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  1. 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

  2. Does preoperative cross-sectional imaging accurately predict main duct involvement in intraductal papillary mucinous neoplasm?

    Science.gov (United States)

    Barron, M R; Roch, A M; Waters, J A; Parikh, J A; DeWitt, J M; Al-Haddad, M A; Ceppa, E P; House, M G; Zyromski, N J; Nakeeb, A; Pitt, H A; Schmidt, C Max

    2014-03-01

    Main pancreatic duct (MPD) involvement is a well-demonstrated risk factor for malignancy in intraductal papillary mucinous neoplasm (IPMN). Preoperative radiographic determination of IPMN type is heavily relied upon in oncologic risk stratification. We hypothesized that radiographic assessment of MPD involvement in IPMN is an accurate predictor of pathological MPD involvement. Data regarding all patients undergoing resection for IPMN at a single academic institution between 1992 and 2012 were gathered prospectively. Retrospective analysis of imaging and pathologic data was undertaken. Preoperative classification of IPMN type was based on cross-sectional imaging (MRI/magnetic resonance cholangiopancreatography (MRCP) and/or CT). Three hundred sixty-two patients underwent resection for IPMN. Of these, 334 had complete data for analysis. Of 164 suspected branch duct (BD) IPMN, 34 (20.7%) demonstrated MPD involvement on final pathology. Of 170 patients with suspicion of MPD involvement, 50 (29.4%) demonstrated no MPD involvement. Of 34 patients with suspected BD-IPMN who were found to have MPD involvement on pathology, 10 (29.4%) had invasive carcinoma. Alternatively, 2/50 (4%) of the patients with suspected MPD involvement who ultimately had isolated BD-IPMN demonstrated invasive carcinoma. Preoperative radiographic IPMN type did not correlate with final pathology in 25% of the patients. In addition, risk of invasive carcinoma correlates with pathologic presence of MPD involvement.

  3. How accurate can genetic predictions be?

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    Dreyfuss Jonathan M

    2012-07-01

    Full Text Available Abstract Background Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution. Results Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features. Conclusion Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.

  4. Customised birthweight standards accurately predict perinatal morbidity

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    Figueras, Francesc; Figueras, Josep; Meler, Eva; Eixarch, Elisenda; Coll, Oriol; Gratacos, Eduard; Gardosi, Jason; Carbonell, Xavier

    2007-01-01

    Objective Fetal growth restriction is associated with adverse perinatal outcome but is often not recognised antenatally, and low birthweight centiles based on population norms are used as a proxy instead. This study compared the association between neonatal morbidity and fetal growth status at birth as determined by customised birthweight centiles and currently used centiles based on population standards. Design Retrospective cohort study. Setting Referral hospital, Barcelona, Spain. Patients A cohort of 13 661 non‐malformed singleton deliveries. Interventions Both population‐based and customised standards for birth weight were applied to the study cohort. Customised weight centiles were calculated by adjusting for maternal height, booking weight, parity, ethnic origin, gestational age at delivery and fetal sex. Main outcome measures Newborn morbidity and perinatal death. Results The association between smallness for gestational age (SGA) and perinatal morbidity was stronger when birthweight limits were customised, and resulted in an additional 4.1% (n = 565) neonates being classified as SGA. Compared with non‐SGA neonates, this newly identified group had an increased risk of perinatal mortality (OR 3.2; 95% CI 1.6 to 6.2), neurological morbidity (OR 3.2; 95% CI 1.7 to 6.1) and non‐neurological morbidity (OR 8; 95% CI 4.8 to 13.6). Conclusion Customised standards improve the prediction of adverse neonatal outcome. The association between SGA and adverse outcome is independent of the gestational age at delivery. PMID:17251224

  5. Can ultrasound biomicroscopy be used to predict accommodation accurately?

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    Ramasubramanian, Viswanathan; Glasser, Adrian

    2015-04-01

    Clinical accommodation testing involves measuring either accommodative optical changes or accommodative biometric changes. Quantifying both optical and biometric changes during accommodation might be helpful in the design and evaluation of accommodation restoration concepts. This study aims to establish the accuracy of ultrasound biomicroscopy (UBM) in predicting the accommodative optical response (AOR) from biometric changes. Static AOR from 0 to 6 diopters (D) stimuli in 1-D steps were measured with infrared photorefraction and a Grand Seiko autorefractor (WR-5100 K; Shigiya Machinery Works Ltd., Hiroshima, Japan) in 26 human subjects aged 21 to 36 years. Objective measurements of accommodative biometric changes to the same stimulus demands were measured from UBM (Vu-MAX; Sonomed Escalon, Lake Success, NY) images in the same group of subjects. AOR was predicted from biometry using linear regressions, 95% confidence intervals, and 95% prediction intervals. Bland-Altman analysis showed 0.52 D greater AOR with photorefraction than with the Grand Seiko autorefractor. Per-diopter changes in accommodative biometry were: anterior chamber depth (ACD): -0.055 mm/D, lens thickness (LT): +0.076 mm/D, anterior lens radii of curvature (ALRC): -0.854 mm/D, posterior lens radii of curvature (PLRC): -0.222 mm/D, and anterior segment length (ASL): +0.030 mm/D. The standard deviation of AOR predicted from linear regressions for various biometry parameters were: ACD: 0.24 D, LT: 0.30 D, ALRC: 0.24 D, PLRC: 0.43 D, ASL: 0.50 D. UBM measured parameters can, on average, predict AOR with a standard deviation of 0.50 D or less using linear regression. UBM is a useful and accurate objective technique for measuring accommodation in young phakic eyes. Copyright 2015, SLACK Incorporated.

  6. Accurate torque-speed performance prediction for brushless dc motors

    Science.gov (United States)

    Gipper, Patrick D.

    Desirable characteristics of the brushless dc motor (BLDCM) have resulted in their application for electrohydrostatic (EH) and electromechanical (EM) actuation systems. But to effectively apply the BLDCM requires accurate prediction of performance. The minimum necessary performance characteristics are motor torque versus speed, peak and average supply current and efficiency. BLDCM nonlinear simulation software specifically adapted for torque-speed prediction is presented. The capability of the software to quickly and accurately predict performance has been verified on fractional to integral HP motor sizes, and is presented. Additionally, the capability of torque-speed prediction with commutation angle advance is demonstrated.

  7. Accurate particle position measurement from images

    CERN Document Server

    Feng, Yan; Liu, Bin; 10.1063/1.2735920

    2011-01-01

    The moment method is an image analysis technique for sub-pixel estimation of particle positions. The total error in the calculated particle position includes effects of pixel locking and random noise in each pixel. Pixel locking, also known as peak locking, is an artifact where calculated particle positions are concentrated at certain locations relative to pixel edges. We report simulations to gain an understanding of the sources of error and their dependence on parameters the experimenter can control. We suggest an algorithm, and we find optimal parameters an experimenter can use to minimize total error and pixel locking. Simulating a dusty plasma experiment, we find that a sub-pixel accuracy of 0.017 pixel or better can be attained. These results are also useful for improving particle position measurement and particle tracking velocimetry (PTV) using video microscopy, in fields including colloids, biology, and fluid mechanics.

  8. Accurate Multisteps Traffic Flow Prediction Based on SVM

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    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  9. Speed-of-sound compensated photoacoustic tomography for accurate imaging

    CERN Document Server

    Jose, Jithin; Steenbergen, Wiendelt; Slump, Cornelis H; van Leeuwen, Ton G; Manohar, Srirang

    2012-01-01

    In most photoacoustic (PA) measurements, variations in speed-of-sound (SOS) of the subject are neglected under the assumption of acoustic homogeneity. Biological tissue with spatially heterogeneous SOS cannot be accurately reconstructed under this assumption. We present experimental and image reconstruction methods with which 2-D SOS distributions can be accurately acquired and reconstructed, and with which the SOS map can be used subsequently to reconstruct highly accurate PA tomograms. We begin with a 2-D iterative reconstruction approach in an ultrasound transmission tomography (UTT) setting, which uses ray refracted paths instead of straight ray paths to recover accurate SOS images of the subject. Subsequently, we use the SOS distribution in a new 2-D iterative approach, where refraction of rays originating from PA sources are accounted for in accurately retrieving the distribution of these sources. Both the SOS reconstruction and SOS-compensated PA reconstruction methods utilize the Eikonal equation to m...

  10. Is Three-Dimensional Soft Tissue Prediction by Software Accurate?

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    Nam, Ki-Uk; Hong, Jongrak

    2015-11-01

    The authors assessed whether virtual surgery, performed with a soft tissue prediction program, could correctly simulate the actual surgical outcome, focusing on soft tissue movement. Preoperative and postoperative computed tomography (CT) data for 29 patients, who had undergone orthognathic surgery, were obtained and analyzed using the Simplant Pro software. The program made a predicted soft tissue image (A) based on presurgical CT data. After the operation, we obtained actual postoperative CT data and an actual soft tissue image (B) was generated. Finally, the 2 images (A and B) were superimposed and analyzed differences between the A and B. Results were grouped in 2 classes: absolute values and vector values. In the absolute values, the left mouth corner was the most significant error point (2.36 mm). The right mouth corner (2.28 mm), labrale inferius (2.08 mm), and the pogonion (2.03 mm) also had significant errors. In vector values, prediction of the right-left side had a left-sided tendency, the superior-inferior had a superior tendency, and the anterior-posterior showed an anterior tendency. As a result, with this program, the position of points tended to be located more left, anterior, and superior than the "real" situation. There is a need to improve the prediction accuracy for soft tissue images. Such software is particularly valuable in predicting craniofacial soft tissues landmarks, such as the pronasale. With this software, landmark positions were most inaccurate in terms of anterior-posterior predictions.

  11. Are Predictive Energy Expenditure Equations in Ventilated Surgery Patients Accurate?

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    Tignanelli, Christopher J; Andrews, Allan G; Sieloff, Kurt M; Pleva, Melissa R; Reichert, Heidi A; Wooley, Jennifer A; Napolitano, Lena M; Cherry-Bukowiec, Jill R

    2017-01-01

    While indirect calorimetry (IC) is the gold standard used to calculate specific calorie needs in the critically ill, predictive equations are frequently utilized at many institutions for various reasons. Prior studies suggest these equations frequently misjudge actual resting energy expenditure (REE) in medical and mixed intensive care unit (ICU) patients; however, their utility for surgical ICU (SICU) patients has not been fully evaluated. Therefore, the objective of this study was to compare the REE measured by IC with REE calculated using specific calorie goals or predictive equations for nutritional support in ventilated adult SICU patients. A retrospective review of prospectively collected data was performed on all adults (n = 419, 18-91 years) mechanically ventilated for >24 hours, with an Fio2 ≤ 60%, who met IC screening criteria. Caloric needs were estimated using Harris-Benedict equations (HBEs), and 20, 25, and 30 kcal/kg/d with actual (ABW), adjusted (ADJ), and ideal body (IBW) weights. The REE was measured using IC. The estimated REE was considered accurate when within ±10% of the measured REE by IC. The HBE, 20, 25, and 30 kcal/kg/d estimates of REE were found to be inaccurate regardless of age, gender, or weight. The HBE and 20 kcal/kg/d underestimated REE, while 25 and 30 kcal/kg/d overestimated REE. Of the methods studied, those found to most often accurately estimate REE were the HBE using ABW, which was accurate 35% of the time, and 25 kcal/kg/d ADJ, which was accurate 34% of the time. This difference was not statistically significant. Using HBE, 20, 25, or 30 kcal/kg/d to estimate daily caloric requirements in critically ill surgical patients is inaccurate compared to REE measured by IC. In SICU patients with nutrition requirements essential to recovery, IC measurement should be performed to guide clinicians in determining goal caloric requirements.

  12. Inverter Modeling For Accurate Energy Predictions Of Tracking HCPV Installations

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    Bowman, J.; Jensen, S.; McDonald, Mark

    2010-10-01

    High efficiency high concentration photovoltaic (HCPV) solar plants of megawatt scale are now operational, and opportunities for expanded adoption are plentiful. However, effective bidding for sites requires reliable prediction of energy production. HCPV module nameplate power is rated for specific test conditions; however, instantaneous HCPV power varies due to site specific irradiance and operating temperature, and is degraded by soiling, protective stowing, shading, and electrical connectivity. These factors interact with the selection of equipment typically supplied by third parties, e.g., wire gauge and inverters. We describe a time sequence model accurately accounting for these effects that predicts annual energy production, with specific reference to the impact of the inverter on energy output and interactions between system-level design decisions and the inverter. We will also show two examples, based on an actual field design, of inverter efficiency calculations and the interaction between string arrangements and inverter selection.

  13. Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

    Science.gov (United States)

    Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

    2013-01-02

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

  14. A fast and accurate method for echocardiography strain rate imaging

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    Tavakoli, Vahid; Sahba, Nima; Hajebi, Nima; Nambakhsh, Mohammad Saleh

    2009-02-01

    Recently Strain and strain rate imaging have proved their superiority with respect to classical motion estimation methods in myocardial evaluation as a novel technique for quantitative analysis of myocardial function. Here in this paper, we propose a novel strain rate imaging algorithm using a new optical flow technique which is more rapid and accurate than the previous correlation-based methods. The new method presumes a spatiotemporal constancy of intensity and Magnitude of the image. Moreover the method makes use of the spline moment in a multiresolution approach. Moreover cardiac central point is obtained using a combination of center of mass and endocardial tracking. It is proved that the proposed method helps overcome the intensity variations of ultrasound texture while preserving the ability of motion estimation technique for different motions and orientations. Evaluation is performed on simulated, phantom (a contractile rubber balloon) and real sequences and proves that this technique is more accurate and faster than the previous methods.

