C T; Shi, C H; Wu, J G; Xu, H M; Zhang, H Z; Ren, Y L

2004-04-01

The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.

Anti-Mullerian hormone is a more accurate predictor of individual time to menopause than mother's age at menopause

NARCIS (Netherlands)

Dolleman, M.; Depmann, M.; Eijkemans, M.J.; Heimensem, J.; Broer, S.L.; Stroom, E.M. van der; Laven, J.S.E.; Rooij, I.A.L.M. van; Scheffer, G.J.; Peeters, P.H.M.; Schouw, Y.T. van der; Lambalk, C.B.; Broekmans, F.J.

2014-01-01

STUDY QUESTION: In the prediction of time to menopause (TTM), what is the added value of anti-Mullerian hormone (AMH) when mother's age at natural menopause (ANM) is also known? SUMMARY ANSWER: AMH is a more accurate predictor of individual TTM than mother's age at menopause. WHAT IS KNOWN ALREADY:

High positive predictive value of Gram stain on catheter-drawn blood samples for the diagnosis of catheter-related bloodstream infection in intensive care neonates.

Science.gov (United States)

Deleers, M; Dodémont, M; Van Overmeire, B; Hennequin, Y; Vermeylen, D; Roisin, S; Denis, O

2016-04-01

Catheter-related bloodstream infections (CRBSIs) remain a leading cause of healthcare-associated infections in preterm infants. Rapid and accurate methods for the diagnosis of CRBSIs are needed in order to implement timely and appropriate treatment. A retrospective study was conducted during a 7-year period (2005-2012) in the neonatal intensive care unit of the University Hospital Erasme to assess the value of Gram stain on catheter-drawn blood samples (CDBS) to predict CRBSIs. Both peripheral samples and CDBS were obtained from neonates with clinically suspected CRBSI. Gram stain, automated culture and quantitative cultures on blood agar plates were performed for each sample. The paired quantitative blood culture was used as the standard to define CRBSI. Out of 397 episodes of suspected CRBSIs, 35 were confirmed by a positive ratio of quantitative culture (>5) or a colony count of CDBS culture >100 colony-forming units (CFU)/mL. All but two of the 30 patients who had a CDBS with a positive Gram stain were confirmed as having a CRBSI. Seven patients who had a CDBS with a negative Gram stain were diagnosed as CRBSI. The sensitivity, specificity, positive predictive value and negative predictive value of Gram stain on CDBS were 80, 99.4, 93.3 and 98.1 %, respectively. Gram staining on CDBS is a viable method for rapidly (<1 h) detecting CRBSI without catheter withdrawal.

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

Science.gov (United States)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

New Danish reference values for spirometry

DEFF Research Database (Denmark)

Løkke, Anders; Marott, Jacob Louis; Mortensen, Jann

2013-01-01

years of age or older with adequate lung function. Results:  We used sex-stratified multiple linear regression analysis to find prediction formulas for forced expiratory volume in 1 s (FEV(1) ), forced vital capacity (FVC) and FEV(1) /FVC adjusted for age and height. The cutoff value of normal lung......Introduction:  International recommendations state that reference values for lung function should derive from cross-sectional studies of healthy nonsmokers and be renewed from time to time because of cohort effect and newer, more accurate, technical equipment. In 1986, the Danish Lung Society...

Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

DEFF Research Database (Denmark)

Christensen, Steen; Doherty, John

2008-01-01

super parameters), and that the structural errors caused by using pilot points and super parameters to parameterize the highly heterogeneous log-transmissivity field can be significant. For the test case much effort is put into studying how the calibrated model's ability to make accurate predictions...

Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

Science.gov (United States)

Li, Longhai; Feng, Cindy X; Qiu, Shi

2017-06-30

An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Prediction of textural attributes using color values of banana (Musa sapientum) during ripening.

Science.gov (United States)

Jaiswal, Pranita; Jha, Shyam Narayan; Kaur, Poonam Preet; Bhardwaj, Rishi; Singh, Ashish Kumar; Wadhawan, Vishakha

2014-06-01

Banana is an important sub-tropical fruit in international trade. It undergoes significant textural and color transformations during ripening process, which in turn influence the eating quality of the fruit. In present study, color ('L', 'a' and 'b' value) and textural attributes of bananas (peel, fruit and pulp firmness; pulp toughness; stickiness) were studied simultaneously using Hunter Color Lab and Texture Analyser, respectively, during ripening period of 10 days at ambient atmosphere. There was significant effect of ripening period on all the considered textural characteristics and color properties of bananas except color value 'b'. In general, textural descriptors (peel, fruit and pulp firmness; and pulp toughness) decreased during ripening except stickiness, while color values viz 'a' and 'b' increased with ripening barring 'L' value. Among various textural attributes, peel toughness and pulp firmness showed highest correlation (r) with 'a' value of banana peel. In order to predict textural properties using color values of banana, five types of equations (linear/polynomial/exponential/logarithmic/power) were fitted. Among them, polynomial equation was found to be the best fit (highest coefficient of determination, R(2)) for prediction of texture using color properties for bananas. The pulp firmness, peel toughness and pulp toughness showed R(2) above 0.84 with indicating its potentiality of the fitted equations for prediction of textural profile of bananas non-destructively using 'a' value.

Predicting suitable optoelectronic properties of monoclinic VON semiconductor crystals for photovoltaics using accurate first-principles computations

KAUST Repository

Harb, Moussab

2015-01-01

Using accurate first-principles quantum calculations based on DFT (including the perturbation theory DFPT) with the range-separated hybrid HSE06 exchange-correlation functional, we predict essential fundamental properties (such as bandgap, optical absorption coefficient, dielectric constant, charge carrier effective masses and exciton binding energy) of two stable monoclinic vanadium oxynitride (VON) semiconductor crystals for solar energy conversion applications. In addition to the predicted band gaps in the optimal range for making single-junction solar cells, both polymorphs exhibit relatively high absorption efficiencies in the visible range, high dielectric constants, high charge carrier mobilities and much lower exciton binding energies than the thermal energy at room temperature. Moreover, their optical absorption, dielectric and exciton dissociation properties are found to be better than those obtained for semiconductors frequently utilized in photovoltaic devices like Si, CdTe and GaAs. These novel results offer a great opportunity for this stoichiometric VON material to be properly synthesized and considered as a new good candidate for photovoltaic applications.

Predicting suitable optoelectronic properties of monoclinic VON semiconductor crystals for photovoltaics using accurate first-principles computations

KAUST Repository

Harb, Moussab

2015-08-26

Using accurate first-principles quantum calculations based on DFT (including the perturbation theory DFPT) with the range-separated hybrid HSE06 exchange-correlation functional, we predict essential fundamental properties (such as bandgap, optical absorption coefficient, dielectric constant, charge carrier effective masses and exciton binding energy) of two stable monoclinic vanadium oxynitride (VON) semiconductor crystals for solar energy conversion applications. In addition to the predicted band gaps in the optimal range for making single-junction solar cells, both polymorphs exhibit relatively high absorption efficiencies in the visible range, high dielectric constants, high charge carrier mobilities and much lower exciton binding energies than the thermal energy at room temperature. Moreover, their optical absorption, dielectric and exciton dissociation properties are found to be better than those obtained for semiconductors frequently utilized in photovoltaic devices like Si, CdTe and GaAs. These novel results offer a great opportunity for this stoichiometric VON material to be properly synthesized and considered as a new good candidate for photovoltaic applications.

A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

Directory of Open Access Journals (Sweden)

Magdalena Ydreborg

Full Text Available Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI in the paper, was based on the model: Log-odds (predicting cirrhosis = -12.17+ (age × 0.11 + (BMI (kg/m(2 × 0.23 + (D7-lathosterol (μg/100 mg cholesterol×(-0.013 + (Platelet count (x10(9/L × (-0.018 + (Prothrombin-INR × 3.69. The area under the ROC curve (AUROC for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96. The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98. In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

The energy and protein value of wheat, maize and blend DDGS for cattle and evaluation of prediction methods.

Science.gov (United States)

De Boever, J L; Blok, M C; Millet, S; Vanacker, J; De Campeneere, S

2014-11-01

The chemical composition inclusive amino acids (AAs) and the energy and protein value of three wheat, three maize and seven blend (mainly wheat) dried distillers grains and solubles (DDGS) were determined. The net energy for lactation (NEL) was derived from digestion coefficients obtained with sheep. The digestible protein in the intestines (DVE) and the degraded protein balance (OEB) were determined by nylon bag incubations in the rumen and the intestines of cannulated cows. Additional chemical parameters like acid-detergent insoluble CP (ADICP), protein solubility in water, in borate-phosphate buffer and in pepsin-HCl, in vitro digestibility (cellulase, protease, rumen fluid) and colour scores (L*, a*, b*) were evaluated as potential predictors of the energy and protein value. Compared to wheat DDGS (WDDGS), maize DDGS (MDDGS) had a higher NEL-value (8.49 v. 7.38 MJ/kg DM), a higher DVE-content (216 v. 198 g/kg DM) and a lower OEB-value (14 v. 66 g/kg DM). The higher energy value of MDDGS was mainly due to the higher crude fat (CFA) content (145 v. 76 g/kg DM) and also to better digestible cell-walls, whereas the higher protein value was mainly due to the higher percentage of rumen bypass protein (RBP: 69.8 v. 55.6%). The NEL-value of blend DDGS (BDDGS) was in between that of the pure DDGS-types, whereas its DVE-value was similar to MDDGS. Although lower in CP and total AAs, MDDGS provided a similar amount of essential AAs as the other DDGS-types. Lysine content was most reduced in the production of WDDGS and cysteine in MDDGS. Fat content explained 68.6% of the variation in NEL, with hemicellulose and crude ash as extra explaining variables. The best predictor for RBP as well as for OEB was the protein solubility in pepsin-HCl (R 2=77.3% and 83.5%). Intestinal digestibility of RBP could best be predicted by ADF (R 3=73.6%) and the combination of CFA and NDF could explain 60.2% of the variation in the content of absorbable microbial protein. The availability of

Influence of coronary artery disease prevalence on predictive values of coronary CT angiography: a meta-regression analysis

Energy Technology Data Exchange (ETDEWEB)

Schlattmann, Peter [University Hospital of Friedrich-Schiller University Jena, Department of Medical Statistics, Informatics and Documentation, Jena (Germany); Schuetz, Georg M. [Freie Universitaet Berlin, Charite, Medical School, Department of Radiology, Humboldt-Universitaet zu Berlin, Berlin (Germany); Dewey, Marc [Freie Universitaet Berlin, Charite, Medical School, Department of Radiology, Humboldt-Universitaet zu Berlin, Berlin (Germany); Charite, Institut fuer Radiologie, Berlin (Germany)

2011-09-15

To evaluate the impact of coronary artery disease (CAD) prevalence on the predictive values of coronary CT angiography. We performed a meta-regression based on a generalised linear mixed model using the binomial distribution and a logit link to analyse the influence of the prevalence of CAD in published studies on the per-patient negative and positive predictive values of CT in comparison to conventional coronary angiography as the reference standard. A prevalence range in which the negative predictive value was higher than 90%, while at the same time the positive predictive value was higher than 70% was considered appropriate. The summary negative and positive predictive values of coronary CT angiography were 93.7% (95% confidence interval [CI] 92.8-94.5%) and 87.5% (95% CI, 86.5-88.5%), respectively. With 95% confidence, negative and positive predictive values higher than 90% and 70% were available with CT for a CAD prevalence of 18-63%. CT systems with >16 detector rows met these requirements for the positive (P < 0.01) and negative (P < 0.05) predictive values in a significantly broader range than systems with {<=}16 detector rows. It is reasonable to perform coronary CT angiography as a rule-out test in patients with a low-to-intermediate likelihood of disease. (orig.)

Influence of coronary artery disease prevalence on predictive values of coronary CT angiography: a meta-regression analysis

International Nuclear Information System (INIS)

Schlattmann, Peter; Schuetz, Georg M.; Dewey, Marc

2011-01-01

To evaluate the impact of coronary artery disease (CAD) prevalence on the predictive values of coronary CT angiography. We performed a meta-regression based on a generalised linear mixed model using the binomial distribution and a logit link to analyse the influence of the prevalence of CAD in published studies on the per-patient negative and positive predictive values of CT in comparison to conventional coronary angiography as the reference standard. A prevalence range in which the negative predictive value was higher than 90%, while at the same time the positive predictive value was higher than 70% was considered appropriate. The summary negative and positive predictive values of coronary CT angiography were 93.7% (95% confidence interval [CI] 92.8-94.5%) and 87.5% (95% CI, 86.5-88.5%), respectively. With 95% confidence, negative and positive predictive values higher than 90% and 70% were available with CT for a CAD prevalence of 18-63%. CT systems with >16 detector rows met these requirements for the positive (P < 0.01) and negative (P < 0.05) predictive values in a significantly broader range than systems with ≤16 detector rows. It is reasonable to perform coronary CT angiography as a rule-out test in patients with a low-to-intermediate likelihood of disease. (orig.)

Genomic Prediction from Whole Genome Sequence in Livestock: The 1000 Bull Genomes Project

DEFF Research Database (Denmark)

Hayes, Benjamin J; MacLeod, Iona M; Daetwyler, Hans D

Advantages of using whole genome sequence data to predict genomic estimated breeding values (GEBV) include better persistence of accuracy of GEBV across generations and more accurate GEBV across breeds. The 1000 Bull Genomes Project provides a database of whole genome sequenced key ancestor bulls....... In a dairy data set, predictions using BayesRC and imputed sequence data from 1000 Bull Genomes were 2% more accurate than with 800k data. We could demonstrate the method identified causal mutations in some cases. Further improvements will come from more accurate imputation of sequence variant genotypes...

Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

Science.gov (United States)

Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

2016-01-01

Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

Neural networks for predicting breeding values and genetic gains

Directory of Open Access Journals (Sweden)

Gabi Nunes Silva

2014-12-01

Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study.

Science.gov (United States)

Escarela, Gabriel

2012-06-01

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.

Towards accurate T-3He Q-value

Energy Technology Data Exchange (ETDEWEB)

Eronen, Tommi; Hoecker, Martin; Ketter, Jochen; Streubel, Sebastian; Blaum, Klaus [Max-Planck Institut fuer Kernphysik, Heidelberg (Germany); Van Dyck, Robert S. Jr. [Department of Physics, University of Washington, Seattle, WA (United States)

2013-07-01

Great efforts have been put forward to determine the neutrino mass from tritium β decay. The most prominent experimental setup, KATRIN, is expected to deliver an upper limit to the neutrino mass that is one order of magnitude more stringent than the current value by measuring the endpoint and the shape of the β spectrum of the tritium decay. The endpoint energy (assuming zero neutrino mass) can also be deduced from the Q-value of the decay by measuring the mass difference of tritium and the daughter {sup 3}He using high-resolution mass spectrometry. Such a measurement would give an excellent, independent calibration point for the KATRIN experiment to deduce its systematics. Our mass-difference measurement utilizes the Tritium- Helium double Penning trap (THe-Trap) setup. Based on the anharmonic cyclotron frequency determination method pioneered at the University of Washington, Seattle, precision at the level of 1 part in 10{sup 11} in the T/{sup 3}He mass ratio is expected. In this contribution, I describe the motivation, the principle, current status, and expectations of the experiment.

Predicting community sensitivity to ozone, using Ellenberg Indicator values

Energy Technology Data Exchange (ETDEWEB)

Jones, M. Laurence M. [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: lj@ceh.ac.uk; Hayes, Felicity [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: fhay@ceh.ac.uk; Mills, Gina [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: gmi@ceh.ac.uk; Sparks, Tim H. [Centre for Ecology and Hydrology Monks Wood, Abbots Ripton, Huntingdon, Cambridgeshire PE28 2LS (United Kingdom)]. E-mail: ths@ceh.ac.uk; Fuhrer, Juerg [Swiss Federal Research Station for Agroecology and Agriculture (FAL), Air Pollution/Climate Group, Reckenholzstrasse 191, CH-8046 Zurich (Switzerland)]. E-mail: juerg.fuhrer@fal.admin.ch

2007-04-15

This paper develops a regression-based model for predicting changes in biomass of individual species exposed to ozone (RS{sub p}), based on their Ellenberg Indicator values. The equation (RS{sub p}=1.805-0.118Light-0.135Salinity) underpredicts observed sensitivity but has the advantage of widespread applicability to almost 3000 European species. The model was applied to grassland communities to develop two further predictive tools. The first tool, percentage change in biomass (ORI%) was tested on data from a field-based ozone exposure experiment and predicted a 27% decrease in biomass over 5 years compared with an observed decrease of 23%. The second tool, an index of community sensitivity to ozone (CORI), was applied to 48 grassland communities and suggests that community sensitivity to ozone is primarily species-driven. A repeat-sampling routine showed that nine species were the minimum requirement to estimate CORI within 5%.

Can administrative health utilisation data provide an accurate diabetes prevalence estimate for a geographical region?

Science.gov (United States)

Chan, Wing Cheuk; Papaconstantinou, Dean; Lee, Mildred; Telfer, Kendra; Jo, Emmanuel; Drury, Paul L; Tobias, Martin

2018-05-01

To validate the New Zealand Ministry of Health (MoH) Virtual Diabetes Register (VDR) using longitudinal laboratory results and to develop an improved algorithm for estimating diabetes prevalence at a population level. The assigned diabetes status of individuals based on the 2014 version of the MoH VDR is compared to the diabetes status based on the laboratory results stored in the Auckland regional laboratory result repository (TestSafe) using the New Zealand diabetes diagnostic criteria. The existing VDR algorithm is refined by reviewing the sensitivity and positive predictive value of the each of the VDR algorithm rules individually and as a combination. The diabetes prevalence estimate based on the original 2014 MoH VDR was 17% higher (n = 108,505) than the corresponding TestSafe prevalence estimate (n = 92,707). Compared to the diabetes prevalence based on TestSafe, the original VDR has a sensitivity of 89%, specificity of 96%, positive predictive value of 76% and negative predictive value of 98%. The modified VDR algorithm has improved the positive predictive value by 6.1% and the specificity by 1.4% with modest reductions in sensitivity of 2.2% and negative predictive value of 0.3%. At an aggregated level the overall diabetes prevalence estimated by the modified VDR is 5.7% higher than the corresponding estimate based on TestSafe. The Ministry of Health Virtual Diabetes Register algorithm has been refined to provide a more accurate diabetes prevalence estimate at a population level. The comparison highlights the potential value of a national population long term condition register constructed from both laboratory results and administrative data. Copyright © 2018 Elsevier B.V. All rights reserved.

Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

Directory of Open Access Journals (Sweden)

G Khalili-Zadeh-Mahani

2016-07-01

Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

Predicted osteotomy planes are accurate when using patient-specific instrumentation for total knee arthroplasty in cadavers: a descriptive analysis.

Science.gov (United States)

Kievit, A J; Dobbe, J G G; Streekstra, G J; Blankevoort, L; Schafroth, M U

2018-06-01

Malalignment of implants is a major source of failure during total knee arthroplasty. To achieve more accurate 3D planning and execution of the osteotomy cuts during surgery, the Signature (Biomet, Warsaw) patient-specific instrumentation (PSI) was used to produce pin guides for the positioning of the osteotomy blocks by means of computer-aided manufacture based on CT scan images. The research question of this study is: what is the transfer accuracy of osteotomy planes predicted by the Signature PSI system for preoperative 3D planning and intraoperative block-guided pin placement to perform total knee arthroplasty procedures? The transfer accuracy achieved by using the Signature PSI system was evaluated by comparing the osteotomy planes predicted preoperatively with the osteotomy planes seen intraoperatively in human cadaveric legs. Outcomes were measured in terms of translational and rotational errors (varus, valgus, flexion, extension and axial rotation) for both tibia and femur osteotomies. Average translational errors between the osteotomy planes predicted using the Signature system and the actual osteotomy planes achieved was 0.8 mm (± 0.5 mm) for the tibia and 0.7 mm (± 4.0 mm) for the femur. Average rotational errors in relation to predicted and achieved osteotomy planes were 0.1° (± 1.2°) of varus and 0.4° (± 1.7°) of anterior slope (extension) for the tibia, and 2.8° (± 2.0°) of varus and 0.9° (± 2.7°) of flexion and 1.4° (± 2.2°) of external rotation for the femur. The similarity between osteotomy planes predicted using the Signature system and osteotomy planes actually achieved was excellent for the tibia although some discrepancies were seen for the femur. The use of 3D system techniques in TKA surgery can provide accurate intraoperative guidance, especially for patients with deformed bone, tailored to individual patients and ensure better placement of the implant.

The accuracy of new wheelchair users' predictions about their future wheelchair use.

Science.gov (United States)

Hoenig, Helen; Griffiths, Patricia; Ganesh, Shanti; Caves, Kevin; Harris, Frances

2012-06-01

This study examined the accuracy of new wheelchair user predictions about their future wheelchair use. This was a prospective cohort study of 84 community-dwelling veterans provided a new manual wheelchair. The association between predicted and actual wheelchair use was strong at 3 mos (ϕ coefficient = 0.56), with 90% of those who anticipated using the wheelchair at 3 mos still using it (i.e., positive predictive value = 0.96) and 60% of those who anticipated not using it indeed no longer using the wheelchair (i.e., negative predictive value = 0.60, overall accuracy = 0.92). Predictive accuracy diminished over time, with overall accuracy declining from 0.92 at 3 mos to 0.66 at 6 mos. At all time points, and for all types of use, patients better predicted use as opposed to disuse, with correspondingly higher positive than negative predictive values. Accuracy of prediction of use in specific indoor and outdoor locations varied according to location. This study demonstrates the importance of better understanding the potential mismatch between the anticipated and actual patterns of wheelchair use. The findings suggest that users can be relied upon to accurately predict their basic wheelchair-related needs in the short-term. Further exploration is needed to identify characteristics that will aid users and their providers in more accurately predicting mobility needs for the long-term.

Computational Techniques for Model Predictive Control of Large-Scale Systems with Continuous-Valued and Discrete-Valued Inputs

Directory of Open Access Journals (Sweden)

Koichi Kobayashi

2013-01-01

Full Text Available We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.

Positive Predictive Value of BI-RADS Categorization in an Asian Population

Directory of Open Access Journals (Sweden)

Yah-Yuen Tan

2004-07-01

Full Text Available The Breast Imaging Reporting And Data System (BI-RADS categorization of mammograms is useful in estimating the risk of malignancy, thereby guiding management decisions. However, in Asian women, in whom breast density is increased, the sensitivity of mammography is correspondingly lower. We sought to determine the positive predictive value of BI-RADS categorization for malignancy in our Asian population and, hence, its value in helping us to choose between the various modalities for breast biopsy. We retrospectively reviewed all patients with occult breast lesions detected on mammography or ultrasound who underwent needle-localization open breast biopsy (NLOB in our institution over a 6-year period. There were 470 biopsies in 427 patients; 16% of lesions were malignant. The positive predictive value of BI-RADS 4 and 5 lesions for cancer was 0.27 and 0.84, respectively. While most BI-RADS 5 mass lesions were invasive cancers, the majority of calcifications in this category were in situ carcinomas. We conclude that BI-RADS remains useful in aiding decision-making for biopsy in our Asian population. Based on positive predictive values, we recommend percutaneous breast biopsy for initial evaluation of lesions categorized as BI-RADS 4 or less. For BI-RADS 5 lesions with microcalcifications, open surgical biopsy as a diagnostic and therapeutic procedure may be more appropriate. In the case of a BI-RADS 5 lesion associated with a mass, initial percutaneous biopsy may be useful for diagnosis, followed by a planned single-stage surgical procedure as necessary.

Determinants of work ability and its predictive value for disability

NARCIS (Netherlands)

Alavinia, S. M.; de Boer, A. G. E. M.; Van Duivenbooden, J. C.; Frings-Dresen, M. H. W.; Burdorf, A.

2009-01-01

Background Maintaining the ability of workers to cope with physical and psychosocial demands at work becomes increasingly important in prolonging working life. Aims To analyse the effects of work-related factors and individual characteristics on work ability and to determine the predictive value of

Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

Science.gov (United States)

Bednarcyk, Brett A.; Arnold, Steven M.

2002-01-01

NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

Predictive value of IgE/IgG4 antibody ratio in children with egg allergy

Directory of Open Access Journals (Sweden)

Okamoto Shindou

2012-06-01

Full Text Available Abstract Background The aim of this study was to investigate the role of specific IgG4 antibodies to hen’s egg white and determine their utility as a marker for the outcome of oral challenge test in children sensitized to hen’s egg Methods The hen’s egg oral food challenge test was performed in 105 sensitized children without atopic dermatitis, and the titers of egg white-specific immunoglobulin G4 (IgG4 and immunoglobulin E (IgE antibodies were measured. To set the cut-off values of IgG4, IgE, and the IgE/IgG4 ratio for predicting positive results in oral challenges, receiver operating characteristic curves were plotted and the area under the curves (AUC were calculated. Results Sixty-four of 105 oral challenges with whole eggs were assessed as positive. The AUC for IgE, IgG4, and IgE/IgG4 for the prediction of positive results were 0.609, 0.724, and 0.847, respectively. Thus, the IgE/IgG4 ratio generated significantly higher specificity, sensitivity, positive predictive value (%, and negative predictive value (% than the individual IgE and IgG4. The negative predictive value of the IgE/IgG4 ratio was 90% at a value of 1. Conclusions We have demonstrated that the egg white-specific serum IgE/IgG4 ratio is important for predicting reactivity to egg during food challenges.

Forming limit prediction by an evolving non-quadratic yield criterion considering the anisotropic hardening and r-value evolution

Science.gov (United States)

Lian, Junhe; Shen, Fuhui; Liu, Wenqi; Münstermann, Sebastian

2018-05-01

The constitutive model development has been driven to a very accurate and fine-resolution description of the material behaviour responding to various environmental variable changes. The evolving features of the anisotropic behaviour during deformation, therefore, has drawn particular attention due to its possible impacts on the sheet metal forming industry. An evolving non-associated Hill48 (enHill48) model was recently proposed and applied to the forming limit prediction by coupling with the modified maximum force criterion. On the one hand, the study showed the significance to include the anisotropic evolution for accurate forming limit prediction. On the other hand, it also illustrated that the enHill48 model introduced an instability region that suddenly decreases the formability. Therefore, in this study, an alternative model that is based on the associated flow rule and provides similar anisotropic predictive capability is extended to chapter the evolving effects and further applied to the forming limit prediction. The final results are compared with experimental data as well as the results by enHill48 model.

Final Technical Report: Increasing Prediction Accuracy.