  15. 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

    In facilities that process special nuclear material (SNM) it is important to account accurately for the fissile material that enters and leaves the plant. Although there are many stages and processes through which materials must be traced and measured, the focus of this project is material that is “held-up” in equipment, pipes, and ducts during normal operation and that can accumulate over time into significant quantities. Accurately estimating the holdup is essential for proper SNM accounting (vis-à-vis nuclear non-proliferation), criticality and radiation safety, waste management, and efficient plant operation. Usually it is not possible to directly measure the holdup quantity and location, so these must be inferred from measured radiation fields, primarily gamma and less frequently neutrons. Current methods to quantify holdup, i.e. Generalized Geometry Holdup (GGH), primarily rely on simple source configurations and crude radiation transport models aided by ad hoc correction factors. This project seeks an alternate method of performing measurement-based holdup calculations using a predictive model that employs state-of-the-art radiation transport codes capable of accurately simulating such situations. Inverse and data assimilation methods use the forward transport model to search for a source configuration that best matches the measured data and simultaneously provide an estimate of the level of confidence in the correctness of such configuration. In this work the holdup problem is re-interpreted as an inverse problem that is under-determined, hence may permit multiple solutions. A probabilistic approach is applied to solving the resulting inverse problem. This approach rates possible solutions according to their plausibility given the measurements and initial information. This is accomplished through the use of Bayes’ Theorem that resolves the issue of multiple solutions by giving an estimate of the probability of observing each possible solution. To use

  16. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

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    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

  17. Artificial neural network accurately predicts hepatitis B surface antigen seroclearance.

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    Ming-Hua Zheng

    Full Text Available BACKGROUND & AIMS: Hepatitis B surface antigen (HBsAg seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB. This study aimed to develop artificial neural networks (ANNs that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. METHODS: Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion, and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC. RESULTS: Serum quantitative HBsAg (qHBsAg and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001 and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P = 0.197 and LRM-HBsAg seroconversion was solely based on qHBsAg (P = 0.01. For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05. Although the performance of ANN-HBsAg seroconversion (AUROC 0.757 was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. CONCLUSIONS: ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.

  18. Fast and accurate predictions of covalent bonds in chemical space

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    Chang, K. Y. Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole

    2016-05-01

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (˜1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H 2+ . Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  19. Generating highly accurate prediction hypotheses through collaborative ensemble learning

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    Arsov, Nino; Pavlovski, Martin; Basnarkov, Lasko; Kocarev, Ljupco

    2017-03-01

    Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model’s constituent learners at various levels. This novel stability-guided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination.

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

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    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.

  1. 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.

  2. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

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    Rossetti, Andrea O.; van Rootselaar, Anne-Fleur; Wesenberg Kjaer, Troels; Horn, Janneke; Ullén, Susann; Friberg, Hans; Nielsen, Niklas; Rosén, Ingmar; Åneman, Anders; Erlinge, David; Gasche, Yvan; Hassager, Christian; Hovdenes, Jan; Kjaergaard, Jesper; Kuiper, Michael; Pellis, Tommaso; Stammet, Pascal; Wanscher, Michael; Wetterslev, Jørn; Wise, Matt P.; Cronberg, Tobias

    2016-01-01

    Objective: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. Methods: In this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3–5 until 180 days. Results: Eight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p EEG was found in 1% of the patients with a poor outcome. Conclusions: Highly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome. PMID:26865516

  3. A potential smoothing algorithm accurately predicts transmembrane helix packing.

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    Pappu, R V; Marshall, G R; Ponder, J W

    1999-01-01

    Potential smoothing, a deterministic analog of stochastic simulated annealing, is a powerful paradigm for the solution of conformational search problems that require extensive sampling, and should be a useful tool in computational approaches to structure prediction and refinement. A novel potential smoothing and search (PSS) algorithm has been developed and applied to predict the packing of transmembrane helices. The highlight of this method is the efficient manner in which it circumvents the combinatorial explosion associated with the large number of minima on multidimensional potential energy surfaces in order to converge to the global energy minimum. Here we show how our potential smoothing and search method succeeds in finding the global minimum energy structure for the glycophorin A (GpA) transmembrane helix dimer by optimizing interhelical van der Waals interactions over rigid and semi-rigid helices. Structures obtained from our ab initio predictions are in close agreement with recent experimental data.

  4. Fast and accurate automatic structure prediction with HHpred.

    Science.gov (United States)

    Hildebrand, Andrea; Remmert, Michael; Biegert, Andreas; Söding, Johannes

    2009-01-01

    Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8.

  5. Third trimester ultrasound soft-tissue measurements accurately predicts macrosomia.

    Science.gov (United States)

    Maruotti, Giuseppe Maria; Saccone, Gabriele; Martinelli, Pasquale

    2017-04-01

    To evaluate the accuracy of sonographic measurements of fetal soft tissue in the prediction of macrosomia. Electronic databases were searched from their inception until September 2015 with no limit for language. We included only studies assessing the accuracy of sonographic measurements of fetal soft tissue in the abdomen or thigh in the prediction of macrosomia  ≥34 weeks of gestation. The primary outcome was the accuracy of sonographic measurements of fetal soft tissue in the prediction of macrosomia. We generated the forest plot for the pooled sensitivity and specificity with 95% confidence interval (CI). Additionally, summary receiver-operating characteristics (ROC) curves were plotted and the area under the curve (AUC) was also computed to evaluate the overall performance of the diagnostic test accuracy. Three studies, including 287 singleton gestations, were analyzed. The pooled sensitivity of sonographic measurements of abdominal or thigh fetal soft tissue in the prediction of macrosomia was 80% (95% CI: 66-89%) and the pooled specificity was 95% (95% CI: 91-97%). The AUC for diagnostic accuracy of sonographic measurements of fetal soft tissue in the prediction of macrosomia was 0.92 and suggested high diagnostic accuracy. Third-trimester sonographic measurements of fetal soft tissue after 34 weeks may help to detect macrosomia with a high degree of accuracy. The pooled detection rate was 80%. A standardization of measurements criteria, reproducibility, building reference charts of fetal subcutaneous tissue and large studies to assess the optimal cutoff of fetal adipose thickness are necessary before the introduction of fetal soft-tissue markers in the clinical practice.

  6. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    DEFF Research Database (Denmark)

    Westhall, Erik; Rossetti, Andrea O; van Rootselaar, Anne-Fleur

    2016-01-01

    OBJECTIVE: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. METHODS: In this cohort study, 4 EEG specialists...... patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present...

  7. 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....

  8. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    DEFF Research Database (Denmark)

    Westhall, Erik; Rossetti, Andrea O; van Rootselaar, Anne-Fleur;

    2016-01-01

    OBJECTIVE: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. METHODS: In this cohort study, 4 EEG specialists...... patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present...

  9. Imaging tests for accurate diagnosis of acute biliary pancreatitis.

    Science.gov (United States)

    Şurlin, Valeriu; Săftoiu, Adrian; Dumitrescu, Daniela

    2014-11-28

    Gallstones represent the most frequent aetiology of acute pancreatitis in many statistics all over the world, estimated between 40%-60%. Accurate diagnosis of acute biliary pancreatitis (ABP) is of outmost importance because clearance of lithiasis [gallbladder and common bile duct (CBD)] rules out recurrences. Confirmation of biliary lithiasis is done by imaging. The sensitivity of the ultrasonography (US) in the detection of gallstones is over 95% in uncomplicated cases, but in ABP, sensitivity for gallstone detection is lower, being less than 80% due to the ileus and bowel distension. Sensitivity of transabdominal ultrasonography (TUS) for choledocolithiasis varies between 50%-80%, but the specificity is high, reaching 95%. Diameter of the bile duct may be orientative for diagnosis. Endoscopic ultrasonography (EUS) seems to be a more effective tool to diagnose ABP rather than endoscopic retrograde cholangiopancreatography (ERCP), which should be performed only for therapeutic purposes. As the sensitivity and specificity of computerized tomography are lower as compared to state-of-the-art magnetic resonance cholangiopancreatography (MRCP) or EUS, especially for small stones and small diameter of CBD, the later techniques are nowadays preferred for the evaluation of ABP patients. ERCP has the highest accuracy for the diagnosis of choledocholithiasis and is used as a reference standard in many studies, especially after sphincterotomy and balloon extraction of CBD stones. Laparoscopic ultrasonography is a useful tool for the intraoperative diagnosis of choledocholithiasis. Routine exploration of the CBD in cases of patients scheduled for cholecystectomy after an attack of ABP was not proven useful. A significant rate of the so-called idiopathic pancreatitis is actually caused by microlithiasis and/or biliary sludge. In conclusion, the general algorithm for CBD stone detection starts with anamnesis, serum biochemistry and then TUS, followed by EUS or MRCP. In the end

  10. Objective criteria accurately predict amputation following lower extremity trauma.

    Science.gov (United States)

    Johansen, K; Daines, M; Howey, T; Helfet, D; Hansen, S T

    1990-05-01

    MESS (Mangled Extremity Severity Score) is a simple rating scale for lower extremity trauma, based on skeletal/soft-tissue damage, limb ischemia, shock, and age. Retrospective analysis of severe lower extremity injuries in 25 trauma victims demonstrated a significant difference between MESS values for 17 limbs ultimately salvaged (mean, 4.88 +/- 0.27) and nine requiring amputation (mean, 9.11 +/- 0.51) (p less than 0.01). A prospective trial of MESS in lower extremity injuries managed at two trauma centers again demonstrated a significant difference between MESS values of 14 salvaged (mean, 4.00 +/- 0.28) and 12 doomed (mean, 8.83 +/- 0.53) limbs (p less than 0.01). In both the retrospective survey and the prospective trial, a MESS value greater than or equal to 7 predicted amputation with 100% accuracy. MESS may be useful in selecting trauma victims whose irretrievably injured lower extremities warrant primary amputation.

  11. An accurate empirical correlation for predicting natural gas viscosity

    Institute of Scientific and Technical Information of China (English)

    Ehsan Sanjari; Ebrahim Nemati Lay; Mohammad Peymani

    2011-01-01

    Natural gas viscosity is an important parameter in many gas and petroleum engineering calculations.This study presents a new empirical model for quickly calculating the natural gas viscosity.The model was derived from 4089 experimental viscosity data with varieties ranging from 0.01to 21,and 1 to 3 of pseudo reduced pressure and temperature,respectively.The accuracy of this new empirical correlation has been compared with commonly used empirical models,including Lee et al.,Heidaryan et al.,Carr et al.,and Adel Elsharkawy correlations.The comparison indicates that this new empirical model can predict viscosity of natural gas with average absolute relative deviation percentage AARD (%) of 2.173.

  12. Accurate theoretical prediction on positron lifetime of bulk materials

    CERN Document Server

    Zhang, Wenshuai; Liu, Jiandang; Ye, Bangjiao

    2015-01-01

    Based on the first-principles calculations, we perform an initiatory statistical assessment on the reliability level of theoretical positron lifetime of bulk material. We found the original generalized gradient approximation (GGA) form of the enhancement factor and correlation potentials overestimates the effect of the gradient factor. Furthermore, an excellent agreement between model and data with the difference being the noise level of the data is found in this work. In addition, we suggest a new GGA form of the correlation scheme which gives the best performance. This work demonstrates that a brand-new reliability level is achieved for the theoretical prediction on positron lifetime of bulk material and the accuracy of the best theoretical scheme can be independent on the type of materials.

  13. 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...... mol(-1) errors at 298 K: three-body dispersion effects, molecular symmetry, anharmonicity, spurious imaginary frequencies, insufficient conformational sampling, wrong or changing ionization states, errors in the solvation free energy of ions, and explicit solvent (and ion) effects that are not well......-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....

  14. Change in BMI accurately predicted by social exposure to acquaintances.

    Directory of Open Access Journals (Sweden)

    Rahman O Oloritun

    Full Text Available Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC and R(2. This study found a model that explains 68% (p<0.0001 of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as

  15. Change in BMI accurately predicted by social exposure to acquaintances.

    Science.gov (United States)

    Oloritun, Rahman O; Ouarda, Taha B M J; Moturu, Sai; Madan, Anmol; Pentland, Alex Sandy; Khayal, Inas

    2013-01-01

    Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (pBMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

  16. Accurate Estimation of Orientation Parameters of Uav Images Through Image Registration with Aerial Oblique Imagery

    Science.gov (United States)

    Onyango, F. A.; Nex, F.; Peter, M. S.; Jende, P.

    2017-05-01

    Unmanned Aerial Vehicles (UAVs) have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS) receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU). Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe's ratio test (Lowe, 2004) is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974) is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

  17. ACCURATE ESTIMATION OF ORIENTATION PARAMETERS OF UAV IMAGES THROUGH IMAGE REGISTRATION WITH AERIAL OBLIQUE IMAGERY

    Directory of Open Access Journals (Sweden)

    F. A. Onyango

    2017-05-01

    Full Text Available Unmanned Aerial Vehicles (UAVs have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU. Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe’s ratio test (Lowe, 2004 is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974 is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

  18. Accurate Image Retrieval Algorithm Based on Color and Texture Feature

    Directory of Open Access Journals (Sweden)

    Chunlai Yan

    2013-06-01

    Full Text Available Content-Based Image Retrieval (CBIR is one of the most active hot spots in the current research field of multimedia retrieval. According to the description and extraction of visual content (feature of the image, CBIR aims to find images that contain specified content (feature in the image database. In this paper, several key technologies of CBIR, e. g. the extraction of the color and texture features of the image, as well as the similarity measures are investigated. On the basis of the theoretical research, an image retrieval system based on color and texture features is designed. In this system, the Weighted Color Feature based on HSV space is adopted as a color feature vector, four features of the Co-occurrence Matrix, saying Energy, Entropy, Inertia Quadrature and Correlation, are used to construct texture vectors, and the Euclidean distance for similarity measure is employed as well. Experimental results show that this CBIR system is efficient in image retrieval.

  19. 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...

  20. Speed-of-sound compensated photoacoustic tomography for accurate imaging

    NARCIS (Netherlands)

    Jose, J.; Willemink, G.H.; Steenbergen, W.; Leeuwen, van A.G.J.M.; Manohar, S.

    2012-01-01

    Purpose: In most photoacoustic (PA) tomographic reconstructions, variations in speed-of-sound (SOS) of the subject are neglected under the assumption of acoustic homogeneity. Biological tissue with spatially heterogeneous SOS cannot be accurately reconstructed under this assumption. The authors pres

  1. [Research on maize multispectral image accurate segmentation and chlorophyll index estimation].

    Science.gov (United States)

    Wu, Qian; Sun, Hong; Li, Min-zan; Song, Yuan-yuan; Zhang, Yan-e

    2015-01-01

    In order to rapidly acquire maize growing information in the field, a non-destructive method of maize chlorophyll content index measurement was conducted based on multi-spectral imaging technique and imaging processing technology. The experiment was conducted at Yangling in Shaanxi province of China and the crop was Zheng-dan 958 planted in about 1 000 m X 600 m experiment field. Firstly, a 2-CCD multi-spectral image monitoring system was available to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (Blue (B), Green (G), Red (R): 400-700 nm) and near-infrared (NIR, 760-1 000 nm) band. The multispectral images were output as RGB and NIR images via the system vertically fixed to the ground with vertical distance of 2 m and angular field of 50°. SPAD index of each sample was'measured synchronously to show the chlorophyll content index. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the NIR maize image was selected to segment the maize leaves from background, because there was a big difference showed in gray histogram between plant and soil background. The NIR image segmentation algorithm was conducted following steps of preliminary and accuracy segmentation: (1) The results of OTSU image segmentation method and the variable threshold algorithm were discussed. It was revealed that the latter was better one in corn plant and weed segmentation. As a result, the variable threshold algorithm based on local statistics was selected for the preliminary image segmentation. The expansion and corrosion were used to optimize the segmented image. (2) The region labeling algorithm was used to segment corn plants from soil and weed background with an accuracy of 95. 59 %. And then, the multi-spectral image of maize canopy was accurately segmented in R, G and B band separately. Thirdly, the image parameters were abstracted based on the segmented visible and NIR images. The average gray

  2. Efficient and Accurate Gaussian Image Filtering Using Running Sums

    CERN Document Server

    Elboher, Elhanan

    2011-01-01

    This paper presents a simple and efficient method to convolve an image with a Gaussian kernel. The computation is performed in a constant number of operations per pixel using running sums along the image rows and columns. We investigate the error function used for kernel approximation and its relation to the properties of the input signal. Based on natural image statistics we propose a quadratic form kernel error function so that the output image l2 error is minimized. We apply the proposed approach to approximate the Gaussian kernel by linear combination of constant functions. This results in very efficient Gaussian filtering method. Our experiments show that the proposed technique is faster than state of the art methods while preserving a similar accuracy.

  3. Improving JWST Coronagraphic Performance with Accurate Image Registration

    Science.gov (United States)

    Van Gorkom, Kyle; Pueyo, Laurent; Lajoie, Charles-Philippe; JWST Coronagraphs Working Group

    2016-06-01

    The coronagraphs on the James Webb Space Telescope (JWST) will enable high-contrast observations of faint objects at small separations from bright hosts, such as circumstellar disks, exoplanets, and quasar disks. Despite attenuation by the coronagraphic mask, bright speckles in the host’s point spread function (PSF) remain, effectively washing out the signal from the faint companion. Suppression of these bright speckles is typically accomplished by repeating the observation with a star that lacks a faint companion, creating a reference PSF that can be subtracted from the science image to reveal any faint objects. Before this reference PSF can be subtracted, however, the science and reference images must be aligned precisely, typically to 1/20 of a pixel. Here, we present several such algorithms for performing image registration on JWST coronagraphic images. Using both simulated and pre-flight test data (taken in cryovacuum), we assess (1) the accuracy of each algorithm at recovering misaligned scenes and (2) the impact of image registration on achievable contrast. Proper image registration, combined with post-processing techniques such as KLIP or LOCI, will greatly improve the performance of the JWST coronagraphs.

  4. Accurate measurement of curvilinear shapes by Virtual Image Correlation

    Science.gov (United States)

    Semin, B.; Auradou, H.; François, M. L. M.

    2011-10-01

    The proposed method allows the detection and the measurement, in the sense of metrology, of smooth elongated curvilinear shapes. Such measurements are required in many fields of physics, for example: mechanical engineering, biology or medicine (deflection of beams, fibers or filaments), fluid mechanics or chemistry (detection of fronts). Contrary to actual methods, the result is given in an analytical form of class C∞ (and not a finite set of locations or pixels) thus curvatures and slopes, often of great interest in science, are given with good confidence. The proposed Virtual Image Correlation (VIC) method uses a virtual beam, an image which consists in a lateral expansion of the curve with a bell-shaped gray level. This figure is deformed until it fits the best the physical image with a method issued from the Digital Image Correlation method in use in solid mechanics. The precision of the identification is studied in a benchmark and successfully compared to two state-of-the-art methods. Three practical examples are given: a bar bending under its own weight, a thin fiber transported by a flow within a fracture and a thermal front. The first allows a comparison with theoretical solution, the second shows the ability of the method to deal with complex shapes and crossings and the third deals with ill-defined image.

  5. Quicksilver: Fast predictive image registration - A deep learning approach.

    Science.gov (United States)

    Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

    2017-07-11

    This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X.

    Science.gov (United States)

    Faraggi, Eshel; Kloczkowski, Andrzej

    2017-01-01

    Accurate prediction of protein secondary structure and other one-dimensional structure features is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. SPINE-X is a software package to predict secondary structure as well as accessible surface area and dihedral angles ϕ and ψ. For secondary structure SPINE-X achieves an accuracy of between 81 and 84 % depending on the dataset and choice of tests. The Pearson correlation coefficient for accessible surface area prediction is 0.75 and the mean absolute error from the ϕ and ψ dihedral angles are 20(∘) and 33(∘), respectively. The source code and a Linux executables for SPINE-X are available from Research and Information Systems at http://mamiris.com .

  7. The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration

    Science.gov (United States)

    Saenz, Daniel L.; Kim, Hojin; Chen, Josephine; Stathakis, Sotirios; Kirby, Neil

    2016-09-01

    The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu’s method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

  8. Feasibility study for image guided kidney surgery: assessment of required intraoperative surface for accurate image to physical space registrations

    Science.gov (United States)

    Benincasa, Anne B.; Clements, Logan W.; Herrell, S. Duke; Chang, Sam S.; Cookson, Michael S.; Galloway, Robert L.

    2006-03-01

    Currently, the removal of kidney tumor masses uses only direct or laparoscopic visualizations, resulting in prolonged procedure and recovery times and reduced clear margin. Applying current image guided surgery (IGS) techniques, as those used in liver cases, to kidney resections (nephrectomies) presents a number of complications. Most notably is the limited field of view of the intraoperative kidney surface, which constrains the ability to obtain a surface delineation that is geometrically descriptive enough to drive a surface-based registration. Two different phantom orientations were used to model the laparoscopic and traditional partial nephrectomy views. For the laparoscopic view, fiducial point sets were compiled from a CT image volume using anatomical features such as the renal artery and vein. For the traditional view, markers attached to the phantom set-up were used for fiducials and targets. The fiducial points were used to perform a point-based registration, which then served as a guide for the surface-based registration. Laser range scanner (LRS) obtained surfaces were registered to each phantom surface using a rigid iterative closest point algorithm. Subsets of each phantom's LRS surface were used in a robustness test to determine the predictability of their registrations to transform the entire surface. Results from both orientations suggest that about half of the kidney's surface needs to be obtained intraoperatively for accurate registrations between the image surface and the LRS surface, suggesting the obtained kidney surfaces were geometrically descriptive enough to perform accurate registrations. This preliminary work paves the way for further development of kidney IGS systems.

  9. Mind-set and close relationships: when bias leads to (In)accurate predictions.

    Science.gov (United States)

    Gagné, F M; Lydon, J E

    2001-07-01

    The authors investigated whether mind-set influences the accuracy of relationship predictions. Because people are more biased in their information processing when thinking about implementing an important goal, relationship predictions made in an implemental mind-set were expected to be less accurate than those made in a more impartial deliberative mind-set. In Study 1, open-ended thoughts of students about to leave for university were coded for mind-set. In Study 2, mind-set about a major life goal was assessed using a self-report measure. In Study 3, mind-set was experimentally manipulated. Overall, mind-set interacted with forecasts to predict relationship survival. Forecasts were more accurate in a deliberative mind-set than in an implemental mind-set. This effect was more pronounced for long-term than for short-term relationship survival. Finally, deliberatives were not pessimistic; implementals were unduly optimistic.

  10. Accurate non-invasive image-based cytotoxicity assays for cultured cells

    Directory of Open Access Journals (Sweden)

    Brouwer Jaap

    2010-06-01

    Full Text Available Abstract Background The CloneSelect™ Imager system is an image-based visualisation system for cell growth assessment. Traditionally cell proliferation is measured with the colorimetric MTT assay. Results Here we show that both the CloneSelect Imager and the MTT approach result in comparable EC50 values when assaying the cytotoxicity of cisplatin and oxaliplatin on various cell lines. However, the image-based technique was found non-invasive, considerably quicker and more accurate than the MTT assay. Conclusions This new image-based technique has the potential to replace the cumbersome MTT assay when fast, unbiased and high-throughput cytotoxicity assays are requested.

  11. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

    Directory of Open Access Journals (Sweden)

    Chen Ke

    2008-05-01

    Full Text Available Abstract Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is

  12. Rapid yet accurate first principle based predictions of alkali halide crystal phases using alchemical perturbation

    CERN Document Server

    Solovyeva, Alisa

    2016-01-01

    We assess the predictive power of alchemical perturbations for estimating fundamental properties in ionic crystals. Using density functional theory we have calculated formation energies, lattice constants, and bulk moduli for all sixteen iso-valence-electronic combinations of pure pristine alkali halides involving elements $A \\in \\{$Na, K, Rb, Cs$\\}$ and $X \\in \\{$F, Cl, Br, I$\\}$. For rock salt, zincblende and cesium chloride symmetry, alchemical Hellmann-Feynman derivatives, evaluated along lattice scans of sixteen reference crystals, have been obtained for all respective 16$\\times$15 combinations of reference and predicted target crystals. Mean absolute errors (MAE) are on par with density functional theory level of accuracy for energies and bulk modulus. Predicted lattice constants are less accurate. NaCl is the best reference salt for alchemical estimates of relative energies (MAE $<$ 40 meV/atom) while alkali fluorides are the worst. By contrast, lattice constants are predicted best using NaF as a re...

  13. A New Method for Accurate Prediction of Ship’s Inertial Stopping Distance

    Directory of Open Access Journals (Sweden)

    Langxiong Gan

    2013-10-01

    Full Text Available This study aims to research the prediction of ship’s inertial stopping distance. Accurate prediction of a ship’s inertial stopping distance helps the duty officers to make the collision avoidance decisions effectively. In this study ship’s inertial stopping distance is calculated using the ALE (Arbitrary Lagrangian Eulerian algorithm implemented in the FLUENT code. Firstly, a method for predicting the inertial stopping distance of a floating body based on the FLUENT code is established. Then, the results calculated by the method are compared with those obtained from the empirical formulae and the physical model tests. The comparison result indicates that the proposed method is robust and can be used effectively to predict the ship’s inertial stopping distance.

  14. Accurate band-to-band registration of AOTF imaging spectrometer using motion detection technology

    Science.gov (United States)

    Zhou, Pengwei; Zhao, Huijie; Jin, Shangzhong; Li, Ningchuan

    2016-05-01

    This paper concerns the problem of platform vibration induced band-to-band misregistration with acousto-optic imaging spectrometer in spaceborne application. Registrating images of different bands formed at different time or different position is difficult, especially for hyperspectral images form acousto-optic tunable filter (AOTF) imaging spectrometer. In this study, a motion detection method is presented using the polychromatic undiffracted beam of AOTF. The factors affecting motion detect accuracy are analyzed theoretically, and calculations show that optical distortion is an easily overlooked factor to achieve accurate band-to-band registration. Hence, a reflective dual-path optical system has been proposed for the first time, with reduction of distortion and chromatic aberration, indicating the potential of higher registration accuracy. Consequently, a spectra restoration experiment using additional motion detect channel is presented for the first time, which shows the accurate spectral image registration capability of this technique.