Energy Technology Data Exchange (ETDEWEB)

King, Bruce Hardison [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stein, Joshua [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-12-01

PV performance models are used to quantify the value of PV plants in a given location. They combine the performance characteristics of the system, the measured or predicted irradiance and weather at a site, and the system configuration and design into a prediction of the amount of energy that will be produced by a PV system. These predictions must be as accurate as possible in order for finance charges to be minimized. Higher accuracy equals lower project risk. The Increasing Prediction Accuracy project at Sandia focuses on quantifying and reducing uncertainties in PV system performance models.

Valuing hydrological forecasts for a pumped storage assisted hydro facility

Science.gov (United States)

Zhao, Guangzhi; Davison, Matt

2009-07-01

SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

Science.gov (United States)

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error model predicts the exposure of

Suboptimal Choice in Pigeons: Stimulus Value Predicts Choice over Frequencies.

Directory of Open Access Journals (Sweden)

Aaron P Smith

Full Text Available Pigeons have shown suboptimal gambling-like behavior when preferring a stimulus that infrequently signals reliable reinforcement over alternatives that provide greater reinforcement overall. As a mechanism for this behavior, recent research proposed that the stimulus value of alternatives with more reliable signals for reinforcement will be preferred relatively independently of their frequencies. The present study tested this hypothesis using a simplified design of a Discriminative alternative that, 50% of the time, led to either a signal for 100% reinforcement or a blackout period indicative of 0% reinforcement against a Nondiscriminative alternative that always led to a signal that predicted 50% reinforcement. Pigeons showed a strong preference for the Discriminative alternative that remained despite reducing the frequency of the signal for reinforcement in subsequent phases to 25% and then 12.5%. In Experiment 2, using the original design of Experiment 1, the stimulus following choice of the Nondiscriminative alternative was increased to 75% and then to 100%. Results showed that preference for the Discriminative alternative decreased only when the signals for reinforcement for the two alternatives predicted the same probability of reinforcement. The ability of several models to predict this behavior are discussed, but the terminal link stimulus value offers the most parsimonious account of this suboptimal behavior.

Use of multiple genetic markers in prediction of breeding values.

NARCIS (Netherlands)

Arendonk, van J.A.M.; Tier, B.; Kinghorn, B.P.

1994-01-01

Genotypes at a marker locus give information on transmission of genes from parents to offspring and that information can be used in predicting the individuals' additive genetic value at a linked quantitative trait locus (MQTL). In this paper a recursive method is presented to build the gametic

[Prediction of SPAD value in oilseed rape leaves using hyperspectral imaging technique].

Science.gov (United States)

Ding, Xi-bin; Liu, Fei; Zhang, Chu; He, Yong

2015-02-01

In the present work, prediction models of SPAD value (Soil and Plant Analyzer Development, often used as a parameter to indicate chlorophyll content) in oilseed rape leaves were successfully built using hyperspectral imaging technique. The hy perspectral images of 160 oilseed rape leaf samples in the spectral range of 380-1030 nm were acquired. Average spectrum was extracted from the region of interest (ROI) of each sample. We chose spectral data in the spectral range of 500-900 nm for analysis. Using Monte Carlo partial least squares(MC-PLS) algorithm, 13 samples were identified as outliers and eliminated. Based on the spectral information and measured SPAD values of the rest 147 samples, several estimation models have been built based on different parameters using different algorithms for comparison, including: (1) a SPAD value estimation model based on partial least squares(PLS) in the whole wavelength region of 500-900 nm; (2) a SPAD value estimation model based on successive projections algorithmcombined with PLS(SPA-PLS); (3) 4 kind of simple experience SPAD value estimation models in which red edge position was used as an argument; (4) 4 kind of simple experience SPAD value estimation models in which three vegetation indexes R710/R760, (R750-R705)/(R750-R705) and R860/(R550 x R708), which all have been proved to have a good relevance with chlorophyll content, were used as an argument respectively; (5) a SPAD value estimation model based on PLS using the 3 vegetation indexes mentioned above. The results indicate that the optimal prediction performance is achieved by PLS model in the whole wavelength region of 500-900 nm, which has a correlation coefficient(r(p)) of 0.8339 and a root mean squares error of predicted (RMSEP) of 1.52. The SPA-PLS model can provide avery close prediction result while the calibration computation has been significantly reduced and the calibration speed has been accelerated sharply. For simple experience models based on red edge

Spectrally accurate contour dynamics

International Nuclear Information System (INIS)

Van Buskirk, R.D.; Marcus, P.S.

1994-01-01

We present an exponentially accurate boundary integral method for calculation the equilibria and dynamics of piece-wise constant distributions of potential vorticity. The method represents contours of potential vorticity as a spectral sum and solves the Biot-Savart equation for the velocity by spectrally evaluating a desingularized contour integral. We use the technique in both an initial-value code and a newton continuation method. Our methods are tested by comparing the numerical solutions with known analytic results, and it is shown that for the same amount of computational work our spectral methods are more accurate than other contour dynamics methods currently in use

[Predictive value of Hodgkin's lymphoma tumor burden in present].

Science.gov (United States)

Kulyova, S A; Karitsky, A P

2014-01-01

Today approximately 70% of patients with Hodgkin lymphoma can be cured with the combined-modality therapy. Tumor burden, the importance of which was demonstrated 15 years ago for the first time, is a powerful prognostic factor. Data of literature of representations on predictive value of Hodgkin's lymphoma tumor burden are shown in the article. The difficult immunological relations between tumor cells and reactive ones lead to development of the main symptoms. Nevertheless, the collective sign of tumor burden shows the greatest influence on survival and on probability of resistance, which relative risk can be predicted on this variable and treatment program. Patients with bulky disease need escalated therapy with high-dose chemotherapy. Integration into predictive models of the variable will change an expected contribution of clinical and laboratory parameters in the regression analyses constructed on patients with Hodgkin's lymphoma. Today the role of diagnostic functional methods, in particular a positron emission tomography, for metabolic active measurement is conducted which allows excluding a reactive component.

Estimating patient dose from CT exams that use automatic exposure control: Development and validation of methods to accurately estimate tube current values.

Science.gov (United States)

McMillan, Kyle; Bostani, Maryam; Cagnon, Christopher H; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H; McNitt-Gray, Michael F

2017-08-01

The vast majority of body CT exams are performed with automatic exposure control (AEC), which adapts the mean tube current to the patient size and modulates the tube current either angularly, longitudinally or both. However, most radiation dose estimation tools are based on fixed tube current scans. Accurate estimates of patient dose from AEC scans require knowledge of the tube current values, which is usually unavailable. The purpose of this work was to develop and validate methods to accurately estimate the tube current values prescribed by one manufacturer's AEC system to enable accurate estimates of patient dose. Methods were developed that took into account available patient attenuation information, user selected image quality reference parameters and x-ray system limits to estimate tube current values for patient scans. Methods consistent with AAPM Report 220 were developed that used patient attenuation data that were: (a) supplied by the manufacturer in the CT localizer radiograph and (b) based on a simulated CT localizer radiograph derived from image data. For comparison, actual tube current values were extracted from the projection data of each patient. Validation of each approach was based on data collected from 40 pediatric and adult patients who received clinically indicated chest (n = 20) and abdomen/pelvis (n = 20) scans on a 64 slice multidetector row CT (Sensation 64, Siemens Healthcare, Forchheim, Germany). For each patient dataset, the following were collected with Institutional Review Board (IRB) approval: (a) projection data containing actual tube current values at each projection view, (b) CT localizer radiograph (topogram) and (c) reconstructed image data. Tube current values were estimated based on the actual topogram (actual-topo) as well as the simulated topogram based on image data (sim-topo). Each of these was compared to the actual tube current values from the patient scan. In addition, to assess the accuracy of each method in estimating

Getting off on the right foot: subjective value versus economic value in predicting longitudinal job outcomes from job offer negotiations.

Science.gov (United States)

Curhan, Jared R; Elfenbein, Hillary Anger; Kilduff, Gavin J

2009-03-01

Although negotiation experiences can affect a negotiator's ensuing attitudes and behavior, little is known about their long-term consequences. Using a longitudinal survey design, the authors tested the degree to which economic and subjective value achieved in job offer negotiations predicts employees' subsequent job attitudes and intentions concerning turnover. Results indicate that subjective value predicts greater compensation satisfaction and job satisfaction and lower turnover intention measured 1 year later. Surprisingly, the economic outcomes that negotiators achieved had no apparent effects on these factors. Implications, limitations, and future directions are discussed. (c) 2009 APA, all rights reserved.

Evaluation of preoperative predictive values of serum CA15-3 and ...

African Journals Online (AJOL)

Evaluation of preoperative predictive values of serum CA15-3 and CEA within Sudanese ... Sudan Journal of Medical Sciences ... Design and setting: This case control study was conducted in Khartoum Teaching Hospital, Khartoum, Sudan.

Value of IgA tTG in Predicting Mucosal Recovery in Children with Celiac Disease on a Gluten Free Diet

Science.gov (United States)

Leonard, Maureen M.; Weir, Dascha C.; DeGroote, Maya; Mitchell, Paul D.; Singh, Prashant; Silvester, Jocelyn A.; Leichtner, Alan M.; Fasano, Alessio

2017-01-01

Objective Our objective was to determine the rate of mucosal recovery in pediatric patients with celiac disease on a gluten free diet. We also sought to determine whether IgA tissue transglutaminase (tTG) correlates with mucosal damage at the time of a repeat endoscopy with duodenal biopsy in these patients. Methods We performed a retrospective chart review of one-hundred and three pediatric patients, under 21 years of age, with a diagnosis of celiac disease defined as Marsh 3 histology, and who underwent a repeat endoscopy with duodenal biopsy at least twelve months after initiating a gluten free diet. Results We found that 19% of pediatric patients treated with a gluten free diet had persistent enteropathy. At the time of the repeat biopsy, tTG was elevated in 43% of cases with persistent enteropathy and 32% of cases in which there was mucosal recovery. Overall the positive predictive value of the autoantibody tissue transglutaminase was 25% and the negative predictive value was 83% in patients on a gluten free diet for a median of 2.4 years. Conclusions Nearly one in five children with celiac disease in our population had persistent enteropathy despite maintaining a gluten free diet and IgA tTG was not an accurate marker of mucosal recovery. Neither the presence of symptoms nor positive serology were predictive of a patient’s histology at the time of repeat biopsy. These findings suggest a revisitation of monitoring and management criteria of celiac disease in childhood. PMID:28112686

Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

Science.gov (United States)

Lin, Jing-Wen

2016-01-01

Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

Energy Technology Data Exchange (ETDEWEB)

Richieri, Raphaelle; Lancon, Christophe [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); Boyer, Laurent [La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Department of Public Health, Marseille (France); Farisse, Jean [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); Colavolpe, Cecile; Mundler, Olivier [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Guedj, Eric [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Hopital de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Marseille Cedex 5 (France)

2011-09-15

The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of {sup 99m}Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p < 0.001, uncorrected). Of the patients, 18 (54.5%) were responders to rTMS and 15 were non-responders (45.5%). There were no statistically significant differences in demographic and clinical characteristics (p > 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

Can Hounsfield Unit Value Predict Type of Urinary Stones?

Directory of Open Access Journals (Sweden)

Alper Gok

2014-03-01

Full Text Available Aim: Aim of this study is to determine the role of Hounsfield unit (HU in predicting results of stone analysis. Material and Method: This study included 199 patients to whom percutaneous nephrolithotomy (PNL procedures were applied between January 2008 and May 2011 in our clinic. Before the procedure HU values of kidney stones were measured using non-contrast computed tomography. After the operation, obtained stone samples were analysed using X-ray diffraction technique. HU values were compared with stone analysis results. Results: Stone analysis revealed eight different stone types. Distribution of stone types and HU value ranges were as follows: 85% calcium oxalate monohydrate, 730-1130 HU; 38% calcium oxalate dihydrate, 510-810 HU; 21% uric acid, 320-550 HU; 23% struvite, 614-870 HU; 7% calcium hydrogene phosphate, 1100-1365 HU; 3% cystine, 630-674 HU; 15% mixed uric acid plus calcium oxalate, 499-840 HU; and 7% mixed cystine plus calcium phosphate, 430-520 HU. HU values of all stone types ranged between 320 and 1365. There was a statistically significant relation between HU values of uric acid and non uric acid stones (p

Highly accurate surface maps from profilometer measurements

Science.gov (United States)

Medicus, Kate M.; Nelson, Jessica D.; Mandina, Mike P.

2013-04-01

Many aspheres and free-form optical surfaces are measured using a single line trace profilometer which is limiting because accurate 3D corrections are not possible with the single trace. We show a method to produce an accurate fully 2.5D surface height map when measuring a surface with a profilometer using only 6 traces and without expensive hardware. The 6 traces are taken at varying angular positions of the lens, rotating the part between each trace. The output height map contains low form error only, the first 36 Zernikes. The accuracy of the height map is ±10% of the actual Zernike values and within ±3% of the actual peak to valley number. The calculated Zernike values are affected by errors in the angular positioning, by the centering of the lens, and to a small effect, choices made in the processing algorithm. We have found that the angular positioning of the part should be better than 1?, which is achievable with typical hardware. The centering of the lens is essential to achieving accurate measurements. The part must be centered to within 0.5% of the diameter to achieve accurate results. This value is achievable with care, with an indicator, but the part must be edged to a clean diameter.

Clinical value of endoluminal ultrasonography in the diagnosis of rectovaginal fistula

International Nuclear Information System (INIS)

Yin, Hao-Qiang; Wang, Chen; Peng, Xin; Xu, Fang; Ren, Ya-Juan; Chao, Yong-Qing; Lu, Jin-Gen; Wang, Song; Xiao, Hu-Sheng

2016-01-01

Rectovaginal fistula (RVF) refers to a pathological passage between the rectum and vagina, which is a public health challenge. This study was aimed to explore the clinical value of endoluminal biplane ultrasonography in the diagnosis of rectovaginal fistula (RVF). Thirty inpatients and outpatients with suspected RVF from January 2006 to June 2013 were included in the study, among whom 28 underwent surgical repair. All 28 patients underwent preoperative endoluminal ultrasonography, and the obtained diagnostic results were compared with the corresponding surgical results. All of the internal openings located at the anal canal and rectum of the 28 patients and confirmed during surgery were revealed by preoperative endosonography, which showed a positive predictive value of 100 %. Regarding the 30 internal openings located in the vagina during surgery, the positive predictive value of preoperative endosonography was 93 %. The six cases of simple fistulas confirmed during surgery were revealed by endosonography; for the 22 cases of complex fistula confirmed during surgery, the positive predictive value of endosonography was 90 %. Surgery confirmed 14 cases of anal fistula and 14 cases of RVF, whereas preoperative endoluminal ultrasonography suggested 16 cases of anal fistula and 12 cases of RVF, resulting in positive predictive values of 92.3 and 93 %, respectively. The use of endoluminal biplane ultrasonography in the diagnosis of RVF can accurately determine the internal openings in the rectum or vagina and can relatively accurately identify concomitant branches and abscesses located in the rectovaginal septum. Thus, it is a good imaging tool for examining internal and external anal sphincter injuries and provides useful information for preoperative preparation and postoperative evaluation

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Science.gov (United States)

Xie, Tian; Grossman, Jeffrey C.

2018-04-01

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

Predictive value of cognition for different domains of outcome in recent-onset schizophrenia.

Science.gov (United States)

Holthausen, Esther A E; Wiersma, Durk; Cahn, Wiepke; Kahn, René S; Dingemans, Peter M; Schene, Aart H; van den Bosch, Robert J

2007-01-15

The aim of this study was to see whether and how cognition predicts outcome in recent-onset schizophrenia in a large range of domains such as course of illness, self-care, interpersonal functioning, vocational functioning and need for care. At inclusion, 115 recent-onset patients were tested on a cognitive battery and 103 patients participated in the follow-up 2 years after inclusion. Differences in outcome between cognitively normal and cognitively impaired patients were also analysed. Cognitive measures at inclusion did not predict number of relapses, activities of daily living and interpersonal functioning. Time in psychosis or in full remission, as well as need for care, were partly predicted by specific cognitive measures. Although statistically significant, the predictive value of cognition with regard to clinical outcome was limited. There was a significant difference between patients with and without cognitive deficits in competitive employment status and vocational functioning. The predictive value of cognition for different social outcome domains varies. It seems that cognition most strongly predicts work performance, where having a cognitive deficit, regardless of the nature of the deficit, acts as a rate-limiting factor.

Impact of the endoscopist's experience on the negative predictive value of capsule endoscopy.

Science.gov (United States)

Velayos Jiménez, Benito; Alcaide Suárez, Noelia; González Redondo, Guillermo; Fernández Salazar, Luis; Aller de la Fuente, Rocío; Del Olmo Martínez, Lourdes; Ruiz Rebollo, Lourdes; González Hernández, José Manuel

2017-01-01

The impact of the accumulated experience of the capsule endoscopy (CE) reader on the accuracy of this test is discussed. To determine whether the negative predictive value of CE findings changes along the learning curve. We reviewed the first 900 CE read by 3 gastroenterologists experienced in endoscopy over 8 years. These 900 CE were divided into 3 groups (300 CE each): group 1 consisted of the sum of the first 100 CE read by each of the 3 endoscopists; group 2, the sum of the second 100 and groups 3, the sum of the third 100. Patients with normal CE were monitored for at least 28 months to estimate the negative predictive value. A total of 54 (18%) CE in group 1, 58 (19.3%) in group 2 and 47 (15.6%) in group 3 were normal, although only 34 patients in group 1, 38 in group 2 and 36 in group 3 with normal CE completed follow up and were eventually studied. The negative predictive value was 88.2% in group 1, 89.5% in group 2 and 97% in group 3 (P>.05). The negative predictive value tended to increase, but remained high and did not change significantly after the first 100 when readers are experienced in conventional endoscopy and have preliminary specific training. Copyright © 2016 Elsevier España, S.L.U., AEEH y AEG. All rights reserved.

Comparative values of medical school assessments in the prediction of internship performance.

Science.gov (United States)

Lee, Ming; Vermillion, Michelle

2018-02-01

Multiple undergraduate achievements have been used for graduate admission consideration. Their relative values in the prediction of residency performance are not clear. This study compared the contributions of major undergraduate assessments to the prediction of internship performance. Internship performance ratings of the graduates of a medical school were collected from 2012 to 2015. Hierarchical multiple regression analyses were used to examine the predictive values of undergraduate measures assessing basic and clinical sciences knowledge and clinical performances, after controlling for differences in the Medical College Admission Test (MCAT). Four hundred eighty (75%) graduates' archived data were used in the study. Analyses revealed that clinical competencies, assessed by the USMLE Step 2 CK, NBME medicine exam, and an eight-station objective structured clinical examination (OSCE), were strong predictors of internship performance. Neither the USMLE Step 1 nor the inpatient internal medicine clerkship evaluation predicted internship performance. The undergraduate assessments as a whole showed a significant collective relationship with internship performance (ΔR 2  = 0.12, p < 0.001). The study supports the use of clinical competency assessments, instead of pre-clinical measures, in graduate admission consideration. It also provides validity evidence for OSCE scores in the prediction of workplace performance.

Estimating Time-Varying PCB Exposures Using Person-Specific Predictions to Supplement Measured Values: A Comparison of Observed and Predicted Values in Two Cohorts of Norwegian Women.

Science.gov (United States)

Nøst, Therese Haugdahl; Breivik, Knut; Wania, Frank; Rylander, Charlotta; Odland, Jon Øyvind; Sandanger, Torkjel Manning

2016-03-01

Studies on the health effects of polychlorinated biphenyls (PCBs) call for an understanding of past and present human exposure. Time-resolved mechanistic models may supplement information on concentrations in individuals obtained from measurements and/or statistical approaches if they can be shown to reproduce empirical data. Here, we evaluated the capability of one such mechanistic model to reproduce measured PCB concentrations in individual Norwegian women. We also assessed individual life-course concentrations. Concentrations of four PCB congeners in pregnant (n = 310, sampled in 2007-2009) and postmenopausal (n = 244, 2005) women were compared with person-specific predictions obtained using CoZMoMAN, an emission-based environmental fate and human food-chain bioaccumulation model. Person-specific predictions were also made using statistical regression models including dietary and lifestyle variables and concentrations. CoZMoMAN accurately reproduced medians and ranges of measured concentrations in the two study groups. Furthermore, rank correlations between measurements and predictions from both CoZMoMAN and regression analyses were strong (Spearman's r > 0.67). Precision in quartile assignments from predictions was strong overall as evaluated by weighted Cohen's kappa (> 0.6). Simulations indicated large inter-individual differences in concentrations experienced in the past. The mechanistic model reproduced all measurements of PCB concentrations within a factor of 10, and subject ranking and quartile assignments were overall largely consistent, although they were weak within each study group. Contamination histories for individuals predicted by CoZMoMAN revealed variation between study subjects, particularly in the timing of peak concentrations. Mechanistic models can provide individual PCB exposure metrics that could serve as valuable supplements to measurements.

Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory.

Science.gov (United States)

Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S; Shirley, Eric L; Prendergast, David

2017-03-03

Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

Development of a method to accurately calculate the Dpb and quickly predict the strength of a chemical bond

International Nuclear Information System (INIS)

Du, Xia; Zhao, Dong-Xia; Yang, Zhong-Zhi

2013-01-01

Highlights: ► A method from new respect to characterize and measure the bond strength is proposed. ► We calculate the D pb of a series of various bonds to justify our approach. ► A quite good linear relationship of the D pb with the bond lengths for series of various bonds is shown. ► Take the prediction of strengths of C–H and N–H bonds for base pairs in DNA as a practical application of our method. - Abstract: A new approach to characterize and measure bond strength has been developed. First, we propose a method to accurately calculate the potential acting on an electron in a molecule (PAEM) at the saddle point along a chemical bond in situ, denoted by D pb . Then, a direct method to quickly evaluate bond strength is established. We choose some familiar molecules as models for benchmarking this method. As a practical application, the D pb of base pairs in DNA along C–H and N–H bonds are obtained for the first time. All results show that C 7 –H of A–T and C 8 –H of G–C are the relatively weak bonds that are the injured positions in DNA damage. The significance of this work is twofold: (i) A method is developed to calculate D pb of various sizable molecules in situ quickly and accurately; (ii) This work demonstrates the feasibility to quickly predict the bond strength in macromolecules

Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.

Science.gov (United States)

Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W

2014-11-26

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. Copyright © 2014 the authors 0270-6474/14/3416046-12$15.00/0.

Prediction of postoperative pulmonary function following thoracic operations. Value of ventilation-perfusion scanning

International Nuclear Information System (INIS)

Bria, W.F.; Kanarek, D.J.; Kazemi, H.

1983-01-01

Surgical resection of lung cancer is frequently required in patients with severely impaired lung function resulting from chronic obstructive pulmonary disease. Twenty patients with obstructive lung disease and cancer (mean preoperative forced expiratory volume in 1 second [FEV1] . 1.73 L) were studied preoperatively and postoperatively by spirometry and radionuclide perfusion, single-breath ventilation, and washout techniques to test the ability of these methods to predict preoperatively the partial loss of lung function by the resection. Postoperative FEV1 and forced vital capacity (FVC) were accurately predicted by the formula: postoperative FEV1 (or FVC) . preoperative FEV1 X percent function of regions of lung not to be resected (r . 0.88 and 0.95, respectively). Ventilation and perfusion scans are equally effective in prediction. Washout data add to the sophistication of the method by permitting the qualitative evaluation of ventilation during tidal breathing. Criteria for patients requiring the study are suggested

Implicit Theories, Expectancies, and Values Predict Mathematics Motivation and Behavior across High School and College.

Science.gov (United States)

Priess-Groben, Heather A; Hyde, Janet Shibley

2017-06-01

Mathematics motivation declines for many adolescents, which limits future educational and career options. The present study sought to identify predictors of this decline by examining whether implicit theories assessed in ninth grade (incremental/entity) predicted course-taking behaviors and utility value in college. The study integrated implicit theory with variables from expectancy-value theory to examine potential moderators and mediators of the association of implicit theories with college mathematics outcomes. Implicit theories and expectancy-value variables were assessed in 165 American high school students (47 % female; 92 % White), who were then followed into their college years, at which time mathematics courses taken, course-taking intentions, and utility value were assessed. Implicit theories predicted course-taking intentions and utility value, but only self-concept of ability predicted courses taken, course-taking intentions, and utility value after controlling for prior mathematics achievement and baseline values. Expectancy for success in mathematics mediated associations between self-concept of ability and college outcomes. This research identifies self-concept of ability as a stronger predictor than implicit theories of mathematics motivation and behavior across several years: math self-concept is critical to sustained engagement in mathematics.

Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

International Nuclear Information System (INIS)

Jairam, Pushpa M.; Jong, Pim A. de; Mali, Willem P.T.M.; Isgum, Ivana; Graaf, Yolanda van der

2015-01-01

Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

Energy Technology Data Exchange (ETDEWEB)

Jairam, Pushpa M. [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Jong, Pim A. de; Mali, Willem P.T.M. [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Isgum, Ivana [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); Graaf, Yolanda van der [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); Collaboration: PROVIDI study-group

2015-06-01

Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

The Economic Value of Predicting Stock Index Returns and Volatility

NARCIS (Netherlands)

Marquering, W.; Verbeek, M.J.C.M.

2000-01-01

In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954-1998, we

Molecular acidity: An accurate description with information-theoretic approach in density functional reactivity theory.

Science.gov (United States)

Cao, Xiaofang; Rong, Chunying; Zhong, Aiguo; Lu, Tian; Liu, Shubin

2018-01-15

Molecular acidity is one of the important physiochemical properties of a molecular system, yet its accurate calculation and prediction are still an unresolved problem in the literature. In this work, we propose to make use of the quantities from the information-theoretic (IT) approach in density functional reactivity theory and provide an accurate description of molecular acidity from a completely new perspective. To illustrate our point, five different categories of acidic series, singly and doubly substituted benzoic acids, singly substituted benzenesulfinic acids, benzeneseleninic acids, phenols, and alkyl carboxylic acids, have been thoroughly examined. We show that using IT quantities such as Shannon entropy, Fisher information, Ghosh-Berkowitz-Parr entropy, information gain, Onicescu information energy, and relative Rényi entropy, one is able to simultaneously predict experimental pKa values of these different categories of compounds. Because of the universality of the quantities employed in this work, which are all density dependent, our approach should be general and be applicable to other systems as well. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Predictive value of ventilatory inflection points determined under field conditions.

Science.gov (United States)

Heyde, Christian; Mahler, Hubert; Roecker, Kai; Gollhofer, Albert

2016-01-01

The aim of this study was to evaluate the predictive potential provided by two ventilatory inflection points (VIP1 and VIP2) examined in field without using gas analysis systems and uncomfortable facemasks. A calibrated respiratory inductance plethysmograph (RIP) and a computerised routine were utilised, respectively, to derive ventilation and to detect VIP1 and VIP2 during a standardised field ramp test on a 400 m running track on 81 participants. In addition, average running speed of a competitive 1000 m run (S1k) was observed as criterion. The predictive value of running speed at VIP1 (SVIP1) and the speed range between VIP1 and VIP2 in relation to VIP2 (VIPSPAN) was analysed via regression analysis. VIPSPAN rather than running speed at VIP2 (SVIP2) was operationalised as a predictor to consider the covariance between SVIP1 and SVIP2. SVIP1 and VIPSPAN, respectively, provided 58.9% and 22.9% of explained variance in regard to S1k. Considering covariance, the timing of two ventilatory inflection points provides predictive value in regard to a competitive 1000 m run. This is the first study to apply computerised detection of ventilatory inflection points in a field setting independent on measurements of the respiratory gas exchange and without using any facemasks.

Positive predictive value of abnormal mammographic findings and role of assessment procedures

International Nuclear Information System (INIS)

Menna, S.; Marra, V.; Di Virgilio, M.R.; Macchia, G.; Frigerio, A.

1999-01-01

To investigate the positive predictive value for cancer of abnormal mammographic findings and the role of assessment, the authors reviewed a series of 962 patients recalled and examined in the first breast screening center of Turin (Italy), out of 18996 women aged 50-59 from 1991 to 1995, within a population-based mammography program. The results of this study confirm the accuracy of mammography in the early detection of breast cancer and the different role of assessment procedures in the various abnormal mammographic findings. The improvement in positive predictive value for screening demonstrates the importance of the learning curve within the screening team. Most of this improvement could be referred to refined diagnostic criteria for calcifications [it

The predictive value of quantitative fibronectin testing in combination with cervical length measurement in symptomatic women

NARCIS (Netherlands)

Bruijn, Merel M. C.; Kamphuis, Esme I.; Hoesli, Irene M.; Martinez de Tejada, Begoña; Loccufier, Anne R.; Kühnert, Maritta; Helmer, Hanns; Franz, Marie; Porath, Martina M.; Oudijk, Martijn A.; Jacquemyn, Yves; Schulzke, Sven M.; Vetter, Grit; Hoste, Griet; Vis, Jolande Y.; Kok, Marjolein; Mol, Ben W. J.; van Baaren, Gert-Jan

2016-01-01

The combination of the qualitative fetal fibronectin test and cervical length measurement has a high negative predictive value for preterm birth within 7 days; however, positive prediction is poor. A new bedside quantitative fetal fibronectin test showed potential additional value over the

Extreme value prediction of the wave-induced vertical bending moment in large container ships

DEFF Research Database (Denmark)

Andersen, Ingrid Marie Vincent; Jensen, Jørgen Juncher

2015-01-01

increase the extreme hull girder response significantly. Focus in the present paper is on the influence of the hull girder flexibility on the extreme response amidships, namely the wave-induced vertical bending moment (VBM) in hogging, and the prediction of the extreme value of the same. The analysis...... in the present paper is based on time series of full scale measurements from three large container ships of 8600, 9400 and 14000 TEU. When carrying out the extreme value estimation the peak-over-threshold (POT) method combined with an appropriate extreme value distribution is applied. The choice of a proper...... threshold level as well as the statistical correlation between clustered peaks influence the extreme value prediction and are taken into consideration in the present paper....

Clinical value of acoustic radiation force impulse in quantitative prediction of the degree of esophageal varices in patients with liver cirrhosis

Directory of Open Access Journals (Sweden)

CHEN Min

2018-01-01

esophageal varices (P=0.245. Conclusion The ARFI value of the spleen helps to achieve accurate quantitative prediction of the degree of esophageal varices in patients with cirrhotic portal hypertension and holds promise for clinical application.

CASSAVA BREEDING I: THE VALUE OF BREEDING VALUE

Directory of Open Access Journals (Sweden)

Hernán Ceballos

2016-08-01

Full Text Available Breeding cassava relies on several selection stages (single row trial-SRT; preliminary; advanced; and uniform yield trials - UYT. This study uses data from 14 years of evaluations. From more than 20,000 genotypes initially evaluated only 114 reached the last stage. The objective was to assess how the data at SRT could be used to predict the probabilities of genotypes reaching the UYT. Phenotypic data from each genotype at SRT was integrated into the selection index (SIN used by the cassava breeding program. Average SIN from all the progenies derived from each progenitor was then obtained. Average SIN is an approximation of the breeding value of each progenitor. Data clearly suggested that some genotypes were better progenitors than others (e.g. high number of their progenies reaching the UYT, suggesting important variation in breeding values of progenitors. However, regression of average SIN of each parental genotype on the number of their respective progenies reaching UYT resulted in a negligible coefficient of determination (r2 = 0.05. Breeding value (e.g. average SIN at SRT was not efficient predicting which genotypes were more likely to reach the UYT stage. Number of families and progenies derived from a given progenitor were more efficient predicting the probabilities of the progeny from a given parent reaching the UYT stage. Large within-family genetic variation tends to mask the true breeding value of each progenitor. The use of partially inbred progenitors (e.g. S1 or S2 genotypes would reduce the within-family genetic variation thus making the assessment of breeding value more accurate. Moreover, partial inbreeding of progenitors can improve the breeding value of the original (S0 parental material and sharply accelerate genetic gains. For instance, homozygous S1 genotypes for the dominant resistance to cassava mosaic disease could be generated and selected. All gametes from these selected S1 genotypes would carry the desirable allele

Prognostic value of tumor size in patients with remnant gastric cancer: is the seventh UICC stage sufficient for predicting prognosis?

Directory of Open Access Journals (Sweden)

Jun Lu

Full Text Available The 7th UICC N stage may be unsuitable for remnant gastric cancer (RGC because the original disease and previous operation usually cause abnormal lymphatic drainage. However, the prognostic significance of the current TNM staging system in RGC has not been studied.Prospective data from 153 RGC patients who underwent curative gastrectomy from Jan 1995 to Aug 2009 were reviewed. All patients were classified according to tumor size (3&≤5 cm as N1;>5&≤7 cm as N2; and>7 cm as N3. The overall survival was estimated using the Kaplan-Meier method, and hazard ratios (HRs were calculated using the Cox proportional hazard model.Tumor sizes ranged from 1.0 to 15.0 cm (median 5.0 cm. Tumor size, depth of invasion and lymph node (LN metastasis were significant prognostic factors based on both the univariate and multivariate analyses (P<0.05. In the survival analysis, the seventh edition UICC-TNM classification provided a detailed classification; however, some subgroups of the UICC-TNM classification did not have significantly different survival rates. The combination of the seventh edition T classification and the suggested N classification, with ideal relative risk (RR results and P value, was distinctive for subgrouping the survival rates except for the IA versus IB and II A versus IIB. A modified staging system based on tumor size, predicted survival more accurately than the conventional TNM staging system.In RGCs, tumor size is an independent prognostic factor and a modified TNM system based on tumor size accurately predicts survival.

Predictive Value of Parkinsonian Primates in Pharmacologic Studies: A Comparison between the Macaque, Marmoset, and Squirrel Monkey.

Science.gov (United States)

Veyres, Nicolas; Hamadjida, Adjia; Huot, Philippe

2018-05-01

The 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned primate is the gold-standard animal model of Parkinson disease (PD) and has been used to assess the effectiveness of experimental drugs on dyskinesia, parkinsonism, and psychosis. Three species have been used in most studies-the macaque, marmoset, and squirrel monkey-the last much less so than the first two species; however, the predictive value of each species at forecasting clinical efficacy, or lack thereof, is poorly documented. Here, we have reviewed all the published literature detailing pharmacologic studies that assessed the effects of experimental drugs on dyskinesia, parkinsonism, and psychosis in each of these species and have calculated their predictive value of success and failure at the clinical level. We found that, for dyskinesia, the macaque has a positive predictive value of 87.5% and a false-positive rate of 38.1%, whereas the marmoset has a positive predictive value of 76.9% and a false-positive rate of 15.6%. For parkinsonism, the macaque has a positive predictive value of 68.2% and a false-positive rate of 44.4%, whereas the marmoset has a positive predictive value of 86.9% and a false-positive rate of 41.7%. No drug that alleviates psychosis in the clinic has shown efficacy at doing so in the macaque, whereas the marmoset has 100% positive predictive value. The small number of studies conducted in the squirrel monkey precluded us from calculating its predictive efficacy. We hope our results will help in the design of pharmacologic experiments and will facilitate the drug discovery and development process in PD. Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics.

Value of routine blood tests for prediction of mortality risk in hip fracture patients

DEFF Research Database (Denmark)

Mosfeldt, Mathias; Pedersen, Ole Birger Vesterager; Riis, Troels

2012-01-01

There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission.......There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission....

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

Science.gov (United States)

Sehgal, Muhammad Shoaib B; Gondal, Iqbal; Dooley, Laurence S

2005-05-15

Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE

Prediction of heating value of straw by proximate data, and near infrared spectroscopy

International Nuclear Information System (INIS)

Huang Caijin; Han Lujia; Yang Zengling; Liu Xian

2008-01-01

Exploration of straw resources for energy production has been attracting agricultural scientists and engineers for decades. And the heating value of straw has always been the focus when initiating a straw-based biomass energy project. Nevertheless determination of heating values of straw needs delicate and expensive calorimeter, and is time-consuming. It's quite desirable to develop quick and easy model predicting heating values of straw. In this study, we proposed three applicable models, first two are multiple linear regression (MLR) equations by contents of moisture, ash, and volatile matter, the other one is based on the near infrared spectroscopy (NIRS) technology. All the models provide satisfactory estimations of heating values of straw samples. The adjusted determination coefficients for MLR models were 0.9049 and 0.9039, and determination coefficients of calibration for NIRS model was 0.9604; When evaluated on independent validation, the determination coefficients were 0.8595, 0.8524 and 0.8946, respectively. The results indicated that both MLR models and NIRS model have the potential to predict the heating values of straw, while the NIRS model presented better accuracy

Fast and simultaneous prediction of animal feed nutritive values using near infrared reflectance spectroscopy

Science.gov (United States)

Samadi; Wajizah, S.; Munawar, A. A.

2018-02-01

Feed plays an important factor in animal production. The purpose of this study is to apply NIRS method in determining feed values. NIRS spectra data were acquired for feed samples in wavelength range of 1000 - 2500 nm with 32 scans and 0.2 nm wavelength. Spectral data were corrected by de-trending (DT) and standard normal variate (SNV) methods. Prediction of in vitro dry matter digestibility (IVDMD) and in vitro organic matter digestibility (IVOMD) were established as model by using principal component regression (PCR) and validated using leave one out cross validation (LOOCV). Prediction performance was quantified using coefficient correlation (r) and residual predictive deviation (RPD) index. The results showed that IVDMD and IVOMD can be predicted by using SNV spectra data with r and RPD index: 0.93 and 2.78 for IVDMD ; 0.90 and 2.35 for IVOMD respectively. In conclusion, NIRS technique appears feasible to predict animal feed nutritive values.

Combined Value of Red Blood Cell Distribution Width and Global Registry of Acute Coronary Events Risk Score for Predicting Cardiovascular Events in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

Science.gov (United States)

Zhao, Na; Mi, Lan; Liu, Xiaojun; Pan, Shuo; Xu, Jiaojiao; Xia, Dongyu; Liu, Zhongwei; Zhang, Yong; Xiang, Yu; Yuan, Zuyi; Guan, Gongchang; Wang, Junkui

2015-01-01

Global Registry of Acute Coronary Events (GRACE) risk score and red blood cell distribution width (RDW) content can both independently predict major adverse cardiac events (MACEs) in patients with acute coronary syndrome (ACS). We investigated the combined predictive value of RDW and GRACE risk score for cardiovascular events in patients with ACS undergoing percutaneous coronary intervention (PCI) for the first time. We enrolled 480 ACS patients. During a median follow-up time of 37.2 months, 70 (14.58%) patients experienced MACEs. Patients were divided into tertiles according to the baseline RDW content (11.30-12.90, 13.00-13.50, 13.60-16.40). GRACE score was positively correlated with RDW content. Multivariate Cox analysis showed that both GRACE score and RDW content were independent predictors of MACEs (hazard ratio 1.039; 95% confidence interval [CI] 1.024-1.055; p risk of MACEs increased with increasing RDW content (p value of combining RDW content and GRACE risk score was significantly improved, also shown by the net reclassification improvement (NRI = 0.352, p value of RDW and GRACE risk score yielded a more accurate predictive value for long-term cardiovascular events in ACS patients who underwent PCI as compared to each measure alone.

Machine Learning Techniques for Prediction of Early Childhood Obesity.

Science.gov (United States)

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Partial volume correction and image segmentation for accurate measurement of standardized uptake value of grey matter in the brain.

Science.gov (United States)

Bural, Gonca; Torigian, Drew; Basu, Sandip; Houseni, Mohamed; Zhuge, Ying; Rubello, Domenico; Udupa, Jayaram; Alavi, Abass

2015-12-01

Our aim was to explore a novel quantitative method [based upon an MRI-based image segmentation that allows actual calculation of grey matter, white matter and cerebrospinal fluid (CSF) volumes] for overcoming the difficulties associated with conventional techniques for measuring actual metabolic activity of the grey matter. We included four patients with normal brain MRI and fluorine-18 fluorodeoxyglucose (F-FDG)-PET scans (two women and two men; mean age 46±14 years) in this analysis. The time interval between the two scans was 0-180 days. We calculated the volumes of grey matter, white matter and CSF by using a novel segmentation technique applied to the MRI images. We measured the mean standardized uptake value (SUV) representing the whole metabolic activity of the brain from the F-FDG-PET images. We also calculated the white matter SUV from the upper transaxial slices (centrum semiovale) of the F-FDG-PET images. The whole brain volume was calculated by summing up the volumes of the white matter, grey matter and CSF. The global cerebral metabolic activity was calculated by multiplying the mean SUV with total brain volume. The whole brain white matter metabolic activity was calculated by multiplying the mean SUV for the white matter by the white matter volume. The global cerebral metabolic activity only reflects those of the grey matter and the white matter, whereas that of the CSF is zero. We subtracted the global white matter metabolic activity from that of the whole brain, resulting in the global grey matter metabolism alone. We then divided the grey matter global metabolic activity by grey matter volume to accurately calculate the SUV for the grey matter alone. The brain volumes ranged between 1546 and 1924 ml. The mean SUV for total brain was 4.8-7. Total metabolic burden of the brain ranged from 5565 to 9617. The mean SUV for white matter was 2.8-4.1. On the basis of these measurements we generated the grey matter SUV, which ranged from 8.1 to 11.3. The

A self-interaction-free local hybrid functional: Accurate binding energies vis-à-vis accurate ionization potentials from Kohn-Sham eigenvalues

International Nuclear Information System (INIS)

Schmidt, Tobias; Kümmel, Stephan; Kraisler, Eli; Makmal, Adi; Kronik, Leeor

2014-01-01

We present and test a new approximation for the exchange-correlation (xc) energy of Kohn-Sham density functional theory. It combines exact exchange with a compatible non-local correlation functional. The functional is by construction free of one-electron self-interaction, respects constraints derived from uniform coordinate scaling, and has the correct asymptotic behavior of the xc energy density. It contains one parameter that is not determined ab initio. We investigate whether it is possible to construct a functional that yields accurate binding energies and affords other advantages, specifically Kohn-Sham eigenvalues that reliably reflect ionization potentials. Tests for a set of atoms and small molecules show that within our local-hybrid form accurate binding energies can be achieved by proper optimization of the free parameter in our functional, along with an improvement in dissociation energy curves and in Kohn-Sham eigenvalues. However, the correspondence of the latter to experimental ionization potentials is not yet satisfactory, and if we choose to optimize their prediction, a rather different value of the functional's parameter is obtained. We put this finding in a larger context by discussing similar observations for other functionals and possible directions for further functional development that our findings suggest

Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms

Directory of Open Access Journals (Sweden)

Hajar Shafiee

2016-06-01

Full Text Available Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran’s Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms. Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data. Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced. Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy.

Prediction of Electricity Usage Using Convolutional Neural Networks

OpenAIRE

Hansen, Martin

2017-01-01

Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

Science.gov (United States)

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Review of clinically accessible methods to determine lean body mass for normalization of standardized uptake values

International Nuclear Information System (INIS)

DEVRIESE, Joke; POTTEL, Hans; BEELS, Laurence; MAES, Alex; VAN DE WIELE, Christophe; GHEYSENS, Olivier

2016-01-01

With the routine use of 2-deoxy-2-[ 18 F]-fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) scans, metabolic activity of tumors can be quantitatively assessed through calculation of SUVs. One possible normalization parameter for the standardized uptake value (SUV) is lean body mass (LBM), which is generally calculated through predictive equations based on height and body weight. (Semi-)direct measurements of LBM could provide more accurate results in cancer populations than predictive equations based on healthy populations. In this context, four methods to determine LBM are reviewed: bioelectrical impedance analysis, dual-energy X-ray absorptiometry. CT, and magnetic resonance imaging. These methods were selected based on clinical accessibility and are compared in terms of methodology, precision and accuracy. By assessing each method’s specific advantages and limitations, a well-considered choice of method can hopefully lead to more accurate SUVLBM values, hence more accurate quantitative assessment of 18F-FDG PET images.

Watershed area ratio accurately predicts daily streamflow in nested catchments in the Catskills, New York

Directory of Open Access Journals (Sweden)

Chris C. Gianfagna

2015-09-01

New hydrological insights for the region: Watershed area ratio was the most important basin parameter for estimating flow at upstream sites based on downstream flow. The area ratio alone explained 93% of the variance in the slopes of relationships between upstream and downstream flows. Regression analysis indicated that flow at any upstream point can be estimated by multiplying the flow at a downstream reference gage by the watershed area ratio. This method accurately predicted upstream flows at area ratios as low as 0.005. We also observed a very strong relationship (R2 = 0.79 between area ratio and flow–flow slopes in non-nested catchments. Our results indicate that a simple flow estimation method based on watershed area ratios is justifiable, and indeed preferred, for the estimation of daily streamflow in ungaged watersheds in the Catskills region.

Lung perfusion SPECT in predicting postoperative pulmonary function in lung cancer

International Nuclear Information System (INIS)

Hirose, Yoshiaki; Imaeda, Takeyoshi; Doi, Hidetaka; Kokubo, Mitsuharu; Sakai, Satoshi; Hirose, Hajime

1993-01-01

The aim of this prospective study is to evaluate the availability of preoperative perfusion SPECT in predicting postoperative pulmonary function following resection. Twenty-three patients with lung cancer who were candidates for lobectomy were investigated preoperatively with spirometry, x-ray computed tomography and 99m Tc-macroaggregated albumin SPECT. Their postoperative pulmonary functions were predicted with these examinations. The forced vital capacity and the forced expiratory volume in one second were selected as parameters for overall pulmonary function. The postoperative pulmonary function was predicted by the following formula: Predicted postoperative value=observed preoperative value x precent perfusion of the lung not to be resected. The patients were reinvestigated with spirometry at 3 months and 6 months after lobectomy, and the values obtained were statistically compared with the predicted values. Close relationships were found between predicted and observed forced vital capacity (r=0.87, p<0.001), and predicted and observed forced expiratory volume in one second (r=0.90, p<0.001). The accurate prediction of pulmonary function after lobectomy could be achieved by means of lung perfusion SPECT. (author)

Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

Science.gov (United States)

Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

2017-12-12

Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

Predictive value of the official cancer alarm symptoms in general practice

DEFF Research Database (Denmark)

Krasnik Huggenberger, Ivan; Andersen, John Sahl

2015-01-01

Introduction: The objective of this study was to investigate the evidence for positive predictive value (PPV) of alarm symptoms and combinations of symptoms for colorectal cancer, breast cancer, prostate cancer and lung cancer in general practice. Methods: This study is based on a literature search...

Towards more accurate and reliable predictions for nuclear applications

International Nuclear Information System (INIS)

Goriely, S.

2015-01-01

The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. In the present contribution, the reliability and accuracy of recent nuclear theories are discussed for most of the relevant quantities needed to estimate reaction cross sections and beta-decay rates, namely nuclear masses, nuclear level densities, gamma-ray strength, fission properties and beta-strength functions. It is shown that nowadays, mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenogical inputs in the prediction of nuclear data. While fundamental nuclear physicists keep on improving state-of-the-art models, e.g. within the shell model or ab initio models, nuclear applications could make use of their most recent results as quantitative constraints or guides to improve the predictions in energy or mass domain that will remain inaccessible experimentally. (orig.)

Ability of commercially available dairy ration programs to predict duodenal flows of protein and essential amino acids in dairy cows.

Science.gov (United States)

Pacheco, D; Patton, R A; Parys, C; Lapierre, H

2012-02-01

The objective of this analysis was to compare the rumen submodel predictions of 4 commonly used dairy ration programs to observed values of duodenal flows of crude protein (CP), protein fractions, and essential AA (EAA). The literature was searched and 40 studies, including 154 diets, were used to compare observed values with those predicted by AminoCow (AC), Agricultural Modeling and Training Systems (AMTS), Cornell-Penn-Miner (CPM), and National Research Council 2001 (NRC) models. The models were evaluated based on their ability to predict the mean, their root mean square prediction error (RMSPE), error bias, and adequacy of regression equations for each protein fraction. The models predicted the mean duodenal CP flow within 5%, with more than 90% of the variation due to random disturbance. The models also predicted within 5% the mean microbial CP flow except CPM, which overestimated it by 27%. Only NRC, however, predicted mean rumen-undegraded protein (RUP) flows within 5%, whereas AC and AMTS underpredicted it by 8 to 9% and CPM by 24%. Regarding duodenal flows of individual AA, across all diets, CPM predicted substantially greater (>10%) mean flows of Arg, His, Ile, Met, and Lys; AMTS predicted greater flow for Arg and Met, whereas AC and NRC estimations were, on average, within 10% of observed values. Overpredictions by the CPM model were mainly related to mean bias, whereas the NRC model had the highest proportion of bias in random disturbance for flows of EAA. Models tended to predict mean flows of EAA more accurately on corn silage and alfalfa diets than on grass-based diets, more accurately on corn grain-based diets than on non-corn-based diets, and finally more accurately in the mid range of diet types. The 4 models were accurate at predicting mean dry matter intake. The AC, AMTS, and NRC models were all sufficiently accurate to be used for balancing EAA in dairy rations under field conditions. Copyright © 2012 American Dairy Science Association

Feedforward signal prediction for accurate motion systems using digital filters

NARCIS (Netherlands)

Butler, H.

2012-01-01

A positioning system that needs to accurately track a reference can benefit greatly from using feedforward. When using a force actuator, the feedforward needs to generate a force proportional to the reference acceleration, which can be measured by means of an accelerometer or can be created by

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

Science.gov (United States)

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

2009-09-01

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

Predicting the combustion kinetics of Chinese coals

Energy Technology Data Exchange (ETDEWEB)

Niksa, Stephen [Niksa Energy Associates LLC, Belmont, CA (United States); Fujiwara, Naoki [Idemitsu Kosan Co., Ltd, Chiba (Japan). Coal and Environment Research Lab.

2013-07-01

The database on the devolatilization of Chinese coals in the English literature represents coals from all ranks and the major Chinese mines. It was mostly acquired with TGAs. There are sufficient datasets from devices that imposed rapid heating rates to bracket combustor behavior. The domains of heating rate, temperature, pressure, and particle size are either directly relevant to combustion conditions, or close enough to manage with modest extrapolations. Whereas the data on ultimate total yields is sufficient to validate a model for any coal type, more detailed product distributions and char compositions would be desirable. Based on the accurate interpretation of this database, there are few unresolved issues surrounding the applicability of FLASHCHAIN {sup registered} for combustion applications in China. The sub-database on devolatilization under rapid heating conditions represents 34 samples. The predicted yields were within the measurement uncertainties of 4 daf wt. % for 29 of these coals. Among the five ultimate yields that were not accurately predicted, three had measured values less than the proximate volatile matter (PVM), despite the rapid heating rates in the tests. Similarly, the sub-database on devolatilization under slow heating conditions characterizes ultimate devolatilization yields of 30 samples. The predicted yields were within the measurement uncertainties for 22 of these coals. Among the eight that were not accurately predicted, three had measured values that were much lower than the PVM (which is a problem even after accounting for the slow heating rates in the tests) and three were in studies that did not report ultimate analyses for the coals tested. Unfortunately, the database on the combustion behavior of the chars from Chinese coals is insufficient to specify char oxidation kinetics.

Spent-fuel composition: a comparison of predicted and measured data

International Nuclear Information System (INIS)

Thomas, C.C. Jr.; Cobb, D.D.; Ostenak, C.A.

1981-03-01

The uncertainty in predictions of the nuclear materials content of spent light-water reactor fuel was investigated to obtain guidelines for nondestructive spent-fuel verification and assay. Values predicted by the reactor operator were compared with measured values from fuel reprocessors for six reactors (three PWR and three BWR). The study indicates that total uranium, total plutonium, fissile uranium, fissile plutonium, and total fissile content can be predicted with biases ranging from 1 to 6% and variabilities (1-sigma) ranging from 2 to 7%. The higher values generally are associated with BWRs. Based on the results of this study, nondestructive assay measurements that are accurate and precise to 5 to 10% (1sigma) or better should be useful for quantitative analyses of typical spent fuel

Can tritiated water-dilution space accurately predict total body water in chukar partridges

International Nuclear Information System (INIS)

Crum, B.G.; Williams, J.B.; Nagy, K.A.