  15. PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif

    OpenAIRE

    Tang, Shengnan; Li, Tonghua; Cong, Peisheng; Xiong, Wenwei; Wang, Zhiheng; Sun, Jiangming

    2013-01-01

    Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapi...

  16. 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.

  17. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    Science.gov (United States)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2017-04-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  18. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    Science.gov (United States)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2016-12-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  19. Accurately Estimating the State of a Geophysical System with Sparse Observations: Predicting the Weather

    CERN Document Server

    An, Zhe; Abarbanel, Henry D I

    2014-01-01

    Utilizing the information in observations of a complex system to make accurate predictions through a quantitative model when observations are completed at time $T$, requires an accurate estimate of the full state of the model at time $T$. When the number of measurements $L$ at each observation time within the observation window is larger than a sufficient minimum value $L_s$, the impediments in the estimation procedure are removed. As the number of available observations is typically such that $L \\ll L_s$, additional information from the observations must be presented to the model. We show how, using the time delays of the measurements at each observation time, one can augment the information transferred from the data to the model, removing the impediments to accurate estimation and permitting dependable prediction. We do this in a core geophysical fluid dynamics model, the shallow water equations, at the heart of numerical weather prediction. The method is quite general, however, and can be utilized in the a...

  20. A Single Linear Prediction Filter that Accurately Predicts the AL Index

    Science.gov (United States)

    McPherron, R. L.; Chu, X.

    2015-12-01

    The AL index is a measure of the strength of the westward electrojet flowing along the auroral oval. It has two components: one from the global DP-2 current system and a second from the DP-1 current that is more localized near midnight. It is generally believed that the index a very poor measure of these currents because of its dependence on the distance of stations from the source of the two currents. In fact over season and solar cycle the coupling strength defined as the steady state ratio of the output AL to the input coupling function varies by a factor of four. There are four factors that lead to this variation. First is the equinoctial effect that modulates coupling strength with peaks (strongest coupling) at the equinoxes. Second is the saturation of the polar cap potential which decreases coupling strength as the strength of the driver increases. Since saturation occurs more frequently at solar maximum we obtain the result that maximum coupling strength occurs at equinox at solar minimum. A third factor is ionospheric conductivity with stronger coupling at summer solstice as compared to winter. The fourth factor is the definition of a solar wind coupling function appropriate to a given index. We have developed an optimum coupling function depending on solar wind speed, density, transverse magnetic field, and IMF clock angle which is better than previous functions. Using this we have determined the seasonal variation of coupling strength and developed an inverse function that modulates the optimum coupling function so that all seasonal variation is removed. In a similar manner we have determined the dependence of coupling strength on solar wind driver strength. The inverse of this function is used to scale a linear prediction filter thus eliminating the dependence on driver strength. Our result is a single linear filter that is adjusted in a nonlinear manner by driver strength and an optimum coupling function that is seasonal modulated. Together this

  1. Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

    Science.gov (United States)

    Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

    2017-07-01

    A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

  2. Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

    CERN Document Server

    Kainmueller, Dagmar

    2014-01-01

    ? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

  3. Role of MR imaging in surgical planning and prediction of successful surgical repair of pelvic organ prolapse

    Directory of Open Access Journals (Sweden)

    Ebtesam Moustafa Kamal

    2013-09-01

    Conclusion: Magnetic resonance imaging can accurately localize pelvic floor defects, evaluate success or failure of surgical procedures, predict the need for more extensive reconstruction, and identify complications.

  4. Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image

    Directory of Open Access Journals (Sweden)

    Zelang Miao

    2015-01-01

    Full Text Available The extraction of road centerline from the classified image is a fundamental image analysis technology. Common problems encountered in road centerline extraction include low ability for coping with the general case, production of undesired objects, and inefficiency. To tackle these limitations, this paper presents a novel accurate centerline extraction method using Gaussian mixture model (GMM and subspace constraint mean shift (SCMS. The proposed method consists of three main steps. GMM is first used to partition the classified image into several clusters. The major axis of the ellipsoid of each cluster is extracted and deemed to be taken as the initial centerline. Finally, the initial result is adjusted using SCMS to produce precise road centerline. Both simulated and real datasets are used to validate the proposed method. Preliminary results demonstrate that the proposed method provides a comparatively robust solution for accurate centerline extraction from a classified image.

  5. More accurate recombination prediction in HIV-1 using a robust decoding algorithm for HMMs

    Directory of Open Access Journals (Sweden)

    Brown Daniel G

    2011-05-01

    Full Text Available Abstract Background Identifying recombinations in HIV is important for studying the epidemiology of the virus and aids in the design of potential vaccines and treatments. The previous widely-used tool for this task uses the Viterbi algorithm in a hidden Markov model to model recombinant sequences. Results We apply a new decoding algorithm for this HMM that improves prediction accuracy. Exactly locating breakpoints is usually impossible, since different subtypes are highly conserved in some sequence regions. Our algorithm identifies these sites up to a certain error tolerance. Our new algorithm is more accurate in predicting the location of recombination breakpoints. Our implementation of the algorithm is available at http://www.cs.uwaterloo.ca/~jmtruszk/jphmm_balls.tar.gz. Conclusions By explicitly accounting for uncertainty in breakpoint positions, our algorithm offers more reliable predictions of recombination breakpoints in HIV-1. We also document a new domain of use for our new decoding approach in HMMs.

  6. Planar Near-Field Phase Retrieval Using GPUs for Accurate THz Far-Field Prediction

    Science.gov (United States)

    Junkin, Gary

    2013-04-01

    With a view to using Phase Retrieval to accurately predict Terahertz antenna far-field from near-field intensity measurements, this paper reports on three fundamental advances that achieve very low algorithmic error penalties. The first is a new Gaussian beam analysis that provides accurate initial complex aperture estimates including defocus and astigmatic phase errors, based only on first and second moment calculations. The second is a powerful noise tolerant near-field Phase Retrieval algorithm that combines Anderson's Plane-to-Plane (PTP) with Fienup's Hybrid-Input-Output (HIO) and Successive Over-Relaxation (SOR) to achieve increased accuracy at reduced scan separations. The third advance employs teraflop Graphical Processing Units (GPUs) to achieve practically real time near-field phase retrieval and to obtain the optimum aperture constraint without any a priori information.

  7. 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...

  8. Medical Image Watermarking Technique for Accurate Tamper Detection in ROI and Exact Recovery of ROI.

    Science.gov (United States)

    Eswaraiah, R; Sreenivasa Reddy, E

    2014-01-01

    In telemedicine while transferring medical images tampers may be introduced. Before making any diagnostic decisions, the integrity of region of interest (ROI) of the received medical image must be verified to avoid misdiagnosis. In this paper, we propose a novel fragile block based medical image watermarking technique to avoid embedding distortion inside ROI, verify integrity of ROI, detect accurately the tampered blocks inside ROI, and recover the original ROI with zero loss. In this proposed method, the medical image is segmented into three sets of pixels: ROI pixels, region of noninterest (RONI) pixels, and border pixels. Then, authentication data and information of ROI are embedded in border pixels. Recovery data of ROI is embedded into RONI. Results of experiments conducted on a number of medical images reveal that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI with 100% accuracy, and recovers the original ROI without any loss.

  9. PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB

    NARCIS (Netherlands)

    Stamhuis, Eize; Thielicke, William

    2014-01-01

    Digital particle image velocimetry (DPIV) is a non-intrusive analysis technique that is very popular for mapping flows quantitatively. To get accurate results, in particular in complex flow fields, a number of challenges have to be faced and solved: The quality of the flow measurements is affected b

  10. PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB

    NARCIS (Netherlands)

    Stamhuis, Eize; Thielicke, William

    2014-01-01

    Digital particle image velocimetry (DPIV) is a non-intrusive analysis technique that is very popular for mapping flows quantitatively. To get accurate results, in particular in complex flow fields, a number of challenges have to be faced and solved: The quality of the flow measurements is affected

  11. A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

    Energy Technology Data Exchange (ETDEWEB)

    Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.; Arkin, Adam P.

    2004-12-01

    We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, and its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.

  12. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    Science.gov (United States)

    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. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

  13. Interferometer predictions with triangulated images

    DEFF Research Database (Denmark)

    Brinch, Christian; Dullemond, C. P.

    2014-01-01

    Interferometers play an increasingly important role for spatially resolved observations. If employed at full potential, interferometry can probe an enormous dynamic range in spatial scale. Interpretation of the observed visibilities requires the numerical computation of Fourier integrals over...... the synthetic model images. To get the correct values of these integrals, the model images must have the right size and resolution. Insufficient care in these choices can lead to wrong results. We present a new general-purpose scheme for the computation of visibilities of radiative transfer images. Our method...... requires a model image that is a list of intensities at arbitrarily placed positions on the image-plane. It creates a triangulated grid from these vertices, and assumes that the intensity inside each triangle of the grid is a linear function. The Fourier integral over each triangle is then evaluated...

  14. Special purpose hybrid transfinite elements and unified computational methodology for accurately predicting thermoelastic stress waves

    Science.gov (United States)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1988-01-01

    This paper represents an attempt to apply extensions of a hybrid transfinite element computational approach for accurately predicting thermoelastic stress waves. The applicability of the present formulations for capturing the thermal stress waves induced by boundary heating for the well known Danilovskaya problems is demonstrated. A unique feature of the proposed formulations for applicability to the Danilovskaya problem of thermal stress waves in elastic solids lies in the hybrid nature of the unified formulations and the development of special purpose transfinite elements in conjunction with the classical Galerkin techniques and transformation concepts. Numerical test cases validate the applicability and superior capability to capture the thermal stress waves induced due to boundary heating.

  15. 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].

  16. 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.

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

    Directory of Open Access Journals (Sweden)

    George Notas

    Full Text Available 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.

  18. LogGPO: An accurate communication model for performance prediction of MPI programs

    Institute of Scientific and Technical Information of China (English)

    CHEN WenGuang; ZHAI JiDong; ZHANG Jin; ZHENG WeiMin

    2009-01-01

    Message passing interface (MPI) is the de facto standard in writing parallel scientific applications on distributed memory systems. Performance prediction of MPI programs on current or future parallel sys-terns can help to find system bottleneck or optimize programs. To effectively analyze and predict per-formance of a large and complex MPI program, an efficient and accurate communication model is highly needed. A series of communication models have been proposed, such as the LogP model family, which assume that the sending overhead, message transmission, and receiving overhead of a communication is not overlapped and there is a maximum overlap degree between computation and communication. However, this assumption does not always hold for MPI programs because either sending or receiving overhead introduced by MPI implementations can decrease potential overlap for large messages. In this paper, we present a new communication model, named LogGPO, which captures the potential overlap between computation with communication of MPI programs. We design and implement a trace-driven simulator to verify the LogGPO model by predicting performance of point-to-point communication and two real applications CG and Sweep3D. The average prediction errors of LogGPO model are 2.4% and 2.0% for these two applications respectively, while the average prediction errors of LogGP model are 38.3% and 9.1% respectively.

  19. An accurate and efficient numerical framework for adaptive numerical weather prediction

    CERN Document Server

    Tumolo, G

    2014-01-01

    We present an accurate and efficient discretization approach for the adaptive discretization of typical model equations employed in numerical weather prediction. A semi-Lagrangian approach is combined with the TR-BDF2 semi-implicit time discretization method and with a spatial discretization based on adaptive discontinuous finite elements. The resulting method has full second order accuracy in time and can employ polynomial bases of arbitrarily high degree in space, is unconditionally stable and can effectively adapt the number of degrees of freedom employed in each element, in order to balance accuracy and computational cost. The p-adaptivity approach employed does not require remeshing, therefore it is especially suitable for applications, such as numerical weather prediction, in which a large number of physical quantities are associated with a given mesh. Furthermore, although the proposed method can be implemented on arbitrary unstructured and nonconforming meshes, even its application on simple Cartesian...

  20. Accurate inference of shoot biomass from high-throughput images of cereal plants

    Directory of Open Access Journals (Sweden)

    Tester Mark

    2011-02-01

    Full Text Available Abstract With the establishment of advanced technology facilities for high throughput plant phenotyping, the problem of estimating plant biomass of individual plants from their two dimensional images is becoming increasingly important. The approach predominantly cited in literature is to estimate the biomass of a plant as a linear function of the projected shoot area of plants in the images. However, the estimation error from this model, which is solely a function of projected shoot area, is large, prohibiting accurate estimation of the biomass of plants, particularly for the salt-stressed plants. In this paper, we propose a method based on plant specific weight for improving the accuracy of the linear model and reducing the estimation bias (the difference between actual shoot dry weight and the value of the shoot dry weight estimated with a predictive model. For the proposed method in this study, we modeled the plant shoot dry weight as a function of plant area and plant age. The data used for developing our model and comparing the results with the linear model were collected from a completely randomized block design experiment. A total of 320 plants from two bread wheat varieties were grown in a supported hydroponics system in a greenhouse. The plants were exposed to two levels of hydroponic salt treatments (NaCl at 0 and 100 mM for 6 weeks. Five harvests were carried out. Each time 64 randomly selected plants were imaged and then harvested to measure the shoot fresh weight and shoot dry weight. The results of statistical analysis showed that with our proposed method, most of the observed variance can be explained, and moreover only a small difference between actual and estimated shoot dry weight was obtained. The low estimation bias indicates that our proposed method can be used to estimate biomass of individual plants regardless of what variety the plant is and what salt treatment has been applied. We validated this model on an independent

  1. Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images

    Science.gov (United States)

    Lavoie, Benjamin R.; Okoniewski, Michal; Fear, Elise C.