1985-01-01

Total body water (TBW) volumes determined from the dilution space of injected tritiated water have consistently overestimated actual water volumes (determined by desiccation to constant mass) in reptiles and mammals, but results for birds are controversial. We investigated potential errors in both the dilution method and the desiccation method in an attempt to resolve this controversy. Tritiated water dilution yielded an accurate measurement of water mass in vitro. However, in vivo, this method yielded a 4.6% overestimate of the amount of water (3.1% of live body mass) in chukar partridges, apparently largely because of loss of tritium from body water to sites of dissociable hydrogens on body solids. An additional source of overestimation (approximately 2% of body mass) was loss of tritium to the solids in blood samples during distillation of blood to obtain pure water for tritium analysis. Measuring tritium activity in plasma samples avoided this problem but required measurement of, and correction for, the dry matter content in plasma. Desiccation to constant mass by lyophilization or oven-drying also overestimated the amount of water actually in the bodies of chukar partridges by 1.4% of body mass, because these values included water adsorbed onto the outside of feathers. When desiccating defeathered carcasses, oven-drying at 70 degrees C yielded TBW values identical to those obtained from lyophilization, but TBW was overestimated (0.5% of body mass) by drying at 100 degrees C due to loss of organic substances as well as water

Accurate microRNA target prediction correlates with protein repression levels

Directory of Open Access Journals (Sweden)

Simossis Victor A

2009-09-01

Full Text Available Abstract Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

Added value of multiple versus single sessions of repetitive transcranial magnetic stimulation in predicting motor cortex stimulation efficacy for refractory neuropathic pain.

Science.gov (United States)

Pommier, Benjamin; Quesada, Charles; Fauchon, Camille; Nuti, Christophe; Vassal, François; Peyron, Roland

2018-05-18

OBJECTIVE Selection criteria for offering patients motor cortex stimulation (MCS) for refractory neuropathic pain are a critical topic of research. A single session of repetitive transcranial magnetic stimulation (rTMS) has been advocated for selecting MCS candidates, but it has a low negative predictive value. Here the authors investigated whether multiple rTMS sessions would more accurately predict MCS efficacy. METHODS Patients included in this longitudinal study could access MCS after at least four rTMS sessions performed 3-4 weeks apart. The positive (PPV) and negative (NPV) predictive values of the four rTMS sessions and the correlation between the analgesic effects of the two treatments were assessed. RESULTS Twelve MCS patients underwent an average of 15.9 rTMS sessions prior to surgery; nine of the patients were rTMS responders. Postoperative follow-up was 57.8 ± 15.6 months (mean ± standard deviation). Mean percentage of pain relief (%R) was 21% and 40% after the first and fourth rTMS sessions, respectively. The corresponding mean durations of pain relief were respectively 2.4 and 12.9 days. A cumulative effect of the rTMS sessions was observed on both %R and duration of pain relief (p < 0.01). The %R value obtained with MCS was 35% after 6 months and 43% at the last follow-up. Both the PPV and NPV of rTMS were 100% after the fourth rTMS session (p = 0.0045). A significant correlation was found between %R or duration of pain relief after the fourth rTMS session and %R at the last MCS follow-up (R 2 = 0.83, p = 0.0003). CONCLUSIONS Four rTMS sessions predicted MCS efficacy better than a single session in neuropathic pain patients. Taking into account the cumulative effects of rTMS, the authors found a high-level correlation between the analgesic effects of rTMS and MCS.

Predictive value of prostate-specific antigen for prostate cancer

DEFF Research Database (Denmark)

Shepherd, Leah; Borges, Alvaro Humberto; Ravn, Lene

2014-01-01

INTRODUCTION: Although prostate cancer (PCa) incidence is lower in HIV+ men than in HIV- men, the usefulness of prostate-specific antigen (PSA) screening in this population is not well defined and may have higher false negative rates than in HIV- men. We aimed to describe the kinetics and predict......INTRODUCTION: Although prostate cancer (PCa) incidence is lower in HIV+ men than in HIV- men, the usefulness of prostate-specific antigen (PSA) screening in this population is not well defined and may have higher false negative rates than in HIV- men. We aimed to describe the kinetics...... and predictive value of PSA in HIV+ men. METHODS: Men with PCa (n=21) and up to two matched controls (n=40) with prospectively stored plasma samples before PCa (or matched date in controls) were selected. Cases and controls were matched on date of first and last sample, age, region of residence and CD4 count...... at first sample date. Total PSA (tPSA), free PSA (fPSA), testosterone and sex hormone binding globulin (SHBG) were measured. Conditional logistic regression models investigated associations between markers and PCa. Sensitivity and specificity of using tPSA >4 µg/L to predict PCa was calculated. Mixed...

Accuration of Time Series and Spatial Interpolation Method for Prediction of Precipitation Distribution on the Geographical Information System

Science.gov (United States)

Prasetyo, S. Y. J.; Hartomo, K. D.

2018-01-01

The Spatial Plan of the Province of Central Java 2009-2029 identifies that most regencies or cities in Central Java Province are very vulnerable to landslide disaster. The data are also supported by other data from Indonesian Disaster Risk Index (In Indonesia called Indeks Risiko Bencana Indonesia) 2013 that suggest that some areas in Central Java Province exhibit a high risk of natural disasters. This research aims to develop an application architecture and analysis methodology in GIS to predict and to map rainfall distribution. We propose our GIS architectural application of “Multiplatform Architectural Spatiotemporal” and data analysis methods of “Triple Exponential Smoothing” and “Spatial Interpolation” as our significant scientific contribution. This research consists of 2 (two) parts, namely attribute data prediction using TES method and spatial data prediction using Inverse Distance Weight (IDW) method. We conduct our research in 19 subdistricts in the Boyolali Regency, Central Java Province, Indonesia. Our main research data is the biweekly rainfall data in 2000-2016 Climatology, Meteorology, and Geophysics Agency (In Indonesia called Badan Meteorologi, Klimatologi, dan Geofisika) of Central Java Province and Laboratory of Plant Disease Observations Region V Surakarta, Central Java. The application architecture and analytical methodology of “Multiplatform Architectural Spatiotemporal” and spatial data analysis methodology of “Triple Exponential Smoothing” and “Spatial Interpolation” can be developed as a GIS application framework of rainfall distribution for various applied fields. The comparison between the TES and IDW methods show that relative to time series prediction, spatial interpolation exhibit values that are approaching actual. Spatial interpolation is closer to actual data because computed values are the rainfall data of the nearest location or the neighbour of sample values. However, the IDW’s main weakness is that some

The Predictive Value of Selection Criteria in an Urban Magnet School

Science.gov (United States)

Lohmeier, Jill Hendrickson; Raad, Jennifer

2012-01-01

The predictive value of selection criteria on outcome data from two cohorts of students (Total N = 525) accepted to an urban magnet high school were evaluated. Regression analyses of typical screening variables (suspensions, absences, metropolitan achievement tests, middle school grade point averages [GPAs], Matrix Analogies test scores, and…

Prediction of pKa values using the PM6 semiempirical method

Directory of Open Access Journals (Sweden)

Jimmy C. Kromann

2016-08-01

Full Text Available The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs of 0.6–0.7 pH units. For carboxylic acids, the accuracy (0.7–1.0 pH units is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5–0.6, slightly worse (0.5–1.0, and worse (1.0–2.5, provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2–5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

Improvement of a land surface model for accurate prediction of surface energy and water balances

International Nuclear Information System (INIS)

Katata, Genki

2009-02-01

In order to predict energy and water balances between the biosphere and atmosphere accurately, sophisticated schemes to calculate evaporation and adsorption processes in the soil and cloud (fog) water deposition on vegetation were implemented in the one-dimensional atmosphere-soil-vegetation model including CO 2 exchange process (SOLVEG2). Performance tests in arid areas showed that the above schemes have a significant effect on surface energy and water balances. The framework of the above schemes incorporated in the SOLVEG2 and instruction for running the model are documented. With further modifications of the model to implement the carbon exchanges between the vegetation and soil, deposition processes of materials on the land surface, vegetation stress-growth-dynamics etc., the model is suited to evaluate an effect of environmental loads to ecosystems by atmospheric pollutants and radioactive substances under climate changes such as global warming and drought. (author)

(18F)-fluorodeoxyglucose PET/CT in cervix cancer: Lymph node assessment and prognostic/predictive value of primary tumour analysis

International Nuclear Information System (INIS)

Leseur, J.; Williaume, D.; Le Prise, E.; De Crevoisier, R.; Devillers, A.; Garin, E.; Fougerou, C.; Bouriel, C.; Leveque, J.; Monpetit, E.; Blanchot, J.

2011-01-01

Purpose. - In cervix carcinoma: (a) to evaluate the ability of ( 18 F)-fluorodeoxyglucose (FDG) positron emission tomography (PET) in the lymph node detection; (b) to investigate the prognostic and predictive value of the primary cervical PET parameters. Patients and methods. - Ninety patients treated for cervix carcinoma and evaluated initially by MRI and FDG PET were included. The performances of FDG-PET for lymph node detection (relatively to the lymph node dissection) have been described (sensitivity, specificity, positive predictive value and negative predictive value). PET tumour parameters analyzed were: maximum standard uptake value (SUV max ), the volume and the maximum diameter. The prognostic and predictive values of these parameters were investigated. The tumour response was evaluated on surgical specimens. Results. - PET detected the cervical tumour with a sensitivity of 97% (mean values: SUV max = 15.8, volume = 27 mm 3 , maximum diameter = 47). For the detection of the lymph nodes, the values of sensibility, specificity, positive predictive value and negative predictive value were: 86, 56, 69 and 78% in the pelvic, and 90, 67, 50 and 95% for the para-aortic area, respectively. The SUV max was correlated with histologic response (P = 0.04). The frequency of partial histological response was significantly higher for tumour SUV max > 10.9 (P = 0.017). The maximum PET diameter and pathologic response had an impact on disease-free survival and overall survival in multivariate analysis (P < 0.05). Conclusion. - PET has high sensitivity in detecting pelvic and para-aortic lymph nodes. Some primary cervical tumour PET parameters are useful as prognostic and predictive factors. (authors)

Value of supervised learning events in predicting doctors in difficulty.

Science.gov (United States)

Patel, Mumtaz; Agius, Steven; Wilkinson, Jack; Patel, Leena; Baker, Paul

2016-07-01

In the UK, supervised learning events (SLE) replaced traditional workplace-based assessments for foundation-year trainees in 2012. A key element of SLEs was to incorporate trainee reflection and assessor feedback in order to drive learning and identify training issues early. Few studies, however, have investigated the value of SLEs in predicting doctors in difficulty. This study aimed to identify principles that would inform understanding about how and why SLEs work or not in identifying doctors in difficulty (DiD). A retrospective case-control study of North West Foundation School trainees' electronic portfolios was conducted. Cases comprised all known DiD. Controls were randomly selected from the same cohort. Free-text supervisor comments from each SLE were assessed for the four domains defined in the General Medical Council's Good Medical Practice Guidelines and each scored blindly for level of concern using a three-point ordinal scale. Cumulative scores for each SLE were then analysed quantitatively for their predictive value of actual DiD. A qualitative thematic analysis was also conducted. The prevalence of DiD in this sample was 6.5%. Receiver operator characteristic curve analysis showed that Team Assessment of Behaviour (TAB) was the only SLE strongly predictive of actual DiD status. The Educational Supervisor Report (ESR) was also strongly predictive of DiD status. Fisher's test showed significant associations of TAB and ESR for both predicted and actual DiD status and also the health and performance subtypes. None of the other SLEs showed significant associations. Qualitative data analysis revealed inadequate completion and lack of constructive, particularly negative, feedback. This indicated that SLEs were not used to their full potential. TAB and the ESR are strongly predictive of DiD. However, SLEs are not being used to their full potential, and the quality of completion of reports on SLEs and feedback needs to be improved in order to better identify

Validity of predicting left ventricular end systolic pressure changes following an acute bout of exercise.

Science.gov (United States)

Kappus, Rebecca M; Ranadive, Sushant M; Yan, Huimin; Lane, Abbi D; Cook, Marc D; Hall, Grenita; Harvey, I Shevon; Wilund, Kenneth R; Woods, Jeffrey A; Fernhall, Bo

2013-01-01

Left ventricular end systolic pressure (LV ESP) is important in assessing left ventricular performance and is usually derived from prediction equations. It is unknown whether these equations are accurate at rest or following exercise in a young, healthy population. Measured LV ESP vs. LV ESP values from the prediction equations were compared at rest, 15 min and 30 min following peak aerobic exercise in 60 participants. LV ESP was obtained by applanation tonometry at rest, 15 min post and 30 min post peak cycle exercise. Measured LV ESP was significantly lower (p<0.05) at all time points in comparison to the two calculated values. Measured LV ESP decreased significantly from rest at both the post15 and post30 time points (p<0.05) and changed differently in comparison to the calculated values (significant interaction; p<0.05). The two LV ESP equations were also significantly different from each other (p<0.05) and changed differently over time (significant interaction; p<0.05). The two commonly used prediction equations did not accurately predict either resting or post exercise LV ESP in a young, healthy population. Thus, LV ESP needs to be individually determined in young, healthy participants. Non-invasive measurement through applanation tonometry appears to allow for a more accurate determination of LV ESP. Copyright © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Achieving target voriconazole concentrations more accurately in children and adolescents.

Science.gov (United States)

Neely, Michael; Margol, Ashley; Fu, Xiaowei; van Guilder, Michael; Bayard, David; Schumitzky, Alan; Orbach, Regina; Liu, Siyu; Louie, Stan; Hope, William

2015-01-01

Despite the documented benefit of voriconazole therapeutic drug monitoring, nonlinear pharmacokinetics make the timing of steady-state trough sampling and appropriate dose adjustments unpredictable by conventional methods. We developed a nonparametric population model with data from 141 previously richly sampled children and adults. We then used it in our multiple-model Bayesian adaptive control algorithm to predict measured concentrations and doses in a separate cohort of 33 pediatric patients aged 8 months to 17 years who were receiving voriconazole and enrolled in a pharmacokinetic study. Using all available samples to estimate the individual Bayesian posterior parameter values, the median percent prediction bias relative to a measured target trough concentration in the patients was 1.1% (interquartile range, -17.1 to 10%). Compared to the actual dose that resulted in the target concentration, the percent bias of the predicted dose was -0.7% (interquartile range, -7 to 20%). Using only trough concentrations to generate the Bayesian posterior parameter values, the target bias was 6.4% (interquartile range, -1.4 to 14.7%; P = 0.16 versus the full posterior parameter value) and the dose bias was -6.7% (interquartile range, -18.7 to 2.4%; P = 0.15). Use of a sample collected at an optimal time of 4 h after a dose, in addition to the trough concentration, resulted in a nonsignificantly improved target bias of 3.8% (interquartile range, -13.1 to 18%; P = 0.32) and a dose bias of -3.5% (interquartile range, -18 to 14%; P = 0.33). With the nonparametric population model and trough concentrations, our control algorithm can accurately manage voriconazole therapy in children independently of steady-state conditions, and it is generalizable to any drug with a nonparametric pharmacokinetic model. (This study has been registered at ClinicalTrials.gov under registration no. NCT01976078.). Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

Science.gov (United States)

Magozzi, Sarah; Calosi, Piero

2015-01-01

Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

Differential encoding of factors influencing predicted reward value in monkey rostral anterior cingulate cortex.

Science.gov (United States)

Toda, Koji; Sugase-Miyamoto, Yasuko; Mizuhiki, Takashi; Inaba, Kiyonori; Richmond, Barry J; Shidara, Munetaka

2012-01-01

The value of a predicted reward can be estimated based on the conjunction of both the intrinsic reward value and the length of time to obtain it. The question we addressed is how the two aspects, reward size and proximity to reward, influence the responses of neurons in rostral anterior cingulate cortex (rACC), a brain region thought to play an important role in reward processing. We recorded from single neurons while two monkeys performed a multi-trial reward schedule task. The monkeys performed 1-4 sequential color discrimination trials to obtain a reward of 1-3 liquid drops. There were two task conditions, a valid cue condition, where the number of trials and reward amount were associated with visual cues, and a random cue condition, where the cue was picked from the cue set at random. In the valid cue condition, the neuronal firing is strongly modulated by the predicted reward proximity during the trials. Information about the predicted reward amount is almost absent at those times. In substantial subpopulations, the neuronal responses decreased or increased gradually through schedule progress to the predicted outcome. These two gradually modulating signals could be used to calculate the effect of time on the perception of reward value. In the random cue condition, little information about the reward proximity or reward amount is encoded during the course of the trial before reward delivery, but when the reward is actually delivered the responses reflect both the reward proximity and reward amount. Our results suggest that the rACC neurons encode information about reward proximity and amount in a manner that is dependent on utility of reward information. The manner in which the information is represented could be used in the moment-to-moment calculation of the effect of time and amount on predicted outcome value.

[Clinical value of angiogenin in predicting the prognosis of patients with idiopathic pulmonary fibrosis].

Science.gov (United States)

Bai, Yanling; Zhu, Haiyan; Sun, Qiyu; Gu, Guozhong; Zhang, Lingyu; Li, Ying; Yang, Baofeng

2017-09-01

To explore the relationship between angiogenin-1/2 (Ang-1/2) and clinical parameters of idiopathic pulmonary fibrosis (IPF), and to assess the value of Ang-1/2 in predicting the prognosis of patients with IPF. A retrospective analysis was conducted. Ninety-one patients diagnosed as IPF by high resolution CT (HRCT) and lung biopsy admitted to Daqing Oil Field General Hospital from March 2014 to January 2015 were enrolled. The general data, serum parameters and pulmonary function parameters of all patients were collected. After treatment, all of the 91 patients were followed-up to 2 years. The patients were divided into favorable prognosis group and unfavorable prognosis group according to follow-up results. The differences in all parameters between the two groups were compared. The relationship between Ang-1, Ang-2 and lung function parameters was analyzed by Pearson correlation analysis. Cox proportional hazard regression model was used to evaluate the effect of clinical parameters on the prognosis of patients with IPF. The effect of Ang-2 in predicting prognosis of patients with IPF was analyzed by receiver operating characteristic (ROC) curve. During the 2-year follow-up period, 30 of 91 patients showed a favorable prognosis, and 55 showed an unfavorable prognosis with a poor prognosis rate of 64.71%, and 6 patients withdrew from the study due to loss of follow-up and death. Compared with the favorable prognosis group, Ang-2 level in the unfavorable prognosis group was significantly increased (μg/L: 2.88±1.63 vs. 1.89±1.22, t = 2.909, P = 0.005), but Ang-1 only showed a slight increase (μg/L: 28.70±14.26 vs. 25.62±11.95, t = 1.005, P = 0.318). The results of Pearson correlation analysis showed that Ang-2 level was negatively correlated with forced expiratory volume in 1 second (FVC1) and the percentage of carbon monoxide diffusing capacity accounting for the expected value (DLCO%: r value was -0.227 and -0.206, and P value was 0.147 and 0.253, respectively

Lean body mass-based standardized uptake value, derived from a predictive equation, might be misleading in PET studies

International Nuclear Information System (INIS)

Erselcan, Taner; Turgut, Bulent; Dogan, Derya; Ozdemir, Semra

2002-01-01

The standardized uptake value (SUV) has gained recognition in recent years as a semiquantitative evaluation parameter in positron emission tomography (PET) studies. However, there is as yet no consensus on the way in which this index should be determined. One of the confusing factors is the normalisation procedure. Among the proposed anthropometric parameters for normalisation is lean body mass (LBM); LBM has been determined by using a predictive equation in most if not all of the studies. In the present study, we assessed the degree of agreement of various LBM predictive equations with a reference method. Secondly, we evaluated the impact of predicted LBM values on a hypothetical value of 2.5 SUV, normalised to LBM (SUV LBM ), by using various equations. The study population consisted of 153 women, aged 32.3±11.8 years (mean±SD), with a height of 1.61±0.06 m, a weight of 71.1±17.5 kg, a body surface area of 1.77±0.22 m 2 and a body mass index of 27.6±6.9 kg/m 2 . LBM (44.2±6.6 kg) was measured by a dual-energy X-ray absorptiometry (DEXA) method. A total of nine equations from the literature were evaluated, four of them from recent PET studies. Although there was significant correlation between predicted and measured LBM values, 95% limits of agreement determined by the Bland and Altman method showed a wide range of variation in predicted LBM values as compared with DEXA, no matter which predictive equation was used. Moreover, only one predictive equation was not statistically different in the comparison of means (DEXA and predicted LBM values). It was also shown that the predictive equations used in this study yield a wide range of SUV LBM values from 1.78 to 5.16 (29% less or 107% more) for an SUV of 2.5. In conclusion, this study suggests that estimation of LBM by use of a predictive equation may cause substantial error for an individual, and that if LBM is chosen for the SUV normalisation procedure, it should be measured, not predicted. (orig.)

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

Directory of Open Access Journals (Sweden)

Saleh Shahinfar

2012-01-01

Full Text Available Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

Can radiation therapy treatment planning system accurately predict surface doses in postmastectomy radiation therapy patients?

International Nuclear Information System (INIS)

Wong, Sharon; Back, Michael; Tan, Poh Wee; Lee, Khai Mun; Baggarley, Shaun; Lu, Jaide Jay

2012-01-01

Skin doses have been an important factor in the dose prescription for breast radiotherapy. Recent advances in radiotherapy treatment techniques, such as intensity-modulated radiation therapy (IMRT) and new treatment schemes such as hypofractionated breast therapy have made the precise determination of the surface dose necessary. Detailed information of the dose at various depths of the skin is also critical in designing new treatment strategies. The purpose of this work was to assess the accuracy of surface dose calculation by a clinically used treatment planning system and those measured by thermoluminescence dosimeters (TLDs) in a customized chest wall phantom. This study involved the construction of a chest wall phantom for skin dose assessment. Seven TLDs were distributed throughout each right chest wall phantom to give adequate representation of measured radiation doses. Point doses from the CMS Xio® treatment planning system (TPS) were calculated for each relevant TLD positions and results correlated. There were no significant difference between measured absorbed dose by TLD and calculated doses by the TPS (p > 0.05 (1-tailed). Dose accuracy of up to 2.21% was found. The deviations from the calculated absorbed doses were overall larger (3.4%) when wedges and bolus were used. 3D radiotherapy TPS is a useful and accurate tool to assess the accuracy of surface dose. Our studies have shown that radiation treatment accuracy expressed as a comparison between calculated doses (by TPS) and measured doses (by TLD dosimetry) can be accurately predicted for tangential treatment of the chest wall after mastectomy.

Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

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R. Gholami

2012-01-01

Full Text Available Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT

Energy Technology Data Exchange (ETDEWEB)

Li, Heyse, E-mail: heyse.li@mail.utoronto.ca [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8 (Canada); Becker, Nathan; Raman, Srinivas [Radiation Oncology, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9 (Canada); Chan, Timothy C. Y. [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada); Bissonnette, Jean-Pierre [Radiation Oncology, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada)

2015-08-15

Purpose: There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment. Methods: A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derived from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman’s rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew’s correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented. Results: For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N). Conclusions: The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse.

Reliable prediction and determination of Norwegian lamb carcass composition and value

International Nuclear Information System (INIS)

Kongsro, Jørgen

2008-01-01

The main objective of this work was to study prediction and determination of Norwegian lamb carcass composition with different techniques spanning from subjective appraisal to computer-intensive methods. There is an increasing demand, both from farmers and processors of meats, for a more objective and reliable system for prediction of muscle (lean meat), fat, bone and value of a lamb carcass. When introducing new technologies for determination of lamb carcass composition, the reference method used for calibration must be precise and reliable. The precision and reliability of the current dissection reference for lamb carcass classification and grading has never been quantified. A poor reference method will not benefit even the most optimal system for prediction and determination of lamb carcasses. To help achieve reliable systems, the uncertainty or errors in the reference method and measuring systems needs to be quantified. Using proper calibration methods for the measuring systems, the uncertainty and modeling power can be determined for lamb carcasses. The results of the work presented in this thesis show that the current classification system using subjective appraisal (EUROP) is reliable; however the accuracy with respect to carcass composition, especially for lean meat or muscle and carcass value, is poor. The reference method used for determining lamb carcass composition with respect to lamb carcass classification and grading is precise and reliable for carcass composition. For the composition and yield of sub-primal cuts, the reliability varied, and was especially poor for the breast cut. Further attention is needed for jointing and cutting of sub-primals to achieve even higher precision and reliability of the reference method. As an alternative to butcher or manual dissection, Computer Tomography (CT) showed promising results with respect to prediction of lamb carcass composition. This method is nicknamed “virtual dissection”. By utilizing the

Can brain responses to movie trailers predict success?

OpenAIRE

Boksem, Maarten

2015-01-01

textabstractDecades of research have shown that much of our mental processing occurs at the subconscious level, including the decisions we make as consumers. These subconscious processes explain why we so often fail to accurately predict our own future choices. Often what we think we want has little or no bearing on the choices we actually make. Now a new study provides the first evidence that brain measures can provide significant added value to models for predicting consumer choice.

Reflow Process Parameters Analysis and Reliability Prediction Considering Multiple Characteristic Values

Directory of Open Access Journals (Sweden)

Guo Yu

2016-01-01

Full Text Available As a major step surface mount technology, reflow process is the key factor affecting the quality of the final product. The setting parameters and characteristic value of temperature curve shows a nonlinear relationship. So parameter impacts on characteristic values are analyzed and the parameters adjustment process based on orthogonal experiment is proposed in the paper. First, setting parameters are determined and the orthogonal test is designed according to production conditions. Then each characteristic value for temperature profile is calculated. Further, multi-index orthogonal experiment is analyzed for acquiring the setting parameters which impacts the PCBA product quality greater. Finally, reliability prediction is carried out considering the main influencing parameters for providing a theoretical basis of parameters adjustment and product quality evaluation in engineering process.

Can magnetic resonance imaging accurately predict concordant pain provocation during provocative disc injection?

International Nuclear Information System (INIS)

Kang, Chang Ho; Kim, Yun Hwan; Kim, Jung Hyuk; Chung, Kyoo Byung; Sung, Deuk Jae; Lee, Sang-Heon; Derby, Richard

2009-01-01

To correlate magnetic resonance (MR) image findings with pain response by provocation discography in patients with discogenic low back pain, with an emphasis on the combination analysis of a high intensity zone (HIZ) and disc contour abnormalities. Sixty-two patients (aged 17-68 years) with axial low back pain that was likely to be disc related underwent lumbar discography (178 discs tested). The MR images were evaluated for disc degeneration, disc contour abnormalities, HIZ, and endplate abnormalities. Based on the combination of an HIZ and disc contour abnormalities, four classes were determined: (1) normal or bulging disc without HIZ; (2) normal or bulging disc with HIZ; (3) disc protrusion without HIZ; (4) disc protrusion with HIZ. These MR image findings and a new combined MR classification were analyzed in the base of concordant pain determined by discography. Disc protrusion with HIZ [sensitivity 45.5%; specificity 97.8%; positive predictive value (PPV), 87.0%] correlated significantly with concordant pain provocation (P < 0.01). A normal or bulging disc with HIZ was not associated with reproduction of pain. Disc degeneration (sensitivity 95.4%; specificity 38.8%; PPV 33.9%), disc protrusion (sensitivity 68.2%; specificity 80.6%; PPV 53.6%), and HIZ (sensitivity 56.8%; specificity 83.6%; PPV 53.2%) were not helpful in the identification of a disc with concordant pain. The proposed MR classification is useful to predict a disc with concordant pain. Disc protrusion with HIZ on MR imaging predicted positive discography in patients with discogenic low back pain. (orig.)

A two step Bayesian approach for genomic prediction of breeding values.

Science.gov (United States)

Shariati, Mohammad M; Sørensen, Peter; Janss, Luc

2012-05-21

In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df. The simulated data from the 15th QTL-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model. Grouping markers outperformed SNP-BLUP model in terms of accuracy of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances. Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

Science.gov (United States)

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

A simple, fast, and accurate thermodynamic-based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part III: Retention time prediction on target column.

Science.gov (United States)

Hou, Siyuan; Stevenson, Keisean A J M; Harynuk, James J

2018-03-27

This is the third part of a three-part series of papers. In Part I, we presented a method for determining the actual effective geometry of a reference column as well as the thermodynamic-based parameters of a set of probe compounds in an in-house mixture. Part II introduced an approach for estimating the actual effective geometry of a target column by collecting retention data of the same mixture of probe compounds on the target column and using their thermodynamic parameters, acquired on the reference column, as a bridge between both systems. Part III, presented here, demonstrates the retention time transfer and prediction from the reference column to the target column using experimental data for a separate mixture of compounds. To predict the retention time of a new compound, we first estimate its thermodynamic-based parameters on the reference column (using geometric parameters determined previously). The compound's retention time on a second column (of previously determined geometry) is then predicted. The models and the associated optimization algorithms were tested using simulated and experimental data. The accuracy of predicted retention times shows that the proposed approach is simple, fast, and accurate for retention time transfer and prediction between gas chromatography columns. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interpretation of Spirometry: Selection of Predicted Values and Defining Abnormality.

Science.gov (United States)

Chhabra, S K

2015-01-01

Spirometry is the most frequently performed investigation to evaluate pulmonary function. It provides clinically useful information on the mechanical properties of the lung and the thoracic cage and aids in taking management-related decisions in a wide spectrum of diseases and disorders. Few measurements in medicine are so dependent on factors related to equipment, operator and the patient. Good spirometry requires quality assured measurements and a systematic approach to interpretation. Standard guidelines on the technical aspects of equipment and their calibration as well as the test procedure have been developed and revised from time-to-time. Strict compliance with standardisation guidelines ensures quality control. Interpretation of spirometry data is based only on two basic measurements--the forced vital capacity (FVC) and the forced expiratory volume in 1 second (FEV1) and their ratio, FEV1/FVC. A meaningful and clinically useful interpretation of the measured data requires a systematic approach and consideration of several important issues. Central to interpretation is the understanding of the development and application of prediction equations. Selection of prediction equations that are appropriate for the ethnic origin of the patient is vital to avoid erroneous interpretation. Defining abnormal values is a debatable but critical aspect of spirometry. A statistically valid definition of the lower limits of normal has been advocated as the better method over the more commonly used approach of defining abnormality as a fixed percentage of the predicted value. Spirometry rarely provides a specific diagnosis. Examination of the flow-volume curve and the measured data provides information to define patterns of ventilatory impairment. Spirometry must be interpreted in conjunction with clinical information including results of other investigations.

Unilateral Prostate Cancer Cannot be Accurately Predicted in Low-Risk Patients

International Nuclear Information System (INIS)

Isbarn, Hendrik; Karakiewicz, Pierre I.; Vogel, Susanne

2010-01-01

Purpose: Hemiablative therapy (HAT) is increasing in popularity for treatment of patients with low-risk prostate cancer (PCa). The validity of this therapeutic modality, which exclusively treats PCa within a single prostate lobe, rests on accurate staging. We tested the accuracy of unilaterally unremarkable biopsy findings in cases of low-risk PCa patients who are potential candidates for HAT. Methods and Materials: The study population consisted of 243 men with clinical stage ≤T2a, a prostate-specific antigen (PSA) concentration of <10 ng/ml, a biopsy-proven Gleason sum of ≤6, and a maximum of 2 ipsilateral positive biopsy results out of 10 or more cores. All men underwent a radical prostatectomy, and pathology stage was used as the gold standard. Univariable and multivariable logistic regression models were tested for significant predictors of unilateral, organ-confined PCa. These predictors consisted of PSA, %fPSA (defined as the quotient of free [uncomplexed] PSA divided by the total PSA), clinical stage (T2a vs. T1c), gland volume, and number of positive biopsy cores (2 vs. 1). Results: Despite unilateral stage at biopsy, bilateral or even non-organ-confined PCa was reported in 64% of all patients. In multivariable analyses, no variable could clearly and independently predict the presence of unilateral PCa. This was reflected in an overall accuracy of 58% (95% confidence interval, 50.6-65.8%). Conclusions: Two-thirds of patients with unilateral low-risk PCa, confirmed by clinical stage and biopsy findings, have bilateral or non-organ-confined PCa at radical prostatectomy. This alarming finding questions the safety and validity of HAT.

Negative predictive value of ultrasound in predicting tumor-free margins in specimen sonography

International Nuclear Information System (INIS)

Naz, S.; Hafeez, S.; Hussain, Z.; Hilal, K.

2017-01-01

Objective: To evaluate the success of ultrasound in post-excision specimen visualization, and negative predictive value of ultrasound for estimation of tumor-free margins using histopathology as the gold standard. Study Design: Cross-sectional analytical study. Place and Duration of Study: The Aga Khan University Hospital, Karachi, Pakistan, from May 2010 till January 2013. Methodology: Sonography of all breast nodules was done before and after exicision by two female radiologists with at least five years clinical experience. All surgeries were performed by the same referring breast surgeons. All nodules were non-palpable and had histopathology as well as specimen sonography performed at AKUH. Subjects were excluded, if histopathology was not available, post-procedure sonogram not done or done in another hospital and nodules that were not seen on ultrasound. After needle localization in 47 patients using ultrasound and in 7 patients using mammogram was done, sonogram was conducted in all 54 lesions. These were then assessed by ultrasound for detection of lesion and tumor-free margins in malignant lesion. Post-excision ultrasound was performed for the evaluation of lesion whether visualized or absent with localizing needle in situ, lesion dimensions, depth measurement between the superior margin of the lesion and its edge. Results: All 54 lesions were present on post-exicison scan, out of which 28 were documented as malignant and 26 as benign. Ultrasound declared all specimens as tumor-free. On histopathology, two lesions were documented as having tumor-positive margins and were proven to be invasive lobular carcinoma. Therefore, the negative predictive value of the specimen sonography for margin detection was 26/28 (92.8%). Conclusion: Ultrasound of the excised breast tumor specimen is a simple and reliable technique for confirmation of the tumor-free margins in non-palpable breast lesions. (author)

The Functional Movement Screen and Injury Risk: Association and Predictive Value in Active Men.

Science.gov (United States)

Bushman, Timothy T; Grier, Tyson L; Canham-Chervak, Michelle; Anderson, Morgan K; North, William J; Jones, Bruce H

2016-02-01

The Functional Movement Screen (FMS) is a series of 7 tests used to assess the injury risk in active populations. To determine the association of the FMS with the injury risk, assess predictive values, and identify optimal cut points using 3 injury types. Cohort study; Level of evidence, 2. Physically active male soldiers aged 18 to 57 years (N = 2476) completed the FMS. Demographic and fitness data were collected by survey. Medical record data for overuse injuries, traumatic injuries, and any injury 6 months after the FMS assessment were obtained. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated along with the receiver operating characteristic (ROC) to determine the area under the curve (AUC) and identify optimal cut points for the risk assessment. Risks, risk ratios (RRs), odds ratios (ORs), and 95% CIs were calculated to assess injury risks. Soldiers who scored ≤14 were at a greater risk for injuries compared with those who scored >14 using the composite score for overuse injuries (RR, 1.84; 95% CI, 1.63-2.09), traumatic injuries (RR, 1.26; 95% CI, 1.03-1.54), and any injury (RR, 1.60; 95% CI, 1.45-1.77). When controlling for other known injury risk factors, multivariate logistic regression analysis identified poor FMS performance (OR [score ≤14/19-21], 2.00; 95% CI, 1.42-2.81) as an independent risk factor for injuries. A cut point of ≤14 registered low measures of predictive value for all 3 injury types (sensitivity, 28%-37%; PPV, 19%-52%; AUC, 54%-61%). Shifting the injury risk cut point of ≤14 to the optimal cut points indicated by the ROC did not appreciably improve sensitivity or the PPV. Although poor FMS performance was associated with a higher risk of injuries, it displayed low sensitivity, PPV, and AUC. On the basis of these findings, the use of the FMS to screen for the injury risk is not recommended in this population because of the low predictive value and misclassification of the

Phase angle assessment by bioelectrical impedance analysis and its predictive value for malnutrition risk in hospitalized geriatric patients.

Science.gov (United States)

Varan, Hacer Dogan; Bolayir, Basak; Kara, Ozgur; Arik, Gunes; Kizilarslanoglu, Muhammet Cemal; Kilic, Mustafa Kemal; Sumer, Fatih; Kuyumcu, Mehmet Emin; Yesil, Yusuf; Yavuz, Burcu Balam; Halil, Meltem; Cankurtaran, Mustafa

2016-12-01

Phase angle (PhA) value determined by bioelectrical impedance analysis (BIA) is an indicator of cell membrane damage and body cell mass. Recent studies have shown that low PhA value is associated with increased nutritional risk in various group of patients. However, there have been only a few studies performed globally assessing the relationship between nutritional risk and PhA in hospitalized geriatric patients. The aim of the study is to evaluate the predictive value of the PhA for malnutrition risk in hospitalized geriatric patients. One hundred and twenty-two hospitalized geriatric patients were included in this cross-sectional study. Comprehensive geriatric assessment tests and BIA measurements were performed within the first 48 h after admission. Nutritional risk state of the patients was determined with NRS-2002. Phase angle values of the patients with malnutrition risk were compared with the patients that did not have the same risk. The independent variables for predicting malnutrition risk were determined. SPSS version 15 was utilized for the statistical analyzes. The patients with malnutrition risk had significantly lower phase angle values than the patients without malnutrition risk (p = 0.003). ROC curve analysis suggested that the optimum PhA cut-off point for malnutrition risk was 4.7° with 79.6 % sensitivity, 64.6 % specificity, 73.9 % positive predictive value, and 73.9 % negative predictive value. BMI, prealbumin, PhA, and Mini Mental State Examination Test scores were the independent variables for predicting malnutrition risk. PhA can be a useful, independent indicator for predicting malnutrition risk in hospitalized geriatric patients.

Predicting Success in an Online Course Using Expectancies, Values, and Typical Mode of Instruction

Science.gov (United States)

Zimmerman, Whitney Alicia

2017-01-01

Expectancies of success and values were used to predict success in an online undergraduate-level introductory statistics course. Students who identified as primarily face-to-face learners were compared to students who identified as primarily online learners. Expectancy value theory served as a model. Expectancies of success were operationalized as…

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Science.gov (United States)

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis.

Science.gov (United States)

Masso, Majid; Vaisman, Iosif I

2008-09-15

Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (DeltaDeltaG) and denaturant (DeltaDeltaG(H2O)) denaturations, as well as mutant thermal stability (DeltaT(m)), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal. We detail a computational mutagenesis technique based on a four-body, knowledge-based, statistical contact potential. For any mutation due to a single amino acid replacement in a protein, the method provides an empirical normalized measure of the ensuing environmental perturbation occurring at every residue position. A feature vector is generated for the mutant by considering perturbations at the mutated position and it's ordered six nearest neighbors in the 3-dimensional (3D) protein structure. These predictors of stability change are evaluated by applying machine learning tools to large training sets of mutants derived from diverse proteins that have been experimentally studied and described. Predictive models based on our combined approach are either comparable to, or in many cases significantly outperform, previously published results. A web server with supporting documentation is available at http://proteins.gmu.edu/automute.

Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

DEFF Research Database (Denmark)

Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V

2007-01-01

was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...

Predicting Basal Metabolic Rate in Men with Motor Complete Spinal Cord Injury.

Science.gov (United States)

Nightingale, Tom E; Gorgey, Ashraf S

2018-01-08

To assess the accuracy of existing basal metabolic rate (BMR) prediction equations in men with chronic (>1 year) spinal cord injury (SCI). The primary aim is to develop new SCI population-specific BMR prediction models, based on anthropometric, body composition and/or demographic variables that are strongly associated with BMR. Thirty men with chronic SCI (Paraplegic; n = 21, Tetraplegic; n = 9), aged 35 ± 11 years (mean ± SD) participated in this cross-sectional study. Criterion BMR values were measured by indirect calorimetry. Body composition (dual energy X-ray absorptiometry; DXA) and anthropometric measurements (circumferences and diameters) were also taken. Multiple linear regression analysis was performed to develop new SCI-specific BMR prediction models. Criterion BMR values were compared to values estimated from six existing and four developed prediction equations RESULTS: Existing equations that use information on stature, weight and/or age, significantly (P BMR by a mean of 14-17% (187-234 kcal/day). Equations that utilised fat-free mass (FFM) accurately predicted BMR. The development of new SCI-specific prediction models demonstrated that the addition of anthropometric variables (weight, height and calf circumference) to FFM (Model 3; r = 0.77), explained 8% more of the variance in BMR than FFM alone (Model 1; r = 0.69). Using anthropometric variables, without FFM, explained less of the variance in BMR (Model 4; r = 0.57). However, all the developed prediction models demonstrated acceptable mean absolute error ≤ 6%. BMR can be more accurately estimated when DXA derived FFM is incorporated into prediction equations. Utilising anthropometric measurements provides a promising alternative to improve the prediction of BMR, beyond that achieved by existing equations in persons with SCI.

PREDICTIVE VALUE OF CD34+ CELLS IN BLOOD OF PATIENT/DONOR BEFORE HEMATOPOIETIC STEM CELLS COLLECTION BY LEUKAPHERESIS

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Dragoslav Domanovič

2004-12-01

Full Text Available Background. In the study we tried to define a predictive value of the circulating CD34+ cells in patients/ donors blood for estimation of the hematopoietic stem cells (HSC collection efficacy determine the optimal time to initiate the collection by leukapheresis procedure.Methods. We retrospectively analyzed 75 collections of HSC using the Amicus cell separator in 39 patients and 15 donors. Circulating CD34+cell counts in patients/donors were compared to the achieved CD34+ cell yields to determine its predictive value for the collection of a targeted yield of > 2 × 106 CD34+ cells/kg body weight of patient.Results. The results of cell counts confirmed that mobilization regimens were successful and HSC collections efficient. High correlation coefficient (r = 0.82 between the number of circulating CD34+ cells before collection and CD34+ cell yield/kg of patient’s body weight was statistically significant (p < 0.05. With ROC analysis we determined the cut-off value 42 × 106/l CD34+ cell counts in the blood of patients/donors before collection that had a positive predictive value 87% and a negative predictive value 91.6%.Conclusions. Analysis showed that the number of circulating CD34+ cells before the procedure express a very high predictive value and can be used for determining the optimal time to initiate collection of HSC by leukapheresis.

Geometric constraints in semiclassical initial value representation calculations in Cartesian coordinates: accurate reduction in zero-point energy.

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Issack, Bilkiss B; Roy, Pierre-Nicholas

2005-08-22

An approach for the inclusion of geometric constraints in semiclassical initial value representation calculations is introduced. An important aspect of the approach is that Cartesian coordinates are used throughout. We devised an algorithm for the constrained sampling of initial conditions through the use of multivariate Gaussian distribution based on a projected Hessian. We also propose an approach for the constrained evaluation of the so-called Herman-Kluk prefactor in its exact log-derivative form. Sample calculations are performed for free and constrained rare-gas trimers. The results show that the proposed approach provides an accurate evaluation of the reduction in zero-point energy. Exact basis set calculations are used to assess the accuracy of the semiclassical results. Since Cartesian coordinates are used, the approach is general and applicable to a variety of molecular and atomic systems.

Diagnostic value of thallium-201 myocardial perfusion IQ-SPECT without and with computed tomography-based attenuation correction to predict clinically significant and insignificant fractional flow reserve

Science.gov (United States)

Tanaka, Haruki; Takahashi, Teruyuki; Ohashi, Norihiko; Tanaka, Koichi; Okada, Takenori; Kihara, Yasuki

2017-01-01

Abstract The aim of this study was to clarify the predictive value of fractional flow reserve (FFR) determined by myocardial perfusion imaging (MPI) using thallium (Tl)-201 IQ-SPECT without and with computed tomography-based attenuation correction (CT-AC) for patients with stable coronary artery disease (CAD). We assessed 212 angiographically identified diseased vessels using adenosine-stress Tl-201 MPI-IQ-SPECT/CT in 84 consecutive, prospectively identified patients with stable CAD. We compared the FFR in 136 of the 212 diseased vessels using visual semiquantitative interpretations of corresponding territories on MPI-IQ-SPECT images without and with CT-AC. FFR inversely correlated most accurately with regional summed difference scores (rSDS) in images without and with CT-AC (r = −0.584 and r = −0.568, respectively, both P system can predict FFR at an optimal cut-off of <0.80, and we propose a novel application of CT-AC to MPI-IQ-SPECT for predicting clinically significant and insignificant FFR even in nonobese patients. PMID:29390486

Dynamics of Flexible MLI-type Debris for Accurate Orbit Prediction

Science.gov (United States)

2014-09-01

debris for accurate propagation under perturbations”, in Proceedings of 65th International Astronautical Congress (IAC 2014), Toronto, Canada , 2014...Surveillance Network ( SSN ) was able to detect more than 900 pieces of debris that were at risk to damage operational spacecraft. In February 10, 2009...created two large debris clouds and the SSN reported that 382 pieces of debris from Iridium 33 and 893 pieces from Cosmos 2251 were created, and

An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

Science.gov (United States)

Wang, Shiyao; Deng, Zhidong; Yin, Gang

2016-02-24

A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

Directory of Open Access Journals (Sweden)

Shiyao Wang

2016-02-01

Full Text Available A high-performance differential global positioning system (GPS  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU/dead reckoning (DR data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

Analysis of the value of post-radiation prostate biopsy in predicting subsequent disease progression

International Nuclear Information System (INIS)

Benda, R.; Shamsa, F.; Meetze, K.; Bolton, S.; Littrup, P.; Grignon, D.; Washington, T.; Forman, J.D.

1997-01-01

Purpose: To analyze the value of Transrectal ultrasound(TRUS), Color flow doppler(CFD) and Prostate specific antigen(PSA) in identifying residual disease in the prostate status post external beam radiation therapy and to determine the value of this pathologic information in predicting subsequent disease progression. Materials and Methods: As part of four prospective protocols, 146 patients had scheduled TRUS guided prostate biopsies 6-25 months status post radiation therapy. The stage distribution was: 13% T1, 51% T2, and 36% T3/T4. Fifty six percent had neo-adjuvant hormones. Conformal photon or mixed neutron/photon irradiation was given to a median 2 Gy/fraction equivalent dose of 77 Gy(range 74 to 84 Gy). Following treatment, patients were assessed by digital rectal exam (DRE), PSA and TRUS guided biopsies at 6, 12 and/or 18 months. The ultrasound and CFD results were scored as normal, suspicious or abnormal. Sextant biopsies were obtained as well as ultrasound guided biopsies from any abnormal ultrasound or doppler area. The biopsies, all read by one pathologist (DG), were graded as negative, marked, moderate, minimal therapeutic effect or positive. The median followup post radiation therapy was 33.6 months and post biopsy was 25.3 months. Comparisons were done by Kappa index with corresponding 95% CI, chi square and Fisher's exact tests. Results: Twenty-eight patients had biopsies at both six and 12-18 months. Overall 35% of patients had all negative cores, 30% had at least one core showing a marked therapeutic effect, and 35% had at least one core showing moderate or minimal therapeutic effect or were positive. Although CFD correlated with a positive biopsy in 9% and a suspicious doppler identified cancer in 15% of cases, an abnormal TRUS identified cancer in 29.5% biopsies ((49(166))). However, a serum PSA >1.5ng/ml at the time of biopsy predicted 61% of positive biopsies ((23(38))). A negative biopsy was associated with low stage (≤T2c, p=0.001), low pre

Angiogenic Markers Predict Pregnancy Complications and Prolongation in Preeclampsia: Continuous Versus Cutoff Values.

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Saleh, Langeza; Vergouwe, Yvonne; van den Meiracker, Anton H; Verdonk, Koen; Russcher, Henk; Bremer, Henk A; Versendaal, Hans J; Steegers, Eric A P; Danser, A H Jan; Visser, Willy

2017-11-01

To assess the incremental value of a single determination of the serum levels of sFlt-1 (soluble Fms-like tyrosine kinase 1) and PlGF (placental growth factor) or their ratio, without using cutoff values, for the prediction of maternal and fetal/neonatal complications and pregnancy prolongation, 620 women with suspected/confirmed preeclampsia, aged 18 to 48 years, were included in a prospective, multicenter, observational cohort study. Women had singleton pregnancies and a median pregnancy duration of 34 (range, 20-41) weeks. Complications occurred in 118 women and 248 fetuses. The median duration between admission and delivery was 12 days. To predict prolongation, PlGF showed the highest incremental value ( R 2 =0.72) on top of traditional predictors (gestational age at inclusion, diastolic blood pressure, proteinuria, creatinine, uric acid, alanine transaminase, lactate dehydrogenase, and platelets) compared with R 2 =0.53 for the traditional predictors only. sFlt-1 showed the highest value to discriminate women with and without maternal complications (C-index=0.83 versus 0.72 for the traditional predictors only), and the sFlt-1/PlGF ratio showed the highest value to discriminate fetal/neonatal complications (C-index=0.86 versus 0.78 for the traditional predictors only). Applying previously suggested cutoff values for the sFlt-1/PlGF ratio yielded lower incremental values than applying continuous values. In conclusion, sFlt-1 and PlGF are strong and independent predictors for days until delivery along with maternal and fetal/neonatal complications on top of the traditional criteria. Their use as continuous variables (instead of applying cutoff values for different gestational ages) should now be tested in a prospective manner, making use of an algorithm calculating the risk of an individual woman with suspected/confirmed preeclampsia to develop complications. © 2017 American Heart Association, Inc.

Concordance and predictive value of two adverse drug event data sets.

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Cami, Aurel; Reis, Ben Y

2014-08-22

Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single "gold standard" ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. We systematically evaluated the concordance of two widely used ADE data sets - Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the

Satisfaction of psychotic patients with care and its value to predict outcomes

NARCIS (Netherlands)

Vermeulen, J. M.; Schirmbeck, N. F.; Van Tricht, M. J.; de Haan, L.

2018-01-01

Background: A key indicator of quality of treatment from the patient's perspective is expressed by satisfaction with care. Our aim was to (i) explore satisfaction and its relation to clinical outcome measures; and (ii) explore the predictive value of satisfaction for the course of outcomes over

How accurate is anatomic limb alignment in predicting mechanical limb alignment after total knee arthroplasty?

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Lee, Seung Ah; Choi, Sang-Hee; Chang, Moon Jong

2015-10-27

Anatomic limb alignment often differs from mechanical limb alignment after total knee arthroplasty (TKA). We sought to assess the accuracy, specificity, and sensitivity for each of three commonly used ranges for anatomic limb alignment (3-9°, 5-10° and 2-10°) in predicting an acceptable range (neutral ± 3°) for mechanical limb alignment after TKA. We also assessed whether the accuracy of anatomic limb alignment was affected by anatomic variation. This retrospective study included 314 primary TKAs. The alignment of the limb was measured with both anatomic and mechanical methods of measurement. We also measured anatomic variation, including the femoral bowing angle, tibial bowing angle, and neck-shaft angle of the femur. All angles were measured on the same full-length standing anteroposterior radiographs. The accuracy, specificity, and sensitivity for each range of anatomic limb alignment were calculated and compared using mechanical limb alignment as the reference standard. The associations between the accuracy of anatomic limb alignment and anatomic variation were also determined. The range of 2-10° for anatomic limb alignment showed the highest accuracy, but it was only 73 % (3-9°, 65 %; 5-10°, 67 %). The specificity of the 2-10° range was 81 %, which was higher than that of the other ranges (3-9°, 69 %; 5-10°, 67 %). However, the sensitivity of the 2-10° range to predict varus malalignment was only 16 % (3-9°, 35 %; 5-10°, 68 %). In addition, the sensitivity of the 2-10° range to predict valgus malalignment was only 43 % (3-9°, 71 %; 5-10°, 43 %). The accuracy of anatomical limb alignment was lower for knees with greater femoral (odds ratio = 1.2) and tibial (odds ratio = 1.2) bowing. Anatomic limb alignment did not accurately predict mechanical limb alignment after TKA, and its accuracy was affected by anatomic variation. Thus, alignment after TKA should be assessed by measuring mechanical alignment rather than anatomic

Ethnic differences in antepartum glucose values that predict postpartum dysglycemia and neonatal macrosomia.

Science.gov (United States)

Ajala, Olubukola; Chik, Constance

2018-03-31

Gestational diabetes (GDM) occurs more often in women from certain ethnic groups and is also associated with fetal macrosomia. In this study, we investigated the ability of a gestational diabetes screening test (GDS), the 2 h 75 g-Oral Glucose Tolerance Test (OGTT), and glycated hemoglobin (HbA1c) in predicting postpartum dysglycemia and fetal macrosomia in women of Caucasian, Filipino, Chinese and South-Asian descent. 848 women diagnosed with carbohydrate intolerance in pregnancy who completed a 2 h 75 g- OGTT within 6 months postpartum, were included in the study. Receiver Operating Characteristic curve analysis was used to test the ability of antepartum GDS, HbA1c and OGTT in predicting postpartum hyperglycemia, type 2 diabetes (T2D) and neonatal macrosomia (birth weight >4000 g). 20.2% had postpartum hyperglycemia while 3.8% had T2D. Those with postpartum dysglycemia were more likely to be non-Caucasian (South-Asian > Filipino > Chinese), have higher antepartum glucose values, require insulin during pregnancy and have cesarean births. Of HbA1c and the antepartum glucose values, a fasting glucose of ≥5.25 mmol/L was predictive of fetal macrosomia in Caucasians. 1 h glucose of ≥11.05 mmol/L was predictive of postpartum hyperglycemia, while 2 h glucose of ≥9.75 mmol/L was predictive of T2D; ethnicity influenced the predictive ability of these tests. Ethnicity influences the ability of antepartum glucose and HbA1c to predict the risk of macrosomia and postpartum dysglycemia. This information will help detect those most at risk of T2D. Copyright © 2018 Elsevier B.V. All rights reserved.