    2016-01-01

    We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range. PMID:27611785

  2. Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge

    Directory of Open Access Journals (Sweden)

    Gui-Song Xia

    2015-11-01

    Full Text Available It is a challenging problem to efficiently interpret the large volumes of remotely sensed image data being collected in the current age of remote sensing “big data”. Although human visual interpretation can yield accurate annotation of remote sensing images, it demands considerable expert knowledge and is always time-consuming, which strongly hinders its efficiency. Alternatively, intelligent approaches (e.g., supervised classification and unsupervised clustering can speed up the annotation process through the application of advanced image analysis and data mining technologies. However, high-quality expert-annotated samples are still a prerequisite for intelligent approaches to achieve accurate results. Thus, how to efficiently annotate remote sensing images with little expert knowledge is an important and inevitable problem. To address this issue, this paper introduces a novel active clustering method for the annotation of high-resolution remote sensing images. More precisely, given a set of remote sensing images, we first build a graph based on these images and then gradually optimize the structure of the graph using a cut-collect process, which relies on a graph-based spectral clustering algorithm and pairwise constraints that are incrementally added via active learning. The pairwise constraints are simply similarity/dissimilarity relationships between the most uncertain pairwise nodes on the graph, which can be easily determined by non-expert human oracles. Furthermore, we also propose a strategy to adaptively update the number of classes in the clustering algorithm. In contrast with existing methods, our approach can achieve high accuracy in the task of remote sensing image annotation with relatively little expert knowledge, thereby greatly lightening the workload burden and reducing the requirements regarding expert knowledge. Experiments on several datasets of remote sensing images show that our algorithm achieves state

  3. Physical modeling of real-world slingshots for accurate speed predictions

    CERN Document Server

    Yeats, Bob

    2016-01-01

    We discuss the physics and modeling of latex-rubber slingshots. The goal is to get accurate speed predictions inspite of the significant real world difficulties of force drift, force hysteresis, rubber ageing, and the very non- linear, non-ideal, force vs. pull distance curves of slingshot rubber bands. Slingshots are known to shoot faster under some circumstances when the bands are tapered rather than having constant width and stiffness. We give both qualitative understanding and numerical predictions of this effect. We consider two models. The first is based on conservation of energy and is easier to implement, but cannot determine the speeds along the rubber bands without making assumptions. The second, treats the bands as a series of mass points subject to being pulled by immediately adjacent mass points according to how much the rubber has been stretched on the two adjacent sides. This is a classic many-body F=ma problem but convergence requires using a particular numerical technique. It gives accurate p...

  4. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

    Directory of Open Access Journals (Sweden)

    Jesse S. Jin

    2010-10-01

    Full Text Available Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  5. Sensor data fusion for accurate cloud presence prediction using Dempster-Shafer evidence theory.

    Science.gov (United States)

    Li, Jiaming; Luo, Suhuai; Jin, Jesse S

    2010-01-01

    Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.

  6. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    Science.gov (United States)

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  7. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    Science.gov (United States)

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  8. Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging.

    Science.gov (United States)

    Choi, Lark Kwon; You, Jaehee; Bovik, Alan Conrad

    2015-11-01

    We propose a referenceless perceptual fog density prediction model based on natural scene statistics (NSS) and fog aware statistical features. The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single image without reference to a corresponding fog-free image, without dependence on salient objects in a scene, without side geographical camera information, without estimating a depth-dependent transmission map, and without training on human-rated judgments. FADE only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. Fog aware statistical features that define the perceptual fog density index derive from a space domain NSS model and the observed characteristics of foggy images. FADE not only predicts perceptual fog density for the entire image, but also provides a local fog density index for each patch. The predicted fog density using FADE correlates well with human judgments of fog density taken in a subjective study on a large foggy image database. As applications, FADE not only accurately assesses the performance of defogging algorithms designed to enhance the visibility of foggy images, but also is well suited for image defogging. A new FADE-based referenceless perceptual image defogging, dubbed DEnsity of Fog Assessment-based DEfogger (DEFADE) achieves better results for darker, denser foggy images as well as on standard foggy images than the state of the art defogging methods. A software release of FADE and DEFADE is available online for public use: http://live.ece.utexas.edu/research/fog/index.html.

  9. Automated Image-Based Procedures for Accurate Artifacts 3D Modeling and Orthoimage Generation

    Directory of Open Access Journals (Sweden)

    Marc Pierrot-Deseilligny

    2011-12-01

    Full Text Available The accurate 3D documentation of architectures and heritages is getting very common and required in different application contexts. The potentialities of the image-based approach are nowadays very well-known but there is a lack of reliable, precise and flexible solutions, possibly open-source, which could be used for metric and accurate documentation or digital conservation and not only for simple visualization or web-based applications. The article presents a set of photogrammetric tools developed in order to derive accurate 3D point clouds and orthoimages for the digitization of archaeological and architectural objects. The aim is also to distribute free solutions (software, methodologies, guidelines, best practices, etc. based on 3D surveying and modeling experiences, useful in different application contexts (architecture, excavations, museum collections, heritage documentation, etc. and according to several representations needs (2D technical documentation, 3D reconstruction, web visualization, etc..

  10. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

    Science.gov (United States)

    Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

    2017-01-01

    Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have

  11. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

    Science.gov (United States)

    Li, Zhen; Zhang, Renyu

    2017-01-01

    Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

  12. Development of anatomically and dielectrically accurate breast phantoms for microwave imaging applications

    Science.gov (United States)

    O'Halloran, M.; Lohfeld, S.; Ruvio, G.; Browne, J.; Krewer, F.; Ribeiro, C. O.; Inacio Pita, V. C.; Conceicao, R. C.; Jones, E.; Glavin, M.

    2014-05-01

    Breast cancer is one of the most common cancers in women. In the United States alone, it accounts for 31% of new cancer cases, and is second only to lung cancer as the leading cause of deaths in American women. More than 184,000 new cases of breast cancer are diagnosed each year resulting in approximately 41,000 deaths. Early detection and intervention is one of the most significant factors in improving the survival rates and quality of life experienced by breast cancer sufferers, since this is the time when treatment is most effective. One of the most promising breast imaging modalities is microwave imaging. The physical basis of active microwave imaging is the dielectric contrast between normal and malignant breast tissue that exists at microwave frequencies. The dielectric contrast is mainly due to the increased water content present in the cancerous tissue. Microwave imaging is non-ionizing, does not require breast compression, is less invasive than X-ray mammography, and is potentially low cost. While several prototype microwave breast imaging systems are currently in various stages of development, the design and fabrication of anatomically and dielectrically representative breast phantoms to evaluate these systems is often problematic. While some existing phantoms are composed of dielectrically representative materials, they rarely accurately represent the shape and size of a typical breast. Conversely, several phantoms have been developed to accurately model the shape of the human breast, but have inappropriate dielectric properties. This study will brie y review existing phantoms before describing the development of a more accurate and practical breast phantom for the evaluation of microwave breast imaging systems.

  13. PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB

    Directory of Open Access Journals (Sweden)

    William Thielicke

    2014-10-01

    Full Text Available Digital particle image velocimetry (DPIV is a non-intrusive analysis technique that is very popular for mapping flows quantitatively. To get accurate results, in particular in complex flow fields, a number of challenges have to be faced and solved: The quality of the flow measurements is affected by computational details such as image pre-conditioning, sub-pixel peak estimators, data validation procedures, interpolation algorithms and smoothing methods. The accuracy of several algorithms was determined and the best performing methods were implemented in a user-friendly open-source tool for performing DPIV flow analysis in Matlab.

  14. Accurate color synthesis of three-dimensional objects in an image

    Science.gov (United States)

    Xin, John H.; Shen, Hui-Liang

    2004-05-01

    Our study deals with color synthesis of a three-dimensional object in an image; i.e., given a single image, a target color can be accurately mapped onto the object such that the color appearance of the synthesized object closely resembles that of the actual one. As it is almost impossible to acquire the complete geometric description of the surfaces of an object in an image, this study attempted to recover the implicit description of geometry for the color synthesis. The description was obtained from either a series of spectral reflectances or the RGB signals at different surface positions on the basis of the dichromatic reflection model. The experimental results showed that this implicit image-based representation is related to the object geometry and is sufficient for accurate color synthesis of three-dimensional objects in an image. The method established is applicable to the color synthesis of both rigid and deformable objects and should contribute to color fidelity in virtual design, manufacturing, and retailing.

  15. Towards first-principles based prediction of highly accurate electrochemical Pourbiax diagrams

    Science.gov (United States)

    Zeng, Zhenhua; Chan, Maria; Greeley, Jeff

    2015-03-01

    Electrochemical Pourbaix diagrams lie at the heart of aqueous electrochemical processes and are central to the identification of stable phases of metals for processes ranging from electrocatalysis to corrosion. Even though standard DFT calculations are potentially powerful tools for the prediction of such Pourbaix diagrams, inherent errors in the description of strongly-correlated transition metal (hydr)oxides, together with neglect of weak van der Waals (vdW) interactions, has limited the reliability of the predictions for even the simplest bulk systems; corresponding predictions for more complex alloy or surface structures are even more challenging . Through introduction of a Hubbard U correction, employment of a state-of-the-art van der Waals functional, and use of pure water as a reference state for the calculations, these errors are systematically corrected. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxide, oxyhydroxide, binary and ternary oxides where the corresponding thermodynamics of oxidation and reduction can be accurately described with standard errors of less than 0.04 eV in comparison with experiment.

  16. Genetic crossovers are predicted accurately by the computed human recombination map.

    Directory of Open Access Journals (Sweden)

    Pavel P Khil

    2010-01-01

    Full Text Available Hotspots of meiotic recombination can change rapidly over time. This instability and the reported high level of inter-individual variation in meiotic recombination puts in question the accuracy of the calculated hotspot map, which is based on the summation of past genetic crossovers. To estimate the accuracy of the computed recombination rate map, we have mapped genetic crossovers to a median resolution of 70 Kb in 10 CEPH pedigrees. We then compared the positions of crossovers with the hotspots computed from HapMap data and performed extensive computer simulations to compare the observed distributions of crossovers with the distributions expected from the calculated recombination rate maps. Here we show that a population-averaged hotspot map computed from linkage disequilibrium data predicts well present-day genetic crossovers. We find that computed hotspot maps accurately estimate both the strength and the position of meiotic hotspots. An in-depth examination of not-predicted crossovers shows that they are preferentially located in regions where hotspots are found in other populations. In summary, we find that by combining several computed population-specific maps we can capture the variation in individual hotspots to generate a hotspot map that can predict almost all present-day genetic crossovers.

  17. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

    Energy Technology Data Exchange (ETDEWEB)

    Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au [Centre for Molecular Simulation, Swinburne University of Technology, PO Box 218, Hawthorn, Victoria 3122 (Australia)

    2013-11-21

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys. 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.

  18. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

    Science.gov (United States)

    Shvab, I.; Sadus, Richard J.

    2013-11-01

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm3 for a wide range of temperatures (298-650 K) and pressures (0.1-700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys. 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.

  19. Predictive depth coding of wavelet transformed images

    Science.gov (United States)

    Lehtinen, Joonas

    1999-10-01

    In this paper, a new prediction based method, predictive depth coding, for lossy wavelet image compression is presented. It compresses a wavelet pyramid composition by predicting the number of significant bits in each wavelet coefficient quantized by the universal scalar quantization and then by coding the prediction error with arithmetic coding. The adaptively found linear prediction context covers spatial neighbors of the coefficient to be predicted and the corresponding coefficients on lower scale and in the different orientation pyramids. In addition to the number of significant bits, the sign and the bits of non-zero coefficients are coded. The compression method is tested with a standard set of images and the results are compared with SFQ, SPIHT, EZW and context based algorithms. Even though the algorithm is very simple and it does not require any extra memory, the compression results are relatively good.

  20. 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.

  1. Distance scaling method for accurate prediction of slowly varying magnetic fields in satellite missions

    Science.gov (United States)

    Zacharias, Panagiotis P.; Chatzineofytou, Elpida G.; Spantideas, Sotirios T.; Capsalis, Christos N.

    2016-07-01

    In the present work, the determination of the magnetic behavior of localized magnetic sources from near-field measurements is examined. The distance power law of the magnetic field fall-off is used in various cases to accurately predict the magnetic signature of an equipment under test (EUT) consisting of multiple alternating current (AC) magnetic sources. Therefore, parameters concerning the location of the observation points (magnetometers) are studied towards this scope. The results clearly show that these parameters are independent of the EUT's size and layout. Additionally, the techniques developed in the present study enable the placing of the magnetometers close to the EUT, thus achieving high signal-to-noise ratio (SNR). Finally, the proposed method is verified by real measurements, using a mobile phone as an EUT.

  2. Accurate Mobility Modeling and Location Prediction Based on Pattern Analysis of Handover Series in Mobile Networks

    Directory of Open Access Journals (Sweden)

    Péter Fülöp

    2009-01-01

    Full Text Available The efficient dimensioning of cellular wireless access networks depends highly on the accuracy of the underlying mathematical models of user distribution and traffic estimations. Mobility prediction also considered as an effective method contributing to the accuracy of IP multicast based multimedia transmissions, and ad hoc routing algorithms. In this paper we focus on the tradeoff between the accuracy and the complexity of the mathematical models used to describe user movements in the network. We propose mobility model extension, in order to utilize user's movement history thus providing more accurate results than other widely used models in the literature. The new models are applicable in real-life scenarios, because these rely on additional information effectively available in cellular networks (e.g. handover history, too. The complexity of the proposed models is analyzed, and the accuracy is justified by means of simulation.