The Prognostic and Predictive Value of Soluble Type IV Collagen in Colorectal Cancer

DEFF Research Database (Denmark)

Rolff, Hans Christian; Christensen, Ib Jarle; Vainer, Ben

2016-01-01

PURPOSE: To investigate the prognostic and predictive biomarker value of type IV collagen in colorectal cancer. EXPERIMENTAL DESIGN: Retrospective evaluation of two independent cohorts of patients with colorectal cancer included prospectively in 2004-2005 (training set) and 2006-2008 (validation....... RESULTS: High levels of type IV collagen showed independent prognostic significance in both cohorts with hazard ratios (HRs; for a one-unit change on the log base 2 scale) of 2.25 [95% confidence intervals (CIs), 1.78-2.84; P ... and validation set, respectively. The prognostic impact was present both in patients with metastatic and nonmetastatic disease. The predictive value of the marker was investigated in stage II and III patients. In the training set, type IV collagen was prognostic both in the subsets of patients receiving...

Predictive value of late decelerations for fetal acidemia in unselective low-risk pregnancies.

Science.gov (United States)

Sameshima, Hiroshi; Ikenoue, Tsuyomu

2005-01-01

We evaluated the clinical significance of late decelerations (LD) of intrapartum fetal heart rate (FHR) monitoring to detect low pH (LD (occasional, 50%; recurrent, > or = 50%) and severity (reduced baseline FHR accelerations and variability) of LD, and low pH (test, and one-way analysis of variance with the Bonferroni/Dunn test. In the 5522 low-risk pregnancies, 301 showed occasional LD and 99 showed recurrent LD. Blood gases and pH values deteriorated as the incidence of LD increased and as baseline accelerations or variability was decreased. Positive predictive value for low pH (LD, and > 50% in recurrent LD with no baseline FHR accelerations and reduced variability. In low-risk pregnancies, information on LD combined with acceleration and baseline variability enables us to predict the potential incidence of fetal acidemia.

Endometrial Receptivity and its Predictive Value for IVF/ICSI-Outcome.

Science.gov (United States)

Heger, A; Sator, M; Pietrowski, D

2012-08-01

Endometrial receptivity plays a crucial role in the establishment of a healthy pregnancy in cycles of assisted reproduction. The endometrium as a key factor during reproduction can be assessed in multiple ways, most commonly through transvaginal grey-scale or 3-D ultrasound. It has been shown that controlled ovarian hyperstimulation has a great impact on the uterine lining, which leads to different study results for the predictive value of endometrial factors measured on different cycle days. There is no clear consensus on whether endometrial factors are appropriate to predict treatment outcome and if so, which one is suited best. The aim of this review is to summarize recent findings of studies about the influence of endometrial thickness, volume and pattern on IVF- and ICSI-treatment outcome and provide an overview of future developments in the field.

Predictive value of serum sST2 in preschool wheezers for development of asthma with high FeNO.

Science.gov (United States)

Ketelaar, M E; van de Kant, K D; Dijk, F N; Klaassen, E M; Grotenboer, N S; Nawijn, M C; Dompeling, E; Koppelman, G H

2017-11-01

Wheezing is common in childhood. However, current prediction models of pediatric asthma have only modest accuracy. Novel biomarkers and definition of subphenotypes may improve asthma prediction. Interleukin-1-receptor-like-1 (IL1RL1 or ST2) is a well-replicated asthma gene and associates with eosinophilia. We investigated whether serum sST2 predicts asthma and asthma with elevated exhaled NO (FeNO), compared to the commonly used Asthma Prediction Index (API). Using logistic regression modeling, we found that serum sST2 levels in 2-3 years-old wheezers do not predict doctors' diagnosed asthma at age 6 years. Instead, sST2 predicts a subphenotype of asthma characterized by increased levels of FeNO, a marker for eosinophilic airway inflammation. Herein, sST2 improved the predictive value of the API (AUC=0.70, 95% CI 0.56-0.84), but had also significant predictive value on its own (AUC=0.65, 95% CI 0.52-0.79). Our study indicates that sST2 in preschool wheezers has predictive value for the development of eosinophilic airway inflammation in asthmatic children at school age. © 2017 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

The predictive value of diagnostic sonography for the effectiveness of conservative treatment of tennis elbow

NARCIS (Netherlands)

Struijs, P. A. A.; Spruyt, M.; Assendelft, W. J. J.; van Dijk, C. N.

2005-01-01

OBJECTIVE. Tennis elbow is a common complaint. Several treatment strategies have been described, but an optimal strategy has not been identified. Sonographic imaging as a predictive,factor has never been studied. The aim of our study was to determine the value of sonographic findings in predicting

Differential maps, difference maps, interpolated maps, and long term prediction

International Nuclear Information System (INIS)

Talman, R.

1988-06-01

Mapping techniques may be thought to be attractive for the long term prediction of motion in accelerators, especially because a simple map can approximately represent an arbitrarily complicated lattice. The intention of this paper is to develop prejudices as to the validity of such methods by applying them to a simple, exactly solveable, example. It is shown that a numerical interpolation map, such as can be generated in the accelerator tracking program TEAPOT, predicts the evolution more accurately than an analytically derived differential map of the same order. Even so, in the presence of ''appreciable'' nonlinearity, it is shown to be impractical to achieve ''accurate'' prediction beyond some hundreds of cycles of oscillation. This suggests that the value of nonlinear maps is restricted to the parameterization of only the ''leading'' deviation from linearity. 41 refs., 6 figs

Combined Value of Red Blood Cell Distribution Width and Global Registry of Acute Coronary Events Risk Score for Predicting Cardiovascular Events in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

Directory of Open Access Journals (Sweden)

Na Zhao

Full Text Available Global Registry of Acute Coronary Events (GRACE risk score and red blood cell distribution width (RDW content can both independently predict major adverse cardiac events (MACEs in patients with acute coronary syndrome (ACS. We investigated the combined predictive value of RDW and GRACE risk score for cardiovascular events in patients with ACS undergoing percutaneous coronary intervention (PCI for the first time. We enrolled 480 ACS patients. During a median follow-up time of 37.2 months, 70 (14.58% patients experienced MACEs. Patients were divided into tertiles according to the baseline RDW content (11.30-12.90, 13.00-13.50, 13.60-16.40. GRACE score was positively correlated with RDW content. Multivariate Cox analysis showed that both GRACE score and RDW content were independent predictors of MACEs (hazard ratio 1.039; 95% confidence interval [CI] 1.024-1.055; p < 0.001; 1.699; 1.294-2.232; p < 0.001; respectively. Furthermore, Kaplan-Meier analysis demonstrated that the risk of MACEs increased with increasing RDW content (p < 0.001. For GRACE score alone, the area under the receiver operating characteristic (ROC curve for MACEs was 0.749 (95% CI: 0.707-0.787. The area under the ROC curve for MACEs increased to 0.805 (0.766-0.839, p = 0.034 after adding RDW content. The incremental predictive value of combining RDW content and GRACE risk score was significantly improved, also shown by the net reclassification improvement (NRI = 0.352, p < 0.001 and integrated discrimination improvement (IDI = 0.023, p = 0.002. Combining the predictive value of RDW and GRACE risk score yielded a more accurate predictive value for long-term cardiovascular events in ACS patients who underwent PCI as compared to each measure alone.

Prediction Methods for Blood Glucose Concentration

DEFF Research Database (Denmark)

“Recent Results on Glucose–Insulin Predictions by Means of a State Observer for Time-Delay Systems” by Pasquale Palumbo et al. introduces a prediction model which in real time predicts the insulin concentration in blood which in turn is used in a control system. The method is tested in simulation...... EEG signals to predict upcoming hypoglycemic situations in real-time by employing artificial neural networks. The results of a 30-day long clinical study with the implanted device and the developed algorithm are presented. The chapter “Meta-Learning Based Blood Glucose Predictor for Diabetic......, but the insulin amount is chosen using factors that account for this expectation. The increasing availability of more accurate continuous blood glucose measurement (CGM) systems is attracting much interest to the possibilities of explicit prediction of future BG values. Against this background, in 2014 a two...

Predictive Value of Whole Blood and Plasma Coagulation Tests for Intra- and Postoperative Bleeding Risk: A Systematic Review

DEFF Research Database (Denmark)

Larsen, Julie Brogaard; Hvas, Anne-Mette

2017-01-01

review of the existing literature assessing the ability of whole blood coagulation (thromboelastography [TEG]/thromboelastometry [ROTEM]/Sonoclot), platelet function tests, and standard plasma-based coagulation tests to predict bleeding in the perioperative setting. We searched PubMed and Embase...... value of testing in patients receiving antithrombotic medication. In general, studies reported low positive predictive values for perioperative testing, whereas negative predictive values were high. The studies yielded moderate areas under receiver operator characteristics (ROC) curve (for the majority...... recommend that both whole blood and plasma-based coagulation tests are primarily used in case of bleeding and not for screening in unselected patients prior to surgery....

Accurate thermoelastic tensor and acoustic velocities of NaCl

Energy Technology Data Exchange (ETDEWEB)

Marcondes, Michel L., E-mail: michel@if.usp.br [Physics Institute, University of Sao Paulo, Sao Paulo, 05508-090 (Brazil); Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455 (United States); Shukla, Gaurav, E-mail: shukla@physics.umn.edu [School of Physics and Astronomy, University of Minnesota, Minneapolis, 55455 (United States); Minnesota supercomputer Institute, University of Minnesota, Minneapolis, 55455 (United States); Silveira, Pedro da [Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455 (United States); Wentzcovitch, Renata M., E-mail: wentz002@umn.edu [Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455 (United States); Minnesota supercomputer Institute, University of Minnesota, Minneapolis, 55455 (United States)

2015-12-15

Despite the importance of thermoelastic properties of minerals in geology and geophysics, their measurement at high pressures and temperatures are still challenging. Thus, ab initio calculations are an essential tool for predicting these properties at extreme conditions. Owing to the approximate description of the exchange-correlation energy, approximations used in calculations of vibrational effects, and numerical/methodological approximations, these methods produce systematic deviations. Hybrid schemes combining experimental data and theoretical results have emerged as a way to reconcile available information and offer more reliable predictions at experimentally inaccessible thermodynamics conditions. Here we introduce a method to improve the calculated thermoelastic tensor by using highly accurate thermal equation of state (EoS). The corrective scheme is general, applicable to crystalline solids with any symmetry, and can produce accurate results at conditions where experimental data may not exist. We apply it to rock-salt-type NaCl, a material whose structural properties have been challenging to describe accurately by standard ab initio methods and whose acoustic/seismic properties are important for the gas and oil industry.

A NEW CLINICAL PREDICTION CRITERION ACCURATELY DETERMINES A SUBSET OF PATIENTS WITH BILATERAL PRIMARY ALDOSTERONISM BEFORE ADRENAL VENOUS SAMPLING.

Science.gov (United States)

Kocjan, Tomaz; Janez, Andrej; Stankovic, Milenko; Vidmar, Gaj; Jensterle, Mojca

2016-05-01

Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

Prediction of parenteral nutrition osmolarity by digital refractometry.

Science.gov (United States)

Chang, Wei-Kuo; Yeh, Ming-Kung

2011-05-01

Infusion of high-osmolarity parenteral nutrition (PN) formulations into a peripheral vein will damage the vessel. In this study, the authors developed a refractometric method to predict PN formulation osmolarity for patients receiving PN. Nutrients in PN formulations were prepared for Brix value and osmolality measurement. Brix value and osmolality measurement of the dextrose, amino acids, and electrolytes were used to evaluate the limiting factor of PN osmolarity prediction. A best-fit equation was generated to predict PN osmolarity (mOsm/L): 81.05 × Brix value--116.33 (R(2) > 0.99). To validate the PN osmolarity prediction by these 4 equations, a total of 500 PN admixtures were tested. The authors found strong linear relationships between the Brix values and the osmolality measurement of dextrose (R(2) = 0.97), amino acids (R(2) = 0.99), and electrolytes (R(2) > 0.96). When PN-measured osmolality was between 600 and 900 mOsm/kg, approximately 43%, 29%, 43%, and 0% of the predicted osmolarity obtained by equations 1, 2, 3, and 4 were outside the acceptable 90% to 110% confidence interval range, respectively. When measured osmolality was between 900 and 1,500 mOsm/kg, 31%, 100%, 85%, and 15% of the predicted osmolarity by equations 1, 2, 3, and 4 were outside the acceptable 90% to 110% confidence interval range, respectively. The refractive method permits accurate PN osmolarity prediction and reasonable quality assurance before PN formulation administration.

Predictive value of T2 relative signal intensity for response to somatostatin analogs in newly diagnosed acromegaly

Energy Technology Data Exchange (ETDEWEB)

Shen, Ming; Zhang, Qilin [Fudan University, Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Shanghai (China); Shanghai Pituitary Tumor Center, Shanghai (China); Liu, Wenjuan; Li, Yiming; Zhang, Zhaoyun; Ye, Hongying; He, Min; Lu, Bin; Yang, Yeping [Shanghai Pituitary Tumor Center, Shanghai (China); Fudan University, Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical College, Shanghai (China); Wang, Meng [Fudan University, Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical College, Shanghai (China); Soochow University, Division of Endocrinology, the Second Affiliated Hospital, Suzhou (China); Zhu, Jingjing [Shanghai Pituitary Tumor Center, Shanghai (China); Fudan University, Department of Neuropathology, Huashan Hospital, Shanghai Medical College, Shanghai (China); Ma, Zengyi; He, Wenqiang; Li, Shiqi; Shou, Xuefei; Qiao, Nidan; Ye, Zhao; Zhang, Yichao; Zhao, Yao; Wang, Yongfei [Fudan University, Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Shanghai (China); Shanghai Pituitary Tumor Center, Shanghai (China); Yao, Zhenwei [Shanghai Pituitary Tumor Center, Shanghai (China); Fudan University, Department of Radiology, Huashan Hospital, Shanghai Medical College, Shanghai (China); Lu, Yun [Fudan University, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Shanghai (China)

2016-11-15

The difficulty of predicting the efficacy of somatostatin analogs (SSA) is not fully resolved. Here, we quantitatively evaluated the predictive value of relative signal intensity (rSI) on T1- and T2-weighted magnetic resonance imaging (MRI) for the short-term efficacy (3 months) of SSA therapy in patients with active acromegaly and assessed the correlation between MRI rSI and expression of somatostatin receptors (SSTR). This was a retrospective review of prospectively recorded data. Ninety-two newly diagnosed patients (37 males and 55 females) with active acromegaly were recruited. All patients were treated with pre-surgical SSA, followed by reassessment and transspenoidal surgery. rSI values were generated by calculating the ratio of SI in the tumor to the SI of normal frontal white matter. The Youden indices were calculated to determine the optimal cutoff of rSI to determine the efficacy of SSA. The correlation between rSI and expression of SSTR2/5 was analyzed by the Spearman rank correlation coefficient. T2 rSI was strongly correlated with biochemical sensitivity to SSA. The cutoff value of T2 rSI to distinguish biochemical sensitivity was 1.205, with a positive predictive value (PPV) of 81.5 % and a negative predictive value (NPV) of 77.3 %. No correlation was found between MRI and tumor size sensitivity. Moreover, T2 rSI was negatively correlated with the expression of SSTR5. T2 rSI correlates with the expression of SSTR5 and quantitatively predicts the biochemical efficacy of SSA in acromegaly. (orig.)

Predictive value of T2 relative signal intensity for response to somatostatin analogs in newly diagnosed acromegaly

International Nuclear Information System (INIS)

Shen, Ming; Zhang, Qilin; Liu, Wenjuan; Li, Yiming; Zhang, Zhaoyun; Ye, Hongying; He, Min; Lu, Bin; Yang, Yeping; Wang, Meng; Zhu, Jingjing; Ma, Zengyi; He, Wenqiang; Li, Shiqi; Shou, Xuefei; Qiao, Nidan; Ye, Zhao; Zhang, Yichao; Zhao, Yao; Wang, Yongfei; Yao, Zhenwei; Lu, Yun

2016-01-01

The difficulty of predicting the efficacy of somatostatin analogs (SSA) is not fully resolved. Here, we quantitatively evaluated the predictive value of relative signal intensity (rSI) on T1- and T2-weighted magnetic resonance imaging (MRI) for the short-term efficacy (3 months) of SSA therapy in patients with active acromegaly and assessed the correlation between MRI rSI and expression of somatostatin receptors (SSTR). This was a retrospective review of prospectively recorded data. Ninety-two newly diagnosed patients (37 males and 55 females) with active acromegaly were recruited. All patients were treated with pre-surgical SSA, followed by reassessment and transspenoidal surgery. rSI values were generated by calculating the ratio of SI in the tumor to the SI of normal frontal white matter. The Youden indices were calculated to determine the optimal cutoff of rSI to determine the efficacy of SSA. The correlation between rSI and expression of SSTR2/5 was analyzed by the Spearman rank correlation coefficient. T2 rSI was strongly correlated with biochemical sensitivity to SSA. The cutoff value of T2 rSI to distinguish biochemical sensitivity was 1.205, with a positive predictive value (PPV) of 81.5 % and a negative predictive value (NPV) of 77.3 %. No correlation was found between MRI and tumor size sensitivity. Moreover, T2 rSI was negatively correlated with the expression of SSTR5. T2 rSI correlates with the expression of SSTR5 and quantitatively predicts the biochemical efficacy of SSA in acromegaly. (orig.)

Predictive value of T2 relative signal intensity for response to somatostatin analogs in newly diagnosed acromegaly.

Science.gov (United States)

Shen, Ming; Zhang, Qilin; Liu, Wenjuan; Wang, Meng; Zhu, Jingjing; Ma, Zengyi; He, Wenqiang; Li, Shiqi; Shou, Xuefei; Li, Yiming; Zhang, Zhaoyun; Ye, Hongying; He, Min; Lu, Bin; Yao, Zhenwei; Lu, Yun; Qiao, Nidan; Ye, Zhao; Zhang, Yichao; Yang, Yeping; Zhao, Yao; Wang, Yongfei

2016-11-01

The difficulty of predicting the efficacy of somatostatin analogs (SSA) is not fully resolved. Here, we quantitatively evaluated the predictive value of relative signal intensity (rSI) on T1- and T2-weighted magnetic resonance imaging (MRI) for the short-term efficacy (3 months) of SSA therapy in patients with active acromegaly and assessed the correlation between MRI rSI and expression of somatostatin receptors (SSTR). This was a retrospective review of prospectively recorded data. Ninety-two newly diagnosed patients (37 males and 55 females) with active acromegaly were recruited. All patients were treated with pre-surgical SSA, followed by reassessment and transspenoidal surgery. rSI values were generated by calculating the ratio of SI in the tumor to the SI of normal frontal white matter. The Youden indices were calculated to determine the optimal cutoff of rSI to determine the efficacy of SSA. The correlation between rSI and expression of SSTR2/5 was analyzed by the Spearman rank correlation coefficient. T2 rSI was strongly correlated with biochemical sensitivity to SSA. The cutoff value of T2 rSI to distinguish biochemical sensitivity was 1.205, with a positive predictive value (PPV) of 81.5 % and a negative predictive value (NPV) of 77.3 %. No correlation was found between MRI and tumor size sensitivity. Moreover, T2 rSI was negatively correlated with the expression of SSTR5. T2 rSI correlates with the expression of SSTR5 and quantitatively predicts the biochemical efficacy of SSA in acromegaly.

Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

Science.gov (United States)

Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

2010-01-01

DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

Directory of Open Access Journals (Sweden)

Sheila M Reynolds

2010-07-01

Full Text Available DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the

Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

Science.gov (United States)

Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

2010-07-08

DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

Assessing smoking status in disadvantaged populations: is computer administered self report an accurate and acceptable measure?

Directory of Open Access Journals (Sweden)

Bryant Jamie

2011-11-01

Full Text Available Abstract Background Self report of smoking status is potentially unreliable in certain situations and in high-risk populations. This study aimed to determine the accuracy and acceptability of computer administered self-report of smoking status among a low socioeconomic (SES population. Methods Clients attending a community service organisation for welfare support were invited to complete a cross-sectional touch screen computer health survey. Following survey completion, participants were invited to provide a breath sample to measure exposure to tobacco smoke in expired air. Sensitivity, specificity, positive predictive value and negative predictive value were calculated. Results Three hundred and eighty three participants completed the health survey, and 330 (86% provided a breath sample. Of participants included in the validation analysis, 59% reported being a daily or occasional smoker. Sensitivity was 94.4% and specificity 92.8%. The positive and negative predictive values were 94.9% and 92.0% respectively. The majority of participants reported that the touch screen survey was both enjoyable (79% and easy (88% to complete. Conclusions Computer administered self report is both acceptable and accurate as a method of assessing smoking status among low SES smokers in a community setting. Routine collection of health information using touch-screen computer has the potential to identify smokers and increase provision of support and referral in the community setting.

Assessing cutoff values for increased exercise blood pressure to predict incident hypertension in a general population.

Science.gov (United States)

Lorbeer, Roberto; Ittermann, Till; Völzke, Henry; Gläser, Sven; Ewert, Ralf; Felix, Stephan B; Dörr, Marcus

2015-07-01

Cutoff values for increased exercise blood pressure (BP) are not established in hypertension guidelines. The aim of the study was to assess optimal cutoff values for increased exercise BP to predict incident hypertension. Data of 661 normotensive participants (386 women) aged 25-77 years from the Study of Health in Pomerania (SHIP-1) with a 5-year follow-up were used. Exercise BP was measured at a submaximal level of 100 W and at maximum level of a symptom-limited cycle ergometry test. Cutoff values for increased exercise BP were defined at the maximum sum of sensitivity and specificity for the prediction of incident hypertension. The area under the receiver-operating characteristic curve (AUC) and net reclassification index (NRI) were calculated to investigate whether increased exercise BP adds predictive value for incident hypertension beyond established cardiovascular risk factors. In men, values of 160  mmHg (100  W level; AUC = 0.7837; NRI = 0.534, P AUC = 0.7677; NRI = 0.340, P = 0.003) were detected as optimal cutoff values for the definition of increased exercise SBP. A value of 190  mmHg (AUC = 0.8347; NRI = 0.519, P < 0.001) showed relevance for the definition of increased exercise SBP in women at the maximum level. According to our analyses, 190 and 210  mmHg are clinically relevant cutoff values for increased exercise SBP at the maximum exercise level of cycle ergometry test for women and men, respectively. In addition, for men, our analyses provided a cutoff value of 160  mmHg for increased exercise SBP at the 100  W level.

Cluster abundance in chameleon f ( R ) gravity I: toward an accurate halo mass function prediction

Energy Technology Data Exchange (ETDEWEB)

Cataneo, Matteo; Rapetti, David [Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen (Denmark); Lombriser, Lucas [Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ (United Kingdom); Li, Baojiu, E-mail: matteoc@dark-cosmology.dk, E-mail: drapetti@dark-cosmology.dk, E-mail: llo@roe.ac.uk, E-mail: baojiu.li@durham.ac.uk [Institute for Computational Cosmology, Department of Physics, Durham University, South Road, Durham DH1 3LE (United Kingdom)

2016-12-01

We refine the mass and environment dependent spherical collapse model of chameleon f ( R ) gravity by calibrating a phenomenological correction inspired by the parameterized post-Friedmann framework against high-resolution N -body simulations. We employ our method to predict the corresponding modified halo mass function, and provide fitting formulas to calculate the enhancement of the f ( R ) halo abundance with respect to that of General Relativity (GR) within a precision of ∼< 5% from the results obtained in the simulations. Similar accuracy can be achieved for the full f ( R ) mass function on the condition that the modeling of the reference GR abundance of halos is accurate at the percent level. We use our fits to forecast constraints on the additional scalar degree of freedom of the theory, finding that upper bounds competitive with current Solar System tests are within reach of cluster number count analyses from ongoing and upcoming surveys at much larger scales. Importantly, the flexibility of our method allows also for this to be applied to other scalar-tensor theories characterized by a mass and environment dependent spherical collapse.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

Characterization and Predictive Value of Segmental Curve Flexibility in Adolescent Idiopathic Scoliosis Patients

DEFF Research Database (Denmark)

Yao, Guanfeng; Cheung, Jason P Y; Shigematsu, Hideki

2017-01-01

STUDY DESIGN: A prospective radiographic analysis of adolescent idiopathic scoliosis (AIS) patients managed with alternate-level pedicle screw fixation was performed. OBJECTIVE: The objective of this study was to characterize segmental curve flexibility and to determine its predictive value...

Predictive value of general movements' quality in low-risk infants for minor neurological dysfunction and behavioural problems at preschool age

NARCIS (Netherlands)

Bennema, Anne N; Schendelaar, Pamela; Seggers, Jorien; Haadsma, Maaike L; Heineman, Maas Jan; Hadders-Algra, Mijna

Background: General movement (GM) assessment is a well-established tool to predict cerebral palsy in high-risk infants. Little is known on the predictive value of GM assessment in low-risk populations. Aims: To assess the predictive value of GM quality in early infancy for the development of the

Predicting Customers Churn in a Relational Database

Directory of Open Access Journals (Sweden)

Catalin CIMPOERU

2014-01-01

Full Text Available This paper explores how two main classical classification models work and generate predictions through a commercial solution of relational database management system (Microsoft SQL Server 2012. The aim of the paper is to accurately predict churn among a set of customers defined by various discrete and continuous variables, derived from three main data sources: the commercial transactions history; the users’ behavior or events happening on their computers; the specific identity information provided by the customers themselves. On a theoretical side, the paper presents the main concepts and ideas underlying the Decision Tree and Naïve Bayes classifiers and exemplifies some of them with actual hand-made calculations of the data being modeled by the software. On an analytical and practical side, the paper analyzes the graphs and tables generated by the classifying models and also reveal the main data insights. In the end, the classifiers’ accuracy is evaluated based on the test data method. The most accurate one is chosen for generating predictions on the customers’ data where the values of the response variable are not known.

Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data

International Nuclear Information System (INIS)

Hormuth II, David A; Weis, Jared A; Barnes, Stephanie L; Miga, Michael I; Yankeelov, Thomas E; Rericha, Erin C; Quaranta, Vito

2015-01-01

Reaction–diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction–diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor ‘grown’ for ten days as dictated by the reaction–diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error 0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction–diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions. (paper)

Can Older Adults Accurately Report their Use of Physical Rehabilitation Services?