  3. Mass transport and direction dependent battery modeling for accurate on-line power capability prediction

    Energy Technology Data Exchange (ETDEWEB)

    Wiegman, H.L.N. [General Electric Corporate Research and Development, Schenectady, NY (United States)

    2000-07-01

    Some recent advances in battery modeling were discussed with reference to on-line impedance estimates and power performance predictions for aqueous solution, porous electrode cell structures. The objective was to determine which methods accurately estimate a battery's internal state and power capability while operating a charge and sustaining a hybrid electric vehicle (HEV) over a wide range of driving conditions. The enhancements to the Randles-Ershler equivalent electrical model of common cells with lead-acid, nickel-cadmium and nickel-metal hydride chemistries were described. This study also investigated which impedances are sensitive to boundary layer charge concentrations and mass transport limitations. Non-linear impedances were shown to significantly affect the battery's ability to process power. The main advantage of on-line estimating a battery's impedance state and power capability is that the battery can be optimally sized for any application. refs., tabs., figs., append.

  4. Fast and accurate prediction of numerical relativity waveforms from binary black hole mergers using surrogate models

    CERN Document Server

    Blackman, Jonathan; Galley, Chad R; Szilagyi, Bela; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-01-01

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. In this paper, we construct an accurate and fast-to-evaluate surrogate model for numerical relativity (NR) waveforms from non-spinning binary black hole coalescences with mass ratios from $1$ to $10$ and durations corresponding to about $15$ orbits before merger. Our surrogate, which is built using reduced order modeling techniques, is distinct from traditional modeling efforts. We find that the full multi-mode surrogate model agrees with waveforms generated by NR to within the numerical error of the NR code. In particular, we show that our modeling strategy produces surrogates which can correctly predict NR waveforms that were {\\em not} used for the surrogate's training. For all practical purposes, then, the surrogate waveform model is equivalent to the high-accuracy, large-scale simulation waveform but can be evaluated in a millisecond to a second dependin...

  5. 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.

  6. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    Science.gov (United States)

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  9. 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

  10. PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

    Science.gov (United States)

    Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

    2015-07-05

    Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

  11. Precision Imaging: more descriptive, predictive and integrative imaging.

    Science.gov (United States)

    Frangi, Alejandro F; Taylor, Zeike A; Gooya, Ali

    2016-10-01

    Medical image analysis has grown into a matured field challenged by progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, biomedical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies. Consideration on how the field has evolved and the experience of the work carried out over the last 15 years in our centre, has led us to envision a future emphasis of medical imaging in Precision Imaging. Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the cross-roads between, and unifying the efforts behind mechanistic and phenomenological model-based imaging. It captures three main directions in the effort to deal with the information deluge in imaging sciences, and thus achieve wisdom from data, information, and knowledge. Precision Imaging is finally characterised by being descriptive, predictive and integrative about the imaged object. This paper provides a brief and personal perspective on how the field has evolved, summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and current trends in the field.

  12. Accurate Image Search using Local Descriptors into a Compact Image Representation

    Directory of Open Access Journals (Sweden)

    Soumia Benkrama

    2013-01-01

    Full Text Available Progress in image retrieval by using low-level features, such as colors, textures and shapes, the performance is still unsatisfied as there are existing gaps between low-level features and high-level semantic concepts. In this work, we present an improved implementation for the bag of visual words approach. We propose a image retrieval system based on bag-of-features (BoF model by using scale invariant feature transform (SIFT and speeded up robust features (SURF. In literature SIFT and SURF give of good results. Based on this observation, we decide to use a bag-of-features approach over quaternion zernike moments (QZM. We compare the results of SIFT and SURF with those of QZM. We propose an indexing method for content based search task that aims to retrieve collection of images and returns a ranked list of objects in response to a query image. Experimental results with the Coil-100 and corel-1000 image database, demonstrate that QZM produces a better performance than known representations (SIFT and SURF.

  13. Curvelet-based sampling for accurate and efficient multimodal image registration

    Science.gov (United States)

    Safran, M. N.; Freiman, M.; Werman, M.; Joskowicz, L.

    2009-02-01

    We present a new non-uniform adaptive sampling method for the estimation of mutual information in multi-modal image registration. The method uses the Fast Discrete Curvelet Transform to identify regions along anatomical curves on which the mutual information is computed. Its main advantages of over other non-uniform sampling schemes are that it captures the most informative regions, that it is invariant to feature shapes, orientations, and sizes, that it is efficient, and that it yields accurate results. Extensive evaluation on 20 validated clinical brain CT images to Proton Density (PD) and T1 and T2-weighted MRI images from the public RIRE database show the effectiveness of our method. Rigid registration accuracy measured at 10 clinical targets and compared to ground truth measurements yield a mean target registration error of 0.68mm(std=0.4mm) for CT-PD and 0.82mm(std=0.43mm) for CT-T2. This is 0.3mm (1mm) more accurate in the average (worst) case than five existing sampling methods. Our method has the lowest registration errors recorded to date for the registration of CT-PD and CT-T2 images in the RIRE website when compared to methods that were tested on at least three patient datasets.

  14. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    Science.gov (United States)

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  15. In Situ Casting and Imaging of the Rat Airway Tree for Accurate 3D Reconstruction

    Science.gov (United States)

    Jacob, Richard E.; Colby, Sean M.; Kabilan, Senthil; Einstein, Daniel R.; Carson, James P.

    2014-01-01

    The use of anatomically accurate, animal-specific airway geometries is important for understanding and modeling the physiology of the respiratory system. One approach for acquiring detailed airway architecture is to create a bronchial cast of the conducting airways. However, typical casting procedures either do not faithfully preserve the in vivo branching angles or produce rigid casts that when removed for imaging are fragile and thus easily damaged. We address these problems by creating an in situ bronchial cast of the conducting airways in rats that can be subsequently imaged in situ using 3D micro-CT imaging. We also demonstrate that deformations in airway branch angles resulting from the casting procedure are small, and that these angle deformations can be reversed through an interactive adjustment of the segmented cast geometry. Animal work was approved by the Institutional Animal Care and Use Committee of Pacific Northwest National Laboratory. PMID:23786464

  16. 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.

  17. Accurate load prediction by BEM with airfoil data from 3D RANS simulations

    Science.gov (United States)

    Schneider, Marc S.; Nitzsche, Jens; Hennings, Holger

    2016-09-01

    In this paper, two methods for the extraction of airfoil coefficients from 3D CFD simulations of a wind turbine rotor are investigated, and these coefficients are used to improve the load prediction of a BEM code. The coefficients are extracted from a number of steady RANS simulations, using either averaging of velocities in annular sections, or an inverse BEM approach for determination of the induction factors in the rotor plane. It is shown that these 3D rotor polars are able to capture the rotational augmentation at the inner part of the blade as well as the load reduction by 3D effects close to the blade tip. They are used as input to a simple BEM code and the results of this BEM with 3D rotor polars are compared to the predictions of BEM with 2D airfoil coefficients plus common empirical corrections for stall delay and tip loss. While BEM with 2D airfoil coefficients produces a very different radial distribution of loads than the RANS simulation, the BEM with 3D rotor polars manages to reproduce the loads from RANS very accurately for a variety of load cases, as long as the blade pitch angle is not too different from the cases from which the polars were extracted.

  18. A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging

    CERN Document Server

    Pinheiro, Manuel

    2015-01-01

    In this work it is proposed a medical image segmentation pipeline for accurate bone segmentation from CT imaging. It is a two-step methodology, with a pre-segmentation step and a segmentation refinement step. First, the user performs a rough segmenting of the desired region of interest. Next, a fully automatic refinement step is applied to the pre-segmented data. The automatic segmentation refinement is composed by several sub-stpng, namely image deconvolution, image cropping and interpolation. The user-defined pre-segmentation is then refined over the deconvolved, cropped, and up-sampled version of the image. The algorithm is applied in the segmentation of CT images of a composite femur bone, reconstructed with different reconstruction protocols. Segmentation outcomes are validated against a gold standard model obtained with coordinate measuring machine Nikon Metris LK V20 with a digital line scanner LC60-D that guarantees an accuracy of 28 $\\mu m$. High sub-pixel accuracy models were obtained for all tested...

  19. Accurate Permittivity Measurements for Microwave Imaging via Ultra-Wideband Removal of Spurious Reflectors

    Directory of Open Access Journals (Sweden)

    Mathew G. Pelletier

    2010-09-01

    Full Text Available The use of microwave imaging is becoming more prevalent for detection of interior hidden defects in manufactured and packaged materials. In applications for detection of hidden moisture, microwave tomography can be used to image the material and then perform an inverse calculation to derive an estimate of the variability of the hidden material, such internal moisture, thereby alerting personnel to damaging levels of the hidden moisture before material degradation occurs. One impediment to this type of imaging occurs with nearby objects create strong reflections that create destructive and constructive interference, at the receiver, as the material is conveyed past the imaging antenna array. In an effort to remove the influence of the reflectors, such as metal bale ties, research was conducted to develop an algorithm for removal of the influence of the local proximity reflectors from the microwave images. This research effort produced a technique, based upon the use of ultra-wideband signals, for the removal of spurious reflections created by local proximity reflectors. This improvement enables accurate microwave measurements of moisture in such products as cotton bales, as well as other physical properties such as density or material composition. The proposed algorithm was shown to reduce errors by a 4:1 ratio and is an enabling technology for imaging applications in the presence of metal bale ties.

  20. Noise-free accurate count of microbial colonies by time-lapse shadow image analysis.

    Science.gov (United States)

    Ogawa, Hiroyuki; Nasu, Senshi; Takeshige, Motomu; Funabashi, Hisakage; Saito, Mikako; Matsuoka, Hideaki

    2012-12-01

    Microbial colonies in food matrices could be counted accurately by a novel noise-free method based on time-lapse shadow image analysis. An agar plate containing many clusters of microbial colonies and/or meat fragments was trans-illuminated to project their 2-dimensional (2D) shadow images on a color CCD camera. The 2D shadow images of every cluster distributed within a 3-mm thick agar layer were captured in focus simultaneously by means of a multiple focusing system, and were then converted to 3-dimensional (3D) shadow images. By time-lapse analysis of the 3D shadow images, it was determined whether each cluster comprised single or multiple colonies or a meat fragment. The analytical precision was high enough to be able to distinguish a microbial colony from a meat fragment, to recognize an oval image as two colonies contacting each other, and to detect microbial colonies hidden under a food fragment. The detection of hidden colonies is its outstanding performance in comparison with other systems. The present system attained accuracy for counting fewer than 5 colonies and is therefore of practical importance.

  1. Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images.

    Science.gov (United States)

    Kang, Ho Chul; Choi, Chankyu; Shin, Juneseuk; Lee, Jeongjin; Shin, Yeong-Gil

    2015-01-01

    In this paper, we propose a fast and accurate semiautomatic method to effectively distinguish individual teeth from the sockets of teeth in dental CT images. Parameter values of thresholding and shapes of the teeth are propagated to the neighboring slice, based on the separated teeth from reference images. After the propagation of threshold values and shapes of the teeth, the histogram of the current slice was analyzed. The individual teeth are automatically separated and segmented by using seeded region growing. Then, the newly generated separation information is iteratively propagated to the neighboring slice. Our method was validated by ten sets of dental CT scans, and the results were compared with the manually segmented result and conventional methods. The average error of absolute value of volume measurement was 2.29 ± 0.56%, which was more accurate than conventional methods. Boosting up the speed with the multicore processors was shown to be 2.4 times faster than a single core processor. The proposed method identified the individual teeth accurately, demonstrating that it can give dentists substantial assistance during dental surgery.

  2. Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images

    Directory of Open Access Journals (Sweden)

    Ho Chul Kang

    2015-01-01

    Full Text Available DIn this paper, we propose a fast and accurate semiautomatic method to effectively distinguish individual teeth from the sockets of teeth in dental CT images. Parameter values of thresholding and shapes of the teeth are propagated to the neighboring slice, based on the separated teeth from reference images. After the propagation of threshold values and shapes of the teeth, the histogram of the current slice was analyzed. The individual teeth are automatically separated and segmented by using seeded region growing. Then, the newly generated separation information is iteratively propagated to the neighboring slice. Our method was validated by ten sets of dental CT scans, and the results were compared with the manually segmented result and conventional methods. The average error of absolute value of volume measurement was 2.29±0.56%, which was more accurate than conventional methods. Boosting up the speed with the multicore processors was shown to be 2.4 times faster than a single core processor. The proposed method identified the individual teeth accurately, demonstrating that it can give dentists substantial assistance during dental surgery.

  3. An accurate and practical method for inference of weak gravitational lensing from galaxy images

    Science.gov (United States)

    Bernstein, Gary M.; Armstrong, Robert; Krawiec, Christina; March, Marisa C.

    2016-07-01

    We demonstrate highly accurate recovery of weak gravitational lensing shear using an implementation of the Bayesian Fourier Domain (BFD) method proposed by Bernstein & Armstrong, extended to correct for selection biases. The BFD formalism is rigorously correct for Nyquist-sampled, background-limited, uncrowded images of background galaxies. BFD does not assign shapes to galaxies, instead compressing the pixel data D into a vector of moments M, such that we have an analytic expression for the probability P(M|g) of obtaining the observations with gravitational lensing distortion g along the line of sight. We implement an algorithm for conducting BFD's integrations over the population of unlensed source galaxies which measures ≈10 galaxies s-1 core-1 with good scaling properties. Initial tests of this code on ≈109 simulated lensed galaxy images recover the simulated shear to a fractional accuracy of m = (2.1 ± 0.4) × 10-3, substantially more accurate than has been demonstrated previously for any generally applicable method. Deep sky exposures generate a sufficiently accurate approximation to the noiseless, unlensed galaxy population distribution assumed as input to BFD. Potential extensions of the method include simultaneous measurement of magnification and shear; multiple-exposure, multiband observations; and joint inference of photometric redshifts and lensing tomography.

  4. Environmental impact prediction using remote sensing images

    Institute of Scientific and Technical Information of China (English)

    Pezhman ROUDGARMI; Masoud MONAVARI; Jahangir FEGHHI; Jafar NOURI; Nematollah KHORASANI

    2008-01-01

    Environmental impact prediction is an important step in many environmental studies. Awide variety of methods have been developed in this concern. During this study, remote sensing images were used for environmental impact prediction in Robatkarim area, Iran, during the years of 2005~2007. It was assumed that environmental impact could be predicted using time series satellite imageries. Natural vegetation cover was chosen as a main environmental element and a case study. Environmental impacts of the regional development on natural vegetation of the area were investigated considering the changes occurred on the extent of natural vegetation cover and the amount of biomass. Vegetation data, land use and land cover classes (as activity factors) within several years were prepared using satellite images. The amount ofbiomass was measured by Soil-adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) based on satellite images. The resulted biomass estimates were tested by the paired samples t-test method. No significant difference was observed between the average biomass of estimated and control samples at the 5% significance level. Finally, regression models were used for the environmental impacts prediction. All obtained regression models for prediction of impacts on natural vegetation cover show values over 0.9 for both correlation coefficient and R-squared. According to the resulted methodology, the prediction models of projects and plans impacts can also be developed for other environmental elements which may be derived using time series remote sensing images.

  5. Artificial neural networks accurately predict mortality in patients with nonvariceal upper GI bleeding.

    Science.gov (United States)

    Rotondano, Gianluca; Cipolletta, Livio; Grossi, Enzo; Koch, Maurizio; Intraligi, Marco; Buscema, Massimo; Marmo, Riccardo

    2011-02-01

    Risk stratification systems that accurately identify patients with a high risk for bleeding through the use of clinical predictors of mortality before endoscopic examination are needed. Computerized (artificial) neural networks (ANNs) are adaptive tools that may improve prognostication. To assess the capability of an ANN to predict mortality in patients with nonvariceal upper GI bleeding and compare the predictive performance of the ANN with that of the Rockall score. Prospective, multicenter study. Academic and community hospitals. This study involved 2380 patients with nonvariceal upper GI bleeding. Upper GI endoscopy. The primary outcome variable was 30-day mortality, defined as any death occurring within 30 days of the index bleeding episode. Other outcome variables were recurrent bleeding and need for surgery. We performed analysis of certified outcomes of 2380 patients with nonvariceal upper GI bleeding. The Rockall score was compared with a supervised ANN (TWIST system, Semeion), adopting the same result validation protocol with random allocation of the sample in training and testing subsets and subsequent crossover. Overall, death occurred in 112 cases (4.70%). Of 68 pre-endoscopic input variables, 17 were selected and used by the ANN versus 16 included in the Rockall score. The sensitivity of the ANN-based model was 83.8% (76.7-90.8) versus 71.4% (62.8-80.0) for the Rockall score. Specificity was 97.5 (96.8-98.2) and 52.0 (49.8 4.2), respectively. Accuracy was 96.8% (96.0-97.5) versus 52.9% (50.8-55.0) (Pbleeding and obscure GI hemorrhage are excluded. In patients with nonvariceal upper GI bleeding, ANNs are significantly superior to the Rockall score in predicting the risk of death. Copyright © 2011 American Society for Gastrointestinal Endoscopy. Published by Mosby, Inc. All rights reserved.

  6. A method to detect landmark pairs accurately between intra-patient volumetric medical images.

    Science.gov (United States)

    Yang, Deshan; Zhang, Miao; Chang, Xiao; Fu, Yabo; Liu, Shi; Li, Harold H; Mutic, Sasa; Duan, Ye

    2017-08-23

    An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration (DIR) methods quantitatively. Landmark detection and pair matching were implemented in a Gaussian pyramid multi-resolution scheme. A 3D scale-invariant feature transform (SIFT) feature detection method and a 3D Harris-Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi-Resolution Inverse-Consistent Guided Matching or MRICGM, was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency. The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter-phase abdomen 4DCT images of three patients, between the original and the re-scanned radiation therapy simulation CT images of two head-neck patients, and between inter-fractional treatment MRIs of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm. A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi-automatic way to generate the

  7. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    Science.gov (United States)

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior.

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

    Energy Technology Data Exchange (ETDEWEB)

    Wong, Sharon [National University of Singapore, Yong Loo Lin School of Medicine (Singapore); Back, Michael [Northern Sydney Cancer Centre, Royal North Shore Hospital, St Leonards, New South Wales (Australia); Tan, Poh Wee; Lee, Khai Mun; Baggarley, Shaun [National University, Cancer Institute, Department of Radiation Oncology, National University, Hospital, Tower Block (Singapore); Lu, Jaide Jay, E-mail: mdcljj@nus.edu.sg [National University of Singapore, Yong Loo Lin School of Medicine (Singapore); National University, Cancer Institute, Department of Radiation Oncology, National University, Hospital, Tower Block (Singapore)

    2012-07-01

    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 Registered-Sign 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.

  9. Computational Chemical Imaging for Cardiovascular Pathology: Chemical Microscopic Imaging Accurately Determines Cardiac Transplant Rejection

    Science.gov (United States)

    Tiwari, Saumya; Reddy, Vijaya B.; Bhargava, Rohit; Raman, Jaishankar

    2015-01-01

    Rejection is a common problem after cardiac transplants leading to significant number of adverse events and deaths, particularly in the first year of transplantation. The gold standard to identify rejection is endomyocardial biopsy. This technique is complex, cumbersome and requires a lot of expertise in the correct interpretation of stained biopsy sections. Traditional histopathology cannot be used actively or quickly during cardiac interventions or surgery. Our objective was to develop a stain-less approach using an emerging technology, Fourier transform infrared (FT-IR) spectroscopic imaging to identify different components of cardiac tissue by their chemical and molecular basis aided by computer recognition, rather than by visual examination using optical microscopy. We studied this technique in assessment of cardiac transplant rejection to evaluate efficacy in an example of complex cardiovascular pathology. We recorded data from human cardiac transplant patients’ biopsies, used a Bayesian classification protocol and developed a visualization scheme to observe chemical differences without the need of stains or human supervision. Using receiver operating characteristic curves, we observed probabilities of detection greater than 95% for four out of five histological classes at 10% probability of false alarm at the cellular level while correctly identifying samples with the hallmarks of the immune response in all cases. The efficacy of manual examination can be significantly increased by observing the inherent biochemical changes in tissues, which enables us to achieve greater diagnostic confidence in an automated, label-free manner. We developed a computational pathology system that gives high contrast images and seems superior to traditional staining procedures. This study is a prelude to the development of real time in situ imaging systems, which can assist interventionists and surgeons actively during procedures. PMID:25932912

  10. Computational chemical imaging for cardiovascular pathology: chemical microscopic imaging accurately determines cardiac transplant rejection.

    Directory of Open Access Journals (Sweden)

    Saumya Tiwari

    Full Text Available Rejection is a common problem after cardiac transplants leading to significant number of adverse events and deaths, particularly in the first year of transplantation. The gold standard to identify rejection is endomyocardial biopsy. This technique is complex, cumbersome and requires a lot of expertise in the correct interpretation of stained biopsy sections. Traditional histopathology cannot be used actively or quickly during cardiac interventions or surgery. Our objective was to develop a stain-less approach using an emerging technology, Fourier transform infrared (FT-IR spectroscopic imaging to identify different components of cardiac tissue by their chemical and molecular basis aided by computer recognition, rather than by visual examination using optical microscopy. We studied this technique in assessment of cardiac transplant rejection to evaluate efficacy in an example of complex cardiovascular pathology. We recorded data from human cardiac transplant patients' biopsies, used a Bayesian classification protocol and developed a visualization scheme to observe chemical differences without the need of stains or human supervision. Using receiver operating characteristic curves, we observed probabilities of detection greater than 95% for four out of five histological classes at 10% probability of false alarm at the cellular level while correctly identifying samples with the hallmarks of the immune response in all cases. The efficacy of manual examination can be significantly increased by observing the inherent biochemical changes in tissues, which enables us to achieve greater diagnostic confidence in an automated, label-free manner. We developed a computational pathology system that gives high contrast images and seems superior to traditional staining procedures. This study is a prelude to the development of real time in situ imaging systems, which can assist interventionists and surgeons actively during procedures.

  11. Hybrid Prediction and Fractal Hyperspectral Image Compression

    Directory of Open Access Journals (Sweden)

    Shiping Zhu

    2015-01-01

    Full Text Available The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.

  12. Predicting accurate fluorescent spectra for high molecular weight polycyclic aromatic hydrocarbons using density functional theory

    Science.gov (United States)

    Powell, Jacob; Heider, Emily C.; Campiglia, Andres; Harper, James K.

    2016-10-01

    The ability of density functional theory (DFT) methods to predict accurate fluorescence spectra for polycyclic aromatic hydrocarbons (PAHs) is explored. Two methods, PBE0 and CAM-B3LYP, are evaluated both in the gas phase and in solution. Spectra for several of the most toxic PAHs are predicted and compared to experiment, including three isomers of C24H14 and a PAH containing heteroatoms. Unusually high-resolution experimental spectra are obtained for comparison by analyzing each PAH at 4.2 K in an n-alkane matrix. All theoretical spectra visually conform to the profiles of the experimental data but are systematically offset by a small amount. Specifically, when solvent is included the PBE0 functional overestimates peaks by 16.1 ± 6.6 nm while CAM-B3LYP underestimates the same transitions by 14.5 ± 7.6 nm. These calculated spectra can be empirically corrected to decrease the uncertainties to 6.5 ± 5.1 and 5.7 ± 5.1 nm for the PBE0 and CAM-B3LYP methods, respectively. A comparison of computed spectra in the gas phase indicates that the inclusion of n-octane shifts peaks by +11 nm on average and this change is roughly equivalent for PBE0 and CAM-B3LYP. An automated approach for comparing spectra is also described that minimizes residuals between a given theoretical spectrum and all available experimental spectra. This approach identifies the correct spectrum in all cases and excludes approximately 80% of the incorrect spectra, demonstrating that an automated search of theoretical libraries of spectra may eventually become feasible.

  13. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy.

    Science.gov (United States)

    Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

    2015-06-16

    Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure-rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides.

  14. Efficient predictive algorithms for image compression

    CERN Document Server

    Rosário Lucas, Luís Filipe; Maciel de Faria, Sérgio Manuel; Morais Rodrigues, Nuno Miguel; Liberal Pagliari, Carla

    2017-01-01

    This book discusses efficient prediction techniques for the current state-of-the-art High Efficiency Video Coding (HEVC) standard, focusing on the compression of a wide range of video signals, such as 3D video, Light Fields and natural images. The authors begin with a review of the state-of-the-art predictive coding methods and compression technologies for both 2D and 3D multimedia contents, which provides a good starting point for new researchers in the field of image and video compression. New prediction techniques that go beyond the standardized compression technologies are then presented and discussed. In the context of 3D video, the authors describe a new predictive algorithm for the compression of depth maps, which combines intra-directional prediction, with flexible block partitioning and linear residue fitting. New approaches are described for the compression of Light Field and still images, which enforce sparsity constraints on linear models. The Locally Linear Embedding-based prediction method is in...

  15. Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning

    Directory of Open Access Journals (Sweden)

    Tanel Pärnamaa

    2017-05-01

    Full Text Available High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a task relatively simple for an experienced human, but difficult to automate on a computer. Here, we train an 11-layer neural network on data from mapping thousands of yeast proteins, achieving per cell localization classification accuracy of 91%, and per protein accuracy of 99% on held-out images. We confirm that low-level network features correspond to basic image characteristics, while deeper layers separate localization classes. Using this network as a feature calculator, we train standard classifiers that assign proteins to previously unseen compartments after observing only a small number of training examples. Our results are the most accurate subcellular localization classifications to date, and demonstrate the usefulness of deep learning for high-throughput microscopy.