Science.gov (United States)

Freedman, Vicki A; Kasper, Judith D; Jette, Alan

2018-04-10

To explore accuracy of rehabilitation service use reports by older adults and variation in accuracy by demographic characteristics, time since use, duration, and setting (inpatient, outpatient, home). We calculate the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of survey-based measures from an observational panel study, the National Health and Aging Trends Study (NHATS), relative to measures developed from linked Medicare claims. Community-dwelling sample of Medicare fee-for-service beneficiaries in 2015 NHATS who were enrolled in Medicare Parts A and B for 12 months prior to their interview (N=4,228). Respondents were asked whether they received rehabilitation services in the last year and the duration and location of services. Healthcare Common Procedure Coding System codes and Revenue Center codes were used to identify Medicare-eligible rehabilitation service. Survey-based reports and Medicare claims yielded similar estimates of rehabilitation use over the last year. Self-reported measures had high sensitivity (77%) and PPV (80%) and even higher specificity and NPV (approaching 95%). However, in adjusted models sensitivity was lower for Black enrollees, the very old, and those with lower education levels. Survey-based measures of rehabilitation accurately captured use over the past year but differential reporting should be considered when characterizing rehabilitation use in certain subgroups of older Americans. Copyright © 2018. Published by Elsevier Inc.

Attributing Predictable Signals at Subseasonal Timescales

Science.gov (United States)

Shelly, A.; Norton, W.; Rowlands, D.; Beech-Brandt, J.

2016-12-01

Subseasonal forecasts offer significant economic value in the management of energy infrastructure and through the associated financial markets. Models are now accurate enough to provide, for some occasions, good forecasts in the subseasonal range. However, it is often not clear what the drivers of these subseasonal signals are and if the forecasts could be more accurate with better representation of physical processes. Also what are the limits of predictability in the subseasonal range? To address these questions, we have run the ECMWF monthly forecast system over the 2015/16 winter with a set of 6 week ensemble integrations initialised every week over the period. In these experiments, we have relaxed the band 15N to 15S to reanalysis fields. Hence, we have a set of forecasts where the tropics is constrained to actual events and we can analyse the changes in predictability in middle latitudes - in particular in regions of high energy consumption like North America and Europe. Not surprisingly, the forecast of some periods are significantly improved while others show no improvement. We discuss events/patterns that have extended range predictability and also the tropical forecast errors which prevent the potential predictability in middle latitudes from being realised.

Assessment of the Swedish EQ-5D experience-based value sets in a total hip replacement population.

Science.gov (United States)

Nemes, Szilárd; Burström, Kristina; Zethraeus, Niklas; Eneqvist, Ted; Garellick, Göran; Rolfson, Ola

2015-12-01

All patients undergoing elective total hip replacement (THR) in Sweden are asked to complete a survey, including the EQ-5D. Thus far, EQ-5D values have been presented using the UK TTO value set based on hypothetical values. Shift to the use of the recently introduced Swedish experience-based value set, derived from a representative Swedish population, is an appealing alternative. To investigate how accurate the Swedish experience-based VAS value set predicts observed EQ VAS values and to compare correlations between Swedish and UK value sets including two provisional value sets derived from the THR population. Pre- and one-year postoperative data from 56,062 THR patients from the Swedish Hip Arthroplasty Register were used. Agreement between the observed and the predicted EQ VAS values was assessed with correlation. Based on pre- and postoperative data, we constructed two provisional VAS value sets. Correlations between observed and calculated values using the Swedish VAS value set were moderate (r = 0.46) in preoperative data and high (r = 0.72) in postoperative data. Correlations between UK and register-based value sets were constantly lower compared to Swedish value sets. Register-based values and Swedish values were highly correlated. The Swedish value sets are more accurate in terms of representation of the Swedish THR patients than the currently used UK TTO value set. We find it feasible to use the experience-based Swedish value sets for further presentation of EQ-5D values in the Swedish THR population.

Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

Science.gov (United States)

Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

2015-09-18

Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

The predictive value of hunger score on gastric evacuation after oral intake of carbohydrate solution.

Science.gov (United States)

Weiji, Qiu; Shitong, Li; Yu, Luo; Tianfang, Hua; Ning, Kong; Lina, Zhang

2018-01-12

Surgical patients are asked to fast for a sufficient duration to ensure that the amount of residual liquid in the stomach is within the safe range, thereby reducing the risk of gastric reflux perioperatively. The authors hypothesized that subjective hunger numerical rating scale (NRS) score could also help assess the process of gastric emptying and determine the amount of fluid remaining in the stomach. The current study consisted of healthy volunteers recruited by advertisement and mutual introduction. Participants were asked to rate their subjective hunger feeling every 30 min after oral administration of 8 mL/kg carbohydrate nutrient solution that contained 10% maltodextrin and 2.5% sucrose. Consecutively, the gastric residual fluid was measured by magnetic resonance imagining (MRI). The Spearman's correlation coefficient, the ROC curves and the stepwise regression were used to analyze the predictive value of NRS for the gastric emptying process. The cohort consisted of 29 healthy volunteers enrolled in this study. The area under ROC curves estimated by the NRS score for the gastric residual volume of 2 mL/kg, 1 mL/kg, and 0.5 mL/kg were AUC 2.0  = 0.78, AUC 1.0  = 0.76, and AUC 0.5  = 0.72, respectively. The correlation coefficient between the NRS score and the residual liquid in the stomach was -0.57 (P hunger NRS score can not accurately predict the gastric residual volume, but it can provide a reference for clinicians to judge the gastric emptying process and it should be used as a second check after oral intake of clear fluids before surgery according to the new fasting protocol.

ABC/2 Method Does not Accurately Predict Cerebral Arteriovenous Malformation Volume.

Science.gov (United States)

Roark, Christopher; Vadlamudi, Venu; Chaudhary, Neeraj; Gemmete, Joseph J; Seinfeld, Joshua; Thompson, B Gregory; Pandey, Aditya S

2018-02-01

Stereotactic radiosurgery (SRS) is a treatment option for cerebral arteriovenous malformations (AVMs) to prevent intracranial hemorrhage. The decision to proceed with SRS is usually based on calculated nidal volume. Physicians commonly use the ABC/2 formula, based on digital subtraction angiography (DSA), when counseling patients for SRS. To determine whether AVM volume calculated using the ABC/2 method on DSA is accurate when compared to the exact volume calculated from thin-cut axial sections used for SRS planning. Retrospective search of neurovascular database to identify AVMs treated with SRS from 1995 to 2015. Maximum nidal diameters in orthogonal planes on DSA images were recorded to determine volume using ABC/2 formula. Nidal target volume was extracted from operative reports of SRS. Volumes were then compared using descriptive statistics and paired t-tests. Ninety intracranial AVMs were identified. Median volume was 4.96 cm3 [interquartile range (IQR) 1.79-8.85] with SRS planning methods and 6.07 cm3 (IQR 1.3-13.6) with ABC/2 methodology. Moderate correlation was seen between SRS and ABC/2 (r = 0.662; P ABC/2 (t = -3.2; P = .002). When AVMs were dichotomized based on ABC/2 volume, significant differences remained (t = 3.1, P = .003 for ABC/2 volume ABC/2 volume > 7 cm3). The ABC/2 method overestimates cerebral AVM volume when compared to volumetric analysis from SRS planning software. For AVMs > 7 cm3, the overestimation is even greater. SRS planning techniques were also significantly different than values derived from equations for cones and cylinders. Copyright © 2017 by the Congress of Neurological Surgeons

Predictive performance of universal termination of resuscitation rules in an Asian community: are they accurate enough?

Science.gov (United States)

Chiang, Wen-Chu; Ko, Patrick Chow-In; Chang, Anna Marie; Liu, Sot Shih-Hung; Wang, Hui-Chih; Yang, Chih-Wei; Hsieh, Ming-Ju; Chen, Shey-Ying; Lai, Mei-Shu; Ma, Matthew Huei-Ming

2015-04-01

Prehospital termination of resuscitation (TOR) rules have not been widely validated outside of Western countries. This study evaluated the performance of TOR rules in an Asian metropolitan with a mixed-tier emergency medical service (EMS). We analysed the Utstein registry of adult, non-traumatic out-of-hospital cardiac arrests (OHCAs) in Taipei to test the performance of TOR rules for advanced life support (ALS) or basic life support (BLS) providers. ALS and BLS-TOR rules were tested in OHCAs among three subgroups: (1) resuscitated by ALS, (2) by BLS and (3) by mixed ALS and BLS. Outcome definition was in-hospital death. Sensitivity, specificity, positive predictive value (PPV), negative predictive value and decreased transport rate (DTR) among various provider combinations were calculated. Of the 3489 OHCAs included, 240 were resuscitated by ALS, 1727 by BLS and 1522 by ALS and BLS. Overall survival to hospital discharge was 197 patients (5.6%). Specificity and PPV of ALS-TOR and BLS-TOR for identifying death ranged from 70.7% to 81.8% and 95.1% to 98.1%, respectively. Applying the TOR rules would have a DTR of 34.2-63.9%. BLS rules had better predictive accuracy and DTR than ALS rules among all subgroups. Application of the ALS and BLS TOR rules would have decreased OHCA transported to the hospital, and BLS rules are reasonable as the universal criteria in a mixed-tier EMS. However, 1.9-4.9% of those who survived would be misclassified as non-survivors, raising concern of compromising patient safety for the implementation of the rules. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Fast and accurate determination of modularity and its effect size

International Nuclear Information System (INIS)

Treviño, Santiago III; Nyberg, Amy; Bassler, Kevin E; Del Genio, Charo I

2015-01-01

We present a fast spectral algorithm for community detection in complex networks. Our method searches for the partition with the maximum value of the modularity via the interplay of several refinement steps that include both agglomeration and division. We validate the accuracy of the algorithm by applying it to several real-world benchmark networks. On all these, our algorithm performs as well or better than any other known polynomial scheme. This allows us to extensively study the modularity distribution in ensembles of Erdős–Rényi networks, producing theoretical predictions for means and variances inclusive of finite-size corrections. Our work provides a way to accurately estimate the effect size of modularity, providing a z-score measure of it and enabling a more informative comparison of networks with different numbers of nodes and links. (paper)

Accurate prediction of complex free surface flow around a high speed craft using a single-phase level set method

Science.gov (United States)

Broglia, Riccardo; Durante, Danilo

2017-11-01

This paper focuses on the analysis of a challenging free surface flow problem involving a surface vessel moving at high speeds, or planing. The investigation is performed using a general purpose high Reynolds free surface solver developed at CNR-INSEAN. The methodology is based on a second order finite volume discretization of the unsteady Reynolds-averaged Navier-Stokes equations (Di Mascio et al. in A second order Godunov—type scheme for naval hydrodynamics, Kluwer Academic/Plenum Publishers, Dordrecht, pp 253-261, 2001; Proceedings of 16th international offshore and polar engineering conference, San Francisco, CA, USA, 2006; J Mar Sci Technol 14:19-29, 2009); air/water interface dynamics is accurately modeled by a non standard level set approach (Di Mascio et al. in Comput Fluids 36(5):868-886, 2007a), known as the single-phase level set method. In this algorithm the governing equations are solved only in the water phase, whereas the numerical domain in the air phase is used for a suitable extension of the fluid dynamic variables. The level set function is used to track the free surface evolution; dynamic boundary conditions are enforced directly on the interface. This approach allows to accurately predict the evolution of the free surface even in the presence of violent breaking waves phenomena, maintaining the interface sharp, without any need to smear out the fluid properties across the two phases. This paper is aimed at the prediction of the complex free-surface flow field generated by a deep-V planing boat at medium and high Froude numbers (from 0.6 up to 1.2). In the present work, the planing hull is treated as a two-degree-of-freedom rigid object. Flow field is characterized by the presence of thin water sheets, several energetic breaking waves and plungings. The computational results include convergence of the trim angle, sinkage and resistance under grid refinement; high-quality experimental data are used for the purposes of validation, allowing to

Clinical value of CT-based preoperative software assisted lung lobe volumetry for predicting postoperative pulmonary function after lung surgery

Science.gov (United States)

Wormanns, Dag; Beyer, Florian; Hoffknecht, Petra; Dicken, Volker; Kuhnigk, Jan-Martin; Lange, Tobias; Thomas, Michael; Heindel, Walter

2005-04-01

This study was aimed to evaluate a morphology-based approach for prediction of postoperative forced expiratory volume in one second (FEV1) after lung resection from preoperative CT scans. Fifteen Patients with surgically treated (lobectomy or pneumonectomy) bronchogenic carcinoma were enrolled in the study. A preoperative chest CT and pulmonary function tests before and after surgery were performed. CT scans were analyzed by prototype software: automated segmentation and volumetry of lung lobes was performed with minimal user interaction. Determined volumes of different lung lobes were used to predict postoperative FEV1 as percentage of the preoperative values. Predicted FEV1 values were compared to the observed postoperative values as standard of reference. Patients underwent lobectomy in twelve cases (6 upper lobes; 1 middle lobe; 5 lower lobes; 6 right side; 6 left side) and pneumonectomy in three cases. Automated calculation of predicted postoperative lung function was successful in all cases. Predicted FEV1 ranged from 54% to 95% (mean 75% +/- 11%) of the preoperative values. Two cases with obviously erroneous LFT were excluded from analysis. Mean error of predicted FEV1 was 20 +/- 160 ml, indicating absence of systematic error; mean absolute error was 7.4 +/- 3.3% respective 137 +/- 77 ml/s. The 200 ml reproducibility criterion for FEV1 was met in 11 of 13 cases (85%). In conclusion, software-assisted prediction of postoperative lung function yielded a clinically acceptable agreement with the observed postoperative values. This method might add useful information for evaluation of functional operability of patients with lung cancer.

Accurate mass measurements of very short-lived nuclei. Prerequisites for high-accuracy investigations of superallowed β-decays

International Nuclear Information System (INIS)

Herfurth, F.; Kellerbauer, A.; Sauvan, E.; Ames, F.; Engels, O.; Audi, G.; Lunney, D.; Beck, D.; Blaum, K.; Kluge, H.J.; Scheidenberger, C.; Sikler, G.; Weber, C.; Bollen, G.; Schwarz, S.; Moore, R.B.; Oinonen, M.

2002-01-01

Mass measurements of 34 Ar, 73-78 Kr, and 74,76 Rb were performed with the Penning-trap mass spectrometer ISOLTRAP. Very accurate Q EC -values are needed for the investigations of the Ft-value of 0 + → 0 + nuclear β-decays used to test the standard model predictions for weak interactions. The necessary accuracy on the Q EC -value requires the mass of mother and daughter nuclei to be measured with δm/m ≤ 3 . 10 -8 . For most of the measured nuclides presented here this has been reached. The 34 Ar mass has been measured with a relative accuracy of 1.1 .10 -8 . The Q EC -value of the 34 Ar 0 + → 0 + decay can now be determined with an uncertainty of about 0.01%. Furthermore, 74 Rb is the shortest-lived nuclide ever investigated in a Penning trap. (orig.)

The Predictive Value of Germline Polymorphisms in Patients with NSCLC

DEFF Research Database (Denmark)

Nygaard, Anneli Dowler; Spindler, Karen-Lise Garm; Andersen, Rikke Fredslund

2010-01-01

urgently needed. Single Nucleotide Polymorphisms (SNPs) are stable markers of potential clinical value and the study aimed at evaluating their use in lung cancer patients given standard chemotherapy. Genomic DNA was extracted from a pre-treatment blood sample drawn from patients with advanced Non....... Haplotypes were estimated and analyzed when relevant. There were no significant associations between SNPs in the EGF system or the DNA-repair system and RR, PFS or OS. In contrast, the VEGF+405, VEGF-460 and VEGF-2579, heterozygous patients had a higher response rate and longer PFS than homozygous patients....... Haplotype analysis of the VEGF+405 and VEGF- 460 supported our findings. These results were, however, not confirmed in the validation cohort. Although significant results regarding VEGF related SNPs, in the primary analysis, no predictive value of a broad panel of SNPs in NSCLC was found in the validation...

Sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate prediction in Sahelian goats after progesterone impregnated sponge synchronization

Directory of Open Access Journals (Sweden)

Justin Kouamo

2014-09-01

Full Text Available Aim: This study was aimed to evaluate the sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate (PR prediction in Sahelian goats after progesterone impregnated sponge synchronization within the framework of caprine artificial insemination (AI program in Fatick (Senegal. Materials and Methods: Of 193 candidate goats in AI program, 167 were selected (day 50 in six villages. Estrus was synchronized by progesterone impregnated sponges installed for 11 days. Two days before the time of sponge removal (day 4, each goat was treated with 500 IU of equine chorionic gonadotropin and 50 μg of dcloprostenol. All goats were inseminated (day 0 with alpine goat semen from France at 45±3 h after sponge removal (day 2. Real-time B-mode ultrasonography was performed at day 50, day 13, day 0, day 40 and day 60 post-AI. Results: Selection rate, estrus response rate, AI rate, PR at days 40 and days 60 were 86.53%; 71.85%; 83.34%; 51% and 68% (p<0.05 respectively. Value of sensitivity, specificity, positive and negative predictive value, accuracy, total conformity, conformity of correct positive, conformity of correct negative and discordance of pregnancy diagnosis by trans-abdominal ultrasonography (TU were 98.03%; 63.26%; 73.52%; 3.12%; 81%; 81%; 50%; 31% and 19%, respectively. Conclusion: These results indicate that the TU can be performed in goats under traditional condition and emphasized the importance of re-examination of goats with negative or doubtful TU diagnoses performed at day 40 post-AI.

Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations.

Science.gov (United States)

Wang, D; Salah El-Basyoni, I; Stephen Baenziger, P; Crossa, J; Eskridge, K M; Dweikat, I

2012-11-01

Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.

The relative value of operon predictions

NARCIS (Netherlands)

Brouwer, Rutger W. W.; Kuipers, Oscar P.; van Hijum, Sacha A. F. T.

For most organisms, computational operon predictions are the only source of genome-wide operon information. Operon prediction methods described in literature are based on (a combination of) the following five criteria: (i) intergenic distance, (ii) conserved gene clusters, (iii) functional relation,

Detailed comparative study regarding different formulae of predicting the iron losses in a machine excited by non-sinusoidal supply

International Nuclear Information System (INIS)

El-Kharashi, Eyhab

2014-01-01

Variable-speed drives in any machine provide an accurate control and high-energy efficiency. More and more often machines are excited by non-sinusoidal voltages. Predicting the amount of iron losses in non-sinusoidal excitation is important. The paper aims to achieve accurate efficiency estimation by presenting a new modified calculation method to predict the iron losses. In a switched reluctance motor, the iron losses can't be ignored, it has considered value. This paper presents conventional and modified Steinmetz formulae for the estimation of the iron losses. The conventional Steinmetz formula consists of three terms: hysteresis, eddy current and anomalous losses. The equations of hysteresis and eddy current losses depend mainly on the value of the peak flux density. The reason to modify the Steinmetz formula is to avoid the need of knowing the peak flux density and the anomalous losses in accurate figures. The paper also explains and clarifies the methods of using both the conventional as well as the modified Steinmetz formulae in accurate calculation of the iron losses in different sections of the magnetic circuit. For both formulae, a comparison is made between the distributions of the iron losses in different parts of the magnetic circuit and the efficiencies. - Highlights: • The paper aims to achieve accurate efficiency estimation. • The predicted iron loss by the conventional Steinmetz formula is inaccurate. • The modified Steinmetz formula is more accurate because it includes the minor loops losses caused by each flux density. • The paper compared the predicted losses obtained by the two different formals to stand on the degree of accuracy

Genomic Prediction of Manganese Efficiency in Winter Barley

Directory of Open Access Journals (Sweden)

Florian Leplat

2016-07-01

Full Text Available Manganese efficiency is a quantitative abiotic stress trait controlled by several genes each with a small effect. Manganese deficiency leads to yield reduction in winter barley ( L.. Breeding new cultivars for this trait remains difficult because of the lack of visual symptoms and the polygenic features of the trait. Hence, Mn efficiency is a potential suitable trait for a genomic selection (GS approach. A collection of 248 winter barley varieties was screened for Mn efficiency using Chlorophyll (Chl fluorescence in six environments prone to induce Mn deficiency. Two models for genomic prediction were implemented to predict future performance and breeding value of untested varieties. Predictions were obtained using multivariate mixed models: best linear unbiased predictor (BLUP and genomic best linear unbiased predictor (G-BLUP. In the first model, predictions were based on the phenotypic evaluation, whereas both phenotypic and genomic marker data were included in the second model. Accuracy of predicting future phenotype, , and accuracy of predicting true breeding values, , were calculated and compared for both models using six cross-validation (CV schemes; these were designed to mimic plant breeding programs. Overall, the CVs showed that prediction accuracies increased when using the G-BLUP model compared with the prediction accuracies using the BLUP model. Furthermore, the accuracies [] of predicting breeding values were more accurate than accuracy of predicting future phenotypes []. The study confirms that genomic data may enhance the prediction accuracy. Moreover it indicates that GS is a suitable breeding approach for quantitative abiotic stress traits.

Prospective Cohort Study Evaluating the Prognostic Value of Simple EEG Parameters in Postanoxic Coma.

Science.gov (United States)

Azabou, Eric; Fischer, Catherine; Mauguiere, François; Vaugier, Isabelle; Annane, Djillali; Sharshar, Tarek; Lofaso, Fréderic

2016-01-01

We prospectively studied early bedside standard EEG characteristics in 61 acute postanoxic coma patients. Five simple EEG features, namely, isoelectric, discontinuous, nonreactive to intense auditory and nociceptive stimuli, dominant delta frequency, and occurrence of paroxysms were classified yes or no. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) of each of these variables for predicting an unfavorable outcome, defined as death, persistent vegetative state, minimally conscious state, or severe neurological disability, as assessed 1 year after coma onset were computed as well as Synek's score. The outcome was unfavorable in 56 (91.8%) patients. Sensitivity, specificity, PPV, NPV, and AUC of nonreactive EEG for predicting an unfavorable outcome were 84%, 80%, 98%, 31%, and 0.82, respectively; and were all very close to the ones of Synek score>3, which were 82%, 80%, 98%, 29%, and 0.81, respectively. Specificities for predicting an unfavorable outcome were 100% for isoelectric, discontinuous, or dominant delta activity EEG. These 3 last features were constantly associated to unfavorable outcome. Absent EEG reactivity strongly predicted an unfavorable outcome in postanoxic coma, and performed as accurate as a Synek score>3. Analyzing characteristics of some simple EEG features may easily help nonneurophysiologist physicians to investigate prognostic issue of postanoxic coma patient. In this study (a) discontinuous, isoelectric, or delta-dominant EEG were constantly associated with unfavorable outcome and (b) nonreactive EEG performed prognostic as accurate as a Synek score>3. © EEG and Clinical Neuroscience Society (ECNS) 2015.

Non-isothermal kinetics model to predict accurate phase transformation and hardness of 22MnB5 boron steel

Energy Technology Data Exchange (ETDEWEB)

Bok, H.-H.; Kim, S.N.; Suh, D.W. [Graduate Institute of Ferrous Technology, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongsangbuk-do (Korea, Republic of); Barlat, F., E-mail: f.barlat@postech.ac.kr [Graduate Institute of Ferrous Technology, POSTECH, San 31, Hyoja-dong, Nam-gu, Pohang, Gyeongsangbuk-do (Korea, Republic of); Lee, M.-G., E-mail: myounglee@korea.ac.kr [Department of Materials Science and Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul (Korea, Republic of)

2015-02-25

A non-isothermal phase transformation kinetics model obtained by modifying the well-known JMAK approach is proposed for application to a low carbon boron steel (22MnB5) sheet. In the modified kinetics model, the parameters are functions of both temperature and cooling rate, and can be identified by a numerical optimization method. Moreover, in this approach the transformation start and finish temperatures are variable instead of the constants that depend on chemical composition. These variable reference temperatures are determined from the measured CCT diagram using dilatation experiments. The kinetics model developed in this work captures the complex transformation behavior of the boron steel sheet sample accurately. In particular, the predicted hardness and phase fractions in the specimens subjected to a wide range of cooling rates were validated by experiments.

School system evaluation by value added analysis under endogeneity.

Science.gov (United States)

Manzi, Jorge; San Martín, Ernesto; Van Bellegem, Sébastien

2014-01-01

Value added is a common tool in educational research on effectiveness. It is often modeled as a (prediction of a) random effect in a specific hierarchical linear model. This paper shows that this modeling strategy is not valid when endogeneity is present. Endogeneity stems, for instance, from a correlation between the random effect in the hierarchical model and some of its covariates. This paper shows that this phenomenon is far from exceptional and can even be a generic problem when the covariates contain the prior score attainments, a typical situation in value added modeling. Starting from a general, model-free definition of value added, the paper derives an explicit expression of the value added in an endogeneous hierarchical linear Gaussian model. Inference on value added is proposed using an instrumental variable approach. The impact of endogeneity on the value added and the estimated value added is calculated accurately. This is also illustrated on a large data set of individual scores of about 200,000 students in Chile.

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…

Prediction of postoperative pulmonary function using 99mTc-MAA perfusion lung SPECT

International Nuclear Information System (INIS)

Hosokawa, Nobuyuki; Tanabe, Masatada; Satoh, Katashi; Takashima, Hitoshi; Ohkawa, Motoomi; Maeda, Masazumi; Tamai, Toyosato; Kojima, Kanji.

1995-01-01

In order to predict postoperative pulmonary function, 99m Tc-MAA perfusion lung SPECT and spirometry were performed preoperatively in 52 patients with resectable primary lung cancer; 44 underwent lobectomy, eight pneumonectomy. Local pulmonary function (called local effective volume) was evaluated according to the degree of radionuclide distribution of each voxel in the SPECT images. The total effective volume was defined as the sum of the local effective volume, and the residual effective volume was the total effective volume excluding loss after operation. Predicted pulmonary function (VC and FEV 1.0) was calculated by the following formula: Predicted value=preoperative value x percent of the residual effective volume. Postoperative pulmonary function was predicted in the same patients by means of 99m Tc-MAA perfusion lung planar scintigraphy and X-ray CT. The patients were reinvestigated with spirometry at one and four months after surgery, and the values were compared with the predicted values. The correlations between the predicted values using SPECT and measured postoperative pulmonary function were highly significant (VC: r=0.867, FEV1.0: r=0.864 one month after operation; VC: r=0.860, FEV1.0: r=0.907 4 months after operation). The predicted values calculated using SPECT were accurate compared with the predicted values calculated using planar scintigraphy or X-ray CT. The patients with predicted FEV1.0 of less than 0.8 liter required home oxygen therapy. This method is valuable for the prediction of postoperative pulmonary function before the surgical procedure. (author)

Accurate density functional prediction of molecular electron affinity with the scaling corrected Kohn–Sham frontier orbital energies

Science.gov (United States)

Zhang, DaDi; Yang, Xiaolong; Zheng, Xiao; Yang, Weitao

2018-04-01

Electron affinity (EA) is the energy released when an additional electron is attached to an atom or a molecule. EA is a fundamental thermochemical property, and it is closely pertinent to other important properties such as electronegativity and hardness. However, accurate prediction of EA is difficult with density functional theory methods. The somewhat large error of the calculated EAs originates mainly from the intrinsic delocalisation error associated with the approximate exchange-correlation functional. In this work, we employ a previously developed non-empirical global scaling correction approach, which explicitly imposes the Perdew-Parr-Levy-Balduz condition to the approximate functional, and achieve a substantially improved accuracy for the calculated EAs. In our approach, the EA is given by the scaling corrected Kohn-Sham lowest unoccupied molecular orbital energy of the neutral molecule, without the need to carry out the self-consistent-field calculation for the anion.

Effect of computational grid on accurate prediction of a wind turbine rotor using delayed detached-eddy simulations

Energy Technology Data Exchange (ETDEWEB)

Bangga, Galih; Weihing, Pascal; Lutz, Thorsten; Krämer, Ewald [University of Stuttgart, Stuttgart (Germany)

2017-05-15

The present study focuses on the impact of grid for accurate prediction of the MEXICO rotor under stalled conditions. Two different blade mesh topologies, O and C-H meshes, and two different grid resolutions are tested for several time step sizes. The simulations are carried out using Delayed detached-eddy simulation (DDES) with two eddy viscosity RANS turbulence models, namely Spalart- Allmaras (SA) and Menter Shear stress transport (SST) k-ω. A high order spatial discretization, WENO (Weighted essentially non- oscillatory) scheme, is used in these computations. The results are validated against measurement data with regards to the sectional loads and the chordwise pressure distributions. The C-H mesh topology is observed to give the best results employing the SST k-ω turbulence model, but the computational cost is more expensive as the grid contains a wake block that increases the number of cells.

Predictive values of Bi-Rads categories 3, 4 and 5 in non-palpable breast masses evaluated by mammography, ultrasound and magnetic resonance imaging

International Nuclear Information System (INIS)

Roveda Junior, Decio; Fleury, Eduardo de Castro Faria; Piato, Sebastiao; Oliveira, Vilmar Marques de; Rinaldi, Jose Francisco; Ferreira, Carlos Alberto Pecci

2007-01-01

Objective: To evaluate the predictive value of BI-RADS TM categories 3, 4 and 5 in non-palpable breast masses assessed by mammography, ultrasound and magnetic resonance imaging. Materials And Methods: Twenty-nine patients with BI-RADS categories 3, 4 and 5 non-palpable breast masses identified by mammograms were submitted to complementary ultrasound and magnetic resonance imaging studies, besides excisional biopsy. In total, 30 biopsies were performed. The lesions as well as their respective BI-RADS classification into 3, 4 and 5 were correlated with the histopathological results. The predictive values calculation was made by means of specific mathematical equations. Results: Negative predictive values for category 3 were: mammography, 69.23%; ultrasound, 70.58%; and magnetic resonance imaging, 100%. Positive predictive values for category 4 were: mammography, 63.63%; ultrasound, 50%; and magnetic resonance imaging, 30.76%. For category 5, positive predictive values were: mammography and ultrasound, 100%; and magnetic resonance imaging, 92.85%. Conclusion: For category 3, the negative predictive value of magnetic resonance imaging was high, and for categories 4 and 5, the positive predictive values of the three modalities were moderate. (author)

Predictive value of the transtheoretical model to smoking cessation in hospitalized patients with cardiovascular disease.

Science.gov (United States)

Chouinard, Maud-Christine; Robichaud-Ekstrand, Sylvie

2007-02-01

Several authors have questioned the transtheoretical model. Determining the predictive value of each cognitive-behavioural element within this model could explain the multiple successes reported in smoking cessation programmes. The purpose of this study was to predict point-prevalent smoking abstinence at 2 and 6 months, using the constructs of the transtheoretical model, when applied to a pooled sample of individuals who were hospitalized for a cardiovascular event. The study follows a predictive correlation design. Recently hospitalized patients (n=168) with cardiovascular disease were pooled from a randomized, controlled trial. Independent variables of the predictive transtheoretical model comprise stages and processes of change, pros and cons to quit smoking (decisional balance), self-efficacy, and social support. These were evaluated at baseline, 2 and 6 months. Compared to smokers, individuals who abstained from smoking at 2 and 6 months were more confident at baseline to remain non-smokers, perceived less pros and cons to continue smoking, utilized less consciousness raising and self-re-evaluation experiential processes of change, and received more positive reinforcement from their social network with regard to their smoke-free behaviour. Self-efficacy and stages of change at baseline were predictive of smoking abstinence after 6 months. Other variables found to be predictive of smoking abstinence at 6 months were an increase in self-efficacy; an increase in positive social support behaviour and a decrease of the pros within the decisional balance. The results partially support the predictive value of the transtheoretical model constructs in smoking cessation for cardiovascular disease patients.

Prognostic and predictive value of cathepsin X in serum from colorectal cancer patients

DEFF Research Database (Denmark)

Vižin, Tjaša; Christensen, Ib Jarle; Wilhelmsen, Michael

2014-01-01

, but for patients in stages I-III with local resectable disease. The significant association of cathepsin X with survival in a group of patients who received no chemotherapy and the absence of this association in the group who received chemotherapy, suggest the possible predictive value for response to chemotherapy...

Clinical value of preoperative serum CA 19-9 and CA 125 levels in predicting the resectability of hilar cholangiocarcinoma.

Science.gov (United States)

Hu, Hai-Jie; Mao, Hui; Tan, Yong-Qiong; Shrestha, Anuj; Ma, Wen-Jie; Yang, Qin; Wang, Jun-Ke; Cheng, Nan-Sheng; Li, Fu-Yu

2016-01-01

To examine the predictive value of tumor markers for evaluating tumor resectability in patients with hilar cholangiocarcinoma and to explore the prognostic effect of various preoperative factors on resectability in patients with potentially resectable tumors. Patients with potentially resectable tumors judged by radiologic examination were included. The receiver operating characteristic (ROC) analysis was conducted to evaluate serum carbohydrate antigenic determinant 19-9 (CA 19-9), carbohydrate antigen 125 (CA 125) and carcino embryonie antigen levels on tumor resectability. Univariate and multivariate logistic regression models were also conducted to analysis the correlation of preoperative factors with resectability. In patients with normal bilirubin levels, ROC curve analysis calculated the ideal CA 19-9 cut-off value of 203.96 U/ml in prediction of resectability, with a sensitivity of 83.7 %, specificity of 80 %, positive predictive value of 91.1 % and negative predictive value of 66.7 %. Meanwhile, the optimal cut-off value for CA 125 to predict resectability was 25.905 U/ml (sensitivity, 78.6 %; specificity, 67.5 %). In a multivariate logistic regression model, tumor size ≤3 cm (OR 4.149, 95 % CI 1.326-12.981, P = 0.015), preoperative CA 19-9 level ≤200 U/ml (OR 20.324, 95 % CI 6.509-63.467, P hilar cholangiocarcinoma. Preoperative CA 19-9 and CA 125 levels predict resectability in patients with radiological resectable hilar cholangiocarcinoma. Increased preoperative CA 19-9 levels and CA 125 levels are associated with poor resectability rate.

Positive predictive value of albumin: globulin ratio for feline infectious peritonitis in a mid-western referral hospital population.

Science.gov (United States)

Jeffery, Unity; Deitz, Krysta; Hostetter, Shannon

2012-12-01

Low albumin to globulin ratio has been found previously to have a high positive predictive value for feline infectious peritonitis (FIP) in cats with clinical signs highly suggestive of the disease. However, FIP can have a more vague clinical presentation. This retrospective study found that the positive predictive value of an albumin:globulin (A:G) ratio of <0.8 and <0.6 was only 12.5% and 25%, respectively, in a group of 100 cats with one or more clinical signs consistent with FIP. The negative predictive value was 100% and 99% for an A:G ratio of <0.8 and A:G<0.6%, respectively. Therefore, when the prevalence of FIP is low, the A:G ratio is useful to rule out FIP but is not helpful in making a positive diagnosis of FIP.

Sensation and perception of sucrose and fat stimuli predict the reinforcing value of food.

Science.gov (United States)

Panek-Scarborough, Leah M; Dewey, Amber M; Temple, Jennifer L

2012-03-20

Chronic overeating can lead to weight gain and obesity. Sensory system function may play a role in the types of foods people select and the amount of food people eat. Several studies have shown that the orosensory components of eating play a strong role in driving food intake and food selection. In addition, previous work has shown that motivation to get food, or the reinforcing value of food, is a predictor of energy intake. The purpose of this study was to test the hypothesis that higher detection thresholds and lower suprathreshold intensity ratings of sweet and fat stimuli are associated with greater reinforcing value of food. In addition, we sought to determine if the sensory ratings of the stimuli would differ depending on whether they were expectorated or swallowed. The reinforcing value of food was measured by having participants perform operant responses for food on progressive ratio schedules of reinforcement. Taste detection thresholds and suprathresholds for solutions containing varied concentrations of sucrose and fat were also measured in two different Experiments. In Experiment 1, we found that sucrose, but not fat, detection predicted the reinforcing value of food with the reinforcing value of food increasing as sucrose detection threshold increased (indicating poorer detection). In Experiment 2, we found that lower suprathreshold ratings of expectorated fat and sucrose predicted greater reinforcing value of food. In addition, higher detection thresholds for fat stimuli (indicating poorer detection) were associated with greater reinforcing value of food. When taken together, these studies suggest that there is a relationship between taste detection and perception and reinforcing value of food and that these relationships vary based on whether the stimulus is swallowed or expectorated. Copyright © 2012 Elsevier Inc. All rights reserved.

Prediction of packaging seal life using thermoanalytical techniques

International Nuclear Information System (INIS)

Nigrey, P.J.

1997-11-01

In this study, Thermogravimetric Analysis (TGA) has been used to study silicone, Viton and Ethylene Propylene (EPDM) rubber. The studies have shown that TGA accurately predicts the relative order of thermo-oxidative stability of these three materials from the calculated activation energies. As expected, the greatest thermal stability was found in silicone rubber followed by Viton and EPDM rubber. The calculated lifetimes for these materials were in relatively close agreement with published values. The preliminary results also accurately reflect decreased thermal stability and lifetime for EPDM rubber exposed to radiation and chemicals. These results suggest TGA provides a rapid method to evaluate material stability

Normal Tissue Complication Probability Estimation by the Lyman-Kutcher-Burman Method Does Not Accurately Predict Spinal Cord Tolerance to Stereotactic Radiosurgery

International Nuclear Information System (INIS)

Daly, Megan E.; Luxton, Gary; Choi, Clara Y.H.; Gibbs, Iris C.; Chang, Steven D.; Adler, John R.; Soltys, Scott G.

2012-01-01

Purpose: To determine whether normal tissue complication probability (NTCP) analyses of the human spinal cord by use of the Lyman-Kutcher-Burman (LKB) model, supplemented by linear–quadratic modeling to account for the effect of fractionation, predict the risk of myelopathy from stereotactic radiosurgery (SRS). Methods and Materials: From November 2001 to July 2008, 24 spinal hemangioblastomas in 17 patients were treated with SRS. Of the tumors, 17 received 1 fraction with a median dose of 20 Gy (range, 18–30 Gy) and 7 received 20 to 25 Gy in 2 or 3 sessions, with cord maximum doses of 22.7 Gy (range, 17.8–30.9 Gy) and 22.0 Gy (range, 20.2–26.6 Gy), respectively. By use of conventional values for α/β, volume parameter n, 50% complication probability dose TD 50 , and inverse slope parameter m, a computationally simplified implementation of the LKB model was used to calculate the biologically equivalent uniform dose and NTCP for each treatment. Exploratory calculations were performed with alternate values of α/β and n. Results: In this study 1 case (4%) of myelopathy occurred. The LKB model using radiobiological parameters from Emami and the logistic model with parameters from Schultheiss overestimated complication rates, predicting 13 complications (54%) and 18 complications (75%), respectively. An increase in the volume parameter (n), to assume greater parallel organization, improved the predictive value of the models. Maximum-likelihood LKB fitting of α/β and n yielded better predictions (0.7 complications), with n = 0.023 and α/β = 17.8 Gy. Conclusions: The spinal cord tolerance to the dosimetry of SRS is higher than predicted by the LKB model using any set of accepted parameters. Only a high α/β value in the LKB model and only a large volume effect in the logistic model with Schultheiss data could explain the low number of complications observed. This finding emphasizes that radiobiological models traditionally used to estimate spinal cord NTCP

Total motile sperm count has a superior predictive value over the WHO 2010 cut-off values for the outcomes of intracytoplasmic sperm injection cycles.

Science.gov (United States)

Borges, E; Setti, A S; Braga, D P A F; Figueira, R C S; Iaconelli, A

2016-09-01

The objective of this study was to compare (i) the intracytoplasmic sperm injection outcomes among groups with different total motile sperm count ranges, (ii) the intracytoplasmic sperm injection outcomes between groups with normal and abnormal total motile sperm count, and (iii) the predictive values of WHO 2010 cut-off values and pre-wash total motile sperm count for the intracytoplasmic sperm injection outcomes, in couples with male infertility. This study included data from 518 patients undergoing their first intracytoplasmic sperm injection cycle as a result of male infertility. Couples were divided into five groups according to their total motile sperm count: Group I, total motile sperm count sperm count 1-5 × 10(6) ; group III, total motile sperm count 5-10 × 10(6) ; group IV, total motile sperm count 10-20 × 10(6) ; and group V, total motile sperm count >20 × 10(6) (which was considered a normal total motile sperm count value). Then, couples were grouped into an abnormal and normal total motile sperm count group. The groups were compared regarding intracytoplasmic sperm injection outcomes. The predictive values of WHO 2010 cut-off values and total motile sperm count for the intracytoplasmic sperm injection outcomes were also investigated. The fertilization rate was lower in total motile sperm count group I compared to total motile sperm count group V (72.5 ± 17.6 vs. 84.9 ± 14.4, p = 0.011). The normal total motile sperm count group had a higher fertilization rate (84.9 ± 14.4 vs. 81.1 ± 15.8, p = 0.016) and lower miscarriage rate (17.9% vs. 29.5%, p = 0.041) compared to the abnormal total motile sperm count group. The total motile sperm count was the only parameter that demonstrated a predictive value for the formation of high-quality embryos on D2 (OR: 1.18, p = 0.013), formation of high-quality embryos on D3 (OR: 1.12, p = 0.037), formation of blastocysts on D5 (OR: 1.16, p = 0.011), blastocyst expansion grade on D5

Prevalence and predictive value of islet cell antibodies and insulin autoantibodies in women with gestational diabetes

DEFF Research Database (Denmark)

Damm, P; Kühl, C; Buschard, K

1994-01-01

The objective of the present study was to investigate the predictive value of islet cell antibodies (ICA) and insulin autoantibodies (IAA) for development of diabetes in women with previous gestational diabetes (GDM). Two hundred and forty-one previous diet-treated GDM patients and 57 women without...... for ICA were ICA-positive and three of these had Type 1 diabetes at follow-up, as well as three ICA-negative patients. The sensitivity, specificity, and predictive value of ICA-positivity for later development of diabetes were 50%, 99%, and 75%, respectively. None of the women was IAA-positive during...

Predicting accurate absolute binding energies in aqueous solution

DEFF Research Database (Denmark)

Jensen, Jan Halborg

2015-01-01

Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal......-represented by continuum models. While I focus on binding free energies in aqueous solution the approach also applies (with minor adjustments) to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent....

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

Science.gov (United States)

Lee, Hanbong

2017-01-01

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

Using fire-weather forecasts and local weather observations in predicting burning index for individual fire-danger stations.

Science.gov (United States)

Owen P. Cramer

1958-01-01

Any agency engaged in forest-fire control needs accurate weather forecasts and systematic procedures for making the best use of predicted and reported weather information. This study explores the practicability of using several tabular and graphical aids for converting area forecasts and local observations of relative humidity and wind speed into predicted values for...

Can pretreatment ADC values predict recurrence of bladder cancer after transurethral resection?

Energy Technology Data Exchange (ETDEWEB)

Funatsu, Hiroyuki, E-mail: hirofunatsu999@hotmail.com [Division of Diagnostic Imaging, Chiba Cancer Center, 666-2 Nitona-cho, Chuo-ku, Chiba 260-8717 (Japan); Imamura, Akihiro; Takano, Hideyuki [Division of Diagnostic Imaging, Chiba Cancer Center, 666-2 Nitona-cho, Chuo-ku, Chiba 260-8717 (Japan); Ueda, Takeshi [Division of Urology, Chiba Cancer Center, 666-2 Nitona-cho, Chuo-ku, Chiba 260-8717 (Japan); Uno, Takashi [Department of Radiology, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuou-ku, Chiba 260-8670 (Japan)

2012-11-15

Objective: The aim of this retrospective study was to investigate the association between the pretreatment apparent diffusion coefficient (ADC) value and recurrence of bladder cancer after transurethral resection. Methods: Patients with superficial bladder cancer were identified. Mean ADC values of the tumors were compared between patients with and without recurrence following trans-urethral resection. A receiver-operator characteristic curve was used for determining the optimal cutoff ADC value. Univariate and multivariate analyses were used to determine the effect of ADC values and other factors. Results: With a mean follow-up period of 25 months, bladder cancer recurred in 14 of 44 patients (32%). The mean ADC value of tumors in patients with recurrence was lower than in those without recurrence (1.08 mm{sup 2}/s vs. 1.28 Multiplication-Sign 10{sup -3} mm{sup 2}/s; p = 0.003). The optimal cutoff ADC value for predicting recurrence was determined to be 1.12 Multiplication-Sign 10{sup -3} mm{sup 2}/s. A modest and significant negative correlation was observed between the ADC values and tumor size (r = -0.436, p = 0.008). After adjustment for size and risk groups, an ADC value equal to or less than the optimal cutoff remained a significant predictor of recurrence (odds ratio 6.3, 95% CI 1.23-32.2, p = 0.027). Conclusion: Pretreatment ADC values may be an independent predictor of bladder cancer recurrence.

Can pretreatment ADC values predict recurrence of bladder cancer after transurethral resection?

International Nuclear Information System (INIS)

Funatsu, Hiroyuki; Imamura, Akihiro; Takano, Hideyuki; Ueda, Takeshi; Uno, Takashi

2012-01-01

Objective: The aim of this retrospective study was to investigate the association between the pretreatment apparent diffusion coefficient (ADC) value and recurrence of bladder cancer after transurethral resection. Methods: Patients with superficial bladder cancer were identified. Mean ADC values of the tumors were compared between patients with and without recurrence following trans-urethral resection. A receiver–operator characteristic curve was used for determining the optimal cutoff ADC value. Univariate and multivariate analyses were used to determine the effect of ADC values and other factors. Results: With a mean follow-up period of 25 months, bladder cancer recurred in 14 of 44 patients (32%). The mean ADC value of tumors in patients with recurrence was lower than in those without recurrence (1.08 mm 2 /s vs. 1.28 × 10 −3 mm 2 /s; p = 0.003). The optimal cutoff ADC value for predicting recurrence was determined to be 1.12 × 10 −3 mm 2 /s. A modest and significant negative correlation was observed between the ADC values and tumor size (r = −0.436, p = 0.008). After adjustment for size and risk groups, an ADC value equal to or less than the optimal cutoff remained a significant predictor of recurrence (odds ratio 6.3, 95% CI 1.23–32.2, p = 0.027). Conclusion: Pretreatment ADC values may be an independent predictor of bladder cancer recurrence.

Exploring the relationship between sequence similarity and accurate phylogenetic trees.

Science.gov (United States)

Cantarel, Brandi L; Morrison, Hilary G; Pearson, William

2006-11-01

We have characterized the relationship between accurate phylogenetic reconstruction and sequence similarity, testing whether high levels of sequence similarity can consistently produce accurate evolutionary trees. We generated protein families with known phylogenies using a modified version of the PAML/EVOLVER program that produces insertions and deletions as well as substitutions. Protein families were evolved over a range of 100-400 point accepted mutations; at these distances 63% of the families shared significant sequence similarity. Protein families were evolved using balanced and unbalanced trees, with ancient or recent radiations. In families sharing statistically significant similarity, about 60% of multiple sequence alignments were 95% identical to true alignments. To compare recovered topologies with true topologies, we used a score that reflects the fraction of clades that were correctly clustered. As expected, the accuracy of the phylogenies was greatest in the least divergent families. About 88% of phylogenies clustered over 80% of clades in families that shared significant sequence similarity, using Bayesian, parsimony, distance, and maximum likelihood methods. However, for protein families with short ancient branches (ancient radiation), only 30% of the most divergent (but statistically significant) families produced accurate phylogenies, and only about 70% of the second most highly conserved families, with median expectation values better than 10(-60), produced accurate trees. These values represent upper bounds on expected tree accuracy for sequences with a simple divergence history; proteins from 700 Giardia families, with a similar range of sequence similarities but considerably more gaps, produced much less accurate trees. For our simulated insertions and deletions, correct multiple sequence alignments did not perform much better than those produced by T-COFFEE, and including sequences with expressed sequence tag-like sequencing errors did not

Breast calcifications. A standardized mammographic reporting and data system to improve positive predictive value

International Nuclear Information System (INIS)

Perugini, G.; Bonzanini, B.; Valentino, C.

1999-01-01

The purpose of this work is to investigate the usefulness of a standardized reporting and data system in improving the positive predictive value of mammography in breast calcifications. Using the Breast Imaging Reporting and Data System lexicon developed by the American College of Radiology, it is defined 5 descriptive categories of breast calcifications and classified diagnostic suspicion of malignancy on a 3-grade scale (low, intermediate and high). Two radiologists reviewed 117 mammographic studies selected from those of the patients submitted to surgical biopsy for mammographically detected calcifications from January 1993 to December 1997, and classified them according to the above criteria. The positive predictive value was calculated for all examinations and for the stratified groups. Defining a standardized system for assessing and describing breast calcifications helps improve the diagnostic accuracy of mammography in clinical practice [it

Differential private collaborative Web services QoS prediction

KAUST Repository

Liu, An

2018-04-04

Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user’s observed QoS values by applying differential privacy to the user’s QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user’s QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice.

Differential private collaborative Web services QoS prediction

KAUST Repository

Liu, An; Shen, Xindi; Li, Zhixu; Liu, Guanfeng; Xu, Jiajie; Zhao, Lei; Zheng, Kai; Shang, Shuo

2018-01-01

Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user’s observed QoS values by applying differential privacy to the user’s QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user’s QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice.

Predictive value of general movements' quality in low-risk infants for minor neurological dysfunction and behavioural problems at preschool age.

Science.gov (United States)

Bennema, Anne N; Schendelaar, Pamela; Seggers, Jorien; Haadsma, Maaike L; Heineman, Maas Jan; Hadders-Algra, Mijna

2016-03-01

General movement (GM) assessment is a well-established tool to predict cerebral palsy in high-risk infants. Little is known on the predictive value of GM assessment in low-risk populations. To assess the predictive value of GM quality in early infancy for the development of the clinically relevant form of minor neurological dysfunction (complex MND) and behavioral problems at preschool age. Prospective cohort study. A total of 216 members of the prospective Groningen Assisted Reproductive Techniques (ART) cohort study were included in this study. ART did not affect neurodevelopmental outcome of these relatively low-risk infants born to subfertile parents. GM quality was determined at 2 weeks and 3 months. At 18 months and 4 years, the Hempel neurological examination was used to assess MND. At 4 years, parents completed the Child Behavior Checklist; this resulted in the total problem score (TPS), internalizing problem score (IPS), and externalizing problem score (EPS). Predictive values of definitely (DA) and mildly (MA) abnormal GMs were calculated. DA GMs at 2 weeks were associated with complex MND at 18 months and atypical TPS and IPS at 4 years (all ppredictive value of DA GMs at 2 weeks were rather low (13%-60%); specificity and negative predictive value were excellent (92%-99%). DA GMs at 3 months occurred too infrequently to calculate prediction. MA GMs were not associated with outcome. GM quality as a single predictor for complex MND and behavioral problems at preschool age has limited clinical value in children at low risk for developmental disorders. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

How long the singular value decomposed entropy predicts the stock market? - Evidence from the Dow Jones Industrial Average Index

Science.gov (United States)

Gu, Rongbao; Shao, Yanmin

2016-07-01

In this paper, a new concept of multi-scales singular value decomposition entropy based on DCCA cross correlation analysis is proposed and its predictive power for the Dow Jones Industrial Average Index is studied. Using Granger causality analysis with different time scales, it is found that, the singular value decomposition entropy has predictive power for the Dow Jones Industrial Average Index for period less than one month, but not for more than one month. This shows how long the singular value decomposition entropy predicts the stock market that extends Caraiani's result obtained in Caraiani (2014). On the other hand, the result also shows an essential characteristic of stock market as a chaotic dynamic system.

Vorticity confinement technique for drag prediction

Science.gov (United States)

Povitsky, Alex; Snyder, Troy

2011-11-01

This work couples wake-integral drag prediction and vorticity confinement technique (VC) for the improved prediction of drag from CFD simulations. Induced drag computations of a thin wing are shown to be more accurate than the more widespread method of surface pressure integration when compared to theoretical lifting-line value. Furthermore, the VC method improves trailing vortex preservation and counteracts the shift from induced drag to numerical entropy drag with increasing distance of Trefftz plane downstream of the wing. Accurate induced drag prediction via the surface integration of pressure barring a sufficiently refined surface grid and increased computation time. Furthermore, the alternative wake-integral technique for drag prediction suffers from numerical dissipation. VC is shown to control the numerical dissipation with very modest computational overhead. The 2-D research code is used to test specific formulations of the VC body force terms and illustrate the computational efficiency of the method compared to a ``brute force'' reduction in spatial step size. For the 3-D wing simulation, ANSYS FLUENT is employed with the VC body force terms added to the solver with user-defined functions (UDFs). VC is successfully implemented to highly unsteady flows typical for Micro Air Vehicles (MAV) producing oscillative drag force either by natural vortex shedding at high angles of attack or by flapping wing motion.