  16. Accurate prediction of band gaps and optical properties of HfO2

    Science.gov (United States)

    Ondračka, Pavel; Holec, David; Nečas, David; Zajíčková, Lenka

    2016-10-01

    We report on optical properties of various polymorphs of hafnia predicted within the framework of density functional theory. The full potential linearised augmented plane wave method was employed together with the Tran-Blaha modified Becke-Johnson potential (TB-mBJ) for exchange and local density approximation for correlation. Unit cells of monoclinic, cubic and tetragonal crystalline, and a simulated annealing-based model of amorphous hafnia were fully relaxed with respect to internal positions and lattice parameters. Electronic structures and band gaps for monoclinic, cubic, tetragonal and amorphous hafnia were calculated using three different TB-mBJ parametrisations and the results were critically compared with the available experimental and theoretical reports. Conceptual differences between a straightforward comparison of experimental measurements to a calculated band gap on the one hand and to a whole electronic structure (density of electronic states) on the other hand, were pointed out, suggesting the latter should be used whenever possible. Finally, dielectric functions were calculated at two levels, using the random phase approximation without local field effects and with a more accurate Bethe-Salpether equation (BSE) to account for excitonic effects. We conclude that a satisfactory agreement with experimental data for HfO2 was obtained only in the latter case.

  17. Accurate prediction of DnaK-peptide binding via homology modelling and experimental data.

    Directory of Open Access Journals (Sweden)

    Joost Van Durme

    2009-08-01

    Full Text Available Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides, refolding and degradation of misfolded proteins, and activation of a wide range of client proteins. The prokaryotic heat-shock protein DnaK is the E. coli representative of the ubiquitous Hsp70 family, which specializes in the binding of exposed hydrophobic regions in unfolded polypeptides. Accurate prediction of DnaK binding sites in E. coli proteins is an essential prerequisite to understand the precise function of this chaperone and the properties of its substrate proteins. In order to map DnaK binding sites in protein sequences, we have developed an algorithm that combines sequence information from peptide binding experiments and structural parameters from homology modelling. We show that this combination significantly outperforms either single approach. The final predictor had a Matthews correlation coefficient (MCC of 0.819 when assessed over the 144 tested peptide sequences to detect true positives and true negatives. To test the robustness of the learning set, we have conducted a simulated cross-validation, where we omit sequences from the learning sets and calculate the rate of repredicting them. This resulted in a surprisingly good MCC of 0.703. The algorithm was also able to perform equally well on a blind test set of binders and non-binders, of which there was no prior knowledge in the learning sets. The algorithm is freely available at http://limbo.vib.be.

  18. ROCK I Has More Accurate Prognostic Value than MET in Predicting Patient Survival in Colorectal Cancer.

    Science.gov (United States)

    Li, Jian; Bharadwaj, Shruthi S; Guzman, Grace; Vishnubhotla, Ramana; Glover, Sarah C

    2015-06-01

    Colorectal cancer remains the second leading cause of death in the United States despite improvements in incidence rates and advancements in screening. The present study evaluated the prognostic value of two tumor markers, MET and ROCK I, which have been noted in other cancers to provide more accurate prognoses of patient outcomes than tumor staging alone. We constructed a tissue microarray from surgical specimens of adenocarcinomas from 108 colorectal cancer patients. Using immunohistochemistry, we examined the expression levels of tumor markers MET and ROCK I, with a pathologist blinded to patient identities and clinical outcomes providing the scoring of MET and ROCK I expression. We then used retrospective analysis of patients' survival data to provide correlations with expression levels of MET and ROCK I. Both MET and ROCK I were significantly over-expressed in colorectal cancer tissues, relative to the unaffected adjacent mucosa. Kaplan-Meier survival analysis revealed that patients' 5-year survival was inversely correlated with levels of expression of ROCK I. In contrast, MET was less strongly correlated with five-year survival. ROCK I provides better efficacy in predicting patient outcomes, compared to either tumor staging or MET expression. As a result, ROCK I may provide a less invasive method of assessing patient prognoses and directing therapeutic interventions. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

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

    Science.gov (United States)

    Cataneo, Matteo; Rapetti, David; Lombriser, Lucas; Li, Baojiu

    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 lesssim 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.

  20. Hindered rotor models with variable kinetic functions for accurate thermodynamic and kinetic predictions

    Science.gov (United States)

    Reinisch, Guillaume; Leyssale, Jean-Marc; Vignoles, Gérard L.

    2010-10-01

    We present an extension of some popular hindered rotor (HR) models, namely, the one-dimensional HR (1DHR) and the degenerated two-dimensional HR (d2DHR) models, allowing for a simple and accurate treatment of internal rotations. This extension, based on the use of a variable kinetic function in the Hamiltonian instead of a constant reduced moment of inertia, is extremely suitable in the case of rocking/wagging motions involved in dissociation or atom transfer reactions. The variable kinetic function is first introduced in the framework of a classical 1DHR model. Then, an effective temperature and potential dependent constant is proposed in the cases of quantum 1DHR and classical d2DHR models. These methods are finally applied to the atom transfer reaction SiCl3+BCl3→SiCl4+BCl2. We show, for this particular case, that a proper accounting of internal rotations greatly improves the accuracy of thermodynamic and kinetic predictions. Moreover, our results confirm (i) that using a suitably defined kinetic function appears to be very adapted to such problems; (ii) that the separability assumption of independent rotations seems justified; and (iii) that a quantum mechanical treatment is not a substantial improvement with respect to a classical one.

  1. Towards accurate cosmological predictions for rapidly oscillating scalar fields as dark matter

    CERN Document Server

    Ureña-López, L Arturo

    2015-01-01

    As we are entering the era of precision cosmology, it is necessary to count on accurate cosmological predictions from any proposed model of dark matter. In this paper we present a novel approach to the cosmological evolution of scalar fields that eases their analytic and numerical analysis at the background and at the linear order of perturbations. We apply the method to a scalar field endowed with a quadratic potential and revisit its properties as dark matter. Some of the results known in the literature are recovered, and a better understanding of the physical properties of the model is provided. It is shown that the Jeans wavenumber defined as $k_J = a \\sqrt{mH}$ is directly related to the suppression of linear perturbations at wavenumbers $k>k_J$. We also discuss some semi-analytical results that are well satisfied by the full numerical solutions obtained from an amended version of the CMB code CLASS. Finally we draw some of the implications that this new treatment of the equations of motion may have in t...

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

    CERN Document Server

    Cataneo, Matteo; Lombriser, Lucas; Li, Baojiu

    2016-01-01

    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 fractional enhancement of the $f(R)$ halo abundance with respect to that of General Relativity (GR) within a precision of $\\lesssim 5\\%$ 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 flexi...

  3. Implicit beliefs about ideal body image predict body image dissatisfaction

    OpenAIRE

    Heider, Niclas; Spruyt, Adriaan; De Houwer, Jan

    2015-01-01

    We examined whether implicit measures of actual and ideal body image can be used to predict body dissatisfaction in young female adults. Participants completed two Implicit Relational Assessment Procedures (IRAPs) to examine their implicit beliefs concerning actual (e.g., I am thin) and desired ideal body image (e.g., I want to be thin). Body dissatisfaction was examined via self-report questionnaires and rating scales. As expected, differences in body dissatisfaction exerted a differential i...

  4. Implicit Beliefs about Ideal Body Image Predict Body Image Dissatisfaction

    OpenAIRE

    Niclas eHeider; Adriaan eSpruyt; Jan eDe Houwer

    2015-01-01

    We examined whether implicit measures of actual and ideal body image can be used to predict body dissatisfaction in young female adults. Participants completed two Implicit Relational Assessment Procedures (IRAPs) to examine their implicit beliefs concerning actual (e.g., I am thin) and desired ideal body image (e.g., I want to be thin). Body dissatisfaction was examined via self-report questionnaires and rating scales. As expected, differences in body dissatisfaction exerted a differential i...

  5. Feasibility study for image-guided kidney surgery: Assessment of required intraoperative surface for accurate physical to image space registrations

    Science.gov (United States)

    Benincasa, Anne B.; Clements, Logan W.; Herrell, S. Duke; Galloway, Robert L.

    2008-01-01

    A notable complication of applying current image-guided surgery techniques of soft tissue to kidney resections (nephrectomies) is the limited field of view of the intraoperative kidney surface. This limited view constrains the ability to obtain a sufficiently geometrically descriptive surface for accurate surface-based registrations. The authors examined the effects of the limited view by using two orientations of a kidney phantom to model typical laparoscopic and open partial nephrectomy views. Point-based registrations, using either rigidly attached markers or anatomical landmarks as fiducials, served as initial alignments for surface-based registrations. Laser range scanner (LRS) obtained surfaces were registered to the phantom’s image surface using a rigid iterative closest point algorithm. Subsets of each orientation’s LRS surface were used in a robustness test to determine which parts of the surface yield the most accurate registrations. Results suggest that obtaining accurate registrations is a function of the percentage of the total surface and of geometric surface properties, such as curvature. Approximately 28% of the total surface is required regardless of the location of that surface subset. However, that percentage decreases when the surface subset contains information from opposite ends of the surface and∕or unique anatomical features, such as the renal artery and vein. PMID:18841875

  6. Ensuring convergence in total-variation-based reconstruction for accurate microcalcification imaging in breast X-ray CT

    CERN Document Server

    Jørgensen, Jakob H; Pan, Xiaochuan

    2011-01-01

    Breast X-ray CT imaging is being considered in screening as an extension to mammography. As a large fraction of the population will be exposed to radiation, low-dose imaging is essential. Iterative image reconstruction based on solving an optimization problem, such as Total-Variation minimization, shows potential for reconstruction from sparse-view data. For iterative methods it is important to ensure convergence to an accurate solution, since important image features, such as presence of microcalcifications indicating breast cancer, may not be visible in a non-converged reconstruction, and this can have clinical significance. To prevent excessively long computational times, which is a practical concern for the large image arrays in CT, it is desirable to keep the number of iterations low, while still ensuring a sufficiently accurate reconstruction for the specific imaging task. This motivates the study of accurate convergence criteria for iterative image reconstruction. In simulation studies with a realistic...

  7. Particle Image Velocimetry Measurements in Anatomically-Accurate Models of the Mammalian Nasal Cavity

    Science.gov (United States)

    Rumple, C.; Richter, J.; Craven, B. A.; Krane, M.

    2012-11-01

    A summary of the research being carried out by our multidisciplinary team to better understand the form and function of the nose in different mammalian species that include humans, carnivores, ungulates, rodents, and marine animals will be presented. The mammalian nose houses a convoluted airway labyrinth, where two hallmark features of mammals occur, endothermy and olfaction. Because of the complexity of the nasal cavity, the anatomy and function of these upper airways remain poorly understood in most mammals. However, recent advances in high-resolution medical imaging, computational modeling, and experimental flow measurement techniques are now permitting the study of airflow and respiratory and olfactory transport phenomena in anatomically-accurate reconstructions of the nasal cavity. Here, we focus on efforts to manufacture transparent, anatomically-accurate models for stereo particle image velocimetry (SPIV) measurements of nasal airflow. Challenges in the design and manufacture of index-matched anatomical models are addressed and preliminary SPIV measurements are presented. Such measurements will constitute a validation database for concurrent computational fluid dynamics (CFD) simulations of mammalian respiration and olfaction. Supported by the National Science Foundation.

  8. Accurate measurement of respiratory airway wall thickness in CT images using a signal restoration technique

    Science.gov (United States)

    Park, Sang Joon; Kim, Tae Jung; Kim, Kwang Gi; Lee, Sang Ho; Goo, Jin Mo; Kim, Jong Hyo

    2008-03-01

    Airway wall thickness (AWT) is an important bio-marker for evaluation of pulmonary diseases such as chronic bronchitis, bronchiectasis. While an image-based analysis of the airway tree can provide precise and valuable airway size information, quantitative measurement of AWT in Multidetector-Row Computed Tomography (MDCT) images involves various sources of error and uncertainty. So we have developed an accurate AWT measurement technique for small airways with three-dimensional (3-D) approach. To evaluate performance of these techniques, we used a set of acryl tube phantom was made to mimic small airways to have three different sizes of wall diameter (4.20, 1.79, 1.24 mm) and wall thickness (1.84, 1.22, 0.67 mm). The phantom was imaged with MDCT using standard reconstruction kernel (Sensation 16, Siemens, Erlangen). The pixel size was 0.488 mm × 0.488 mm × 0.75 mm in x, y, and z direction respectively. The images were magnified in 5 times using cubic B-spline interpolation, and line profiles were obtained for each tube. To recover faithful line profile from the blurred images, the line profiles were deconvolved with a point spread kernel of the MDCT which was estimated using the ideal tube profile and image line profile. The inner diameter, outer diameter, and wall thickness of each tube were obtained with full-width-half-maximum (FWHM) method for the line profiles before and after deconvolution processing. Results show that significant improvement was achieved over the conventional FWHM method in the measurement of AWT.

  9. Accurate and Simplified Prediction of AVF for Delay and Energy Efficient Cache Design

    Institute of Scientific and Technical Information of China (English)

    An-Guo Ma; Yu Cheng; Zuo-Cheng Xing

    2011-01-01

    With continuous technology scaling, on-chip structures are becoming more and more susceptible to soft errors. Architectural vulnerability factor (AVF) has been introduced to quantify the architectural vulnerability of on-chip structures to soft errors. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures (i.e., issue queue, reorder buffer). Cache is one of the most susceptible components to soft errors and is commonly protected with error correcting codes (ECC). However, protecting caches closer to the processor (i.e., L1 data cache (L1D)) using ECC could result in high overhead. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to over-protection. Therefore, designing AVF-aware ECC is attractive for designers to balance among performance, power and reliability for cache, especially at early design stage. In this paper, we improve the methodology of cache AVF computation and develop a new AVF estimation framework, soft error reliability analysis based on SimpleScalar. Then we characterize dynamic vulnerability behavior of L1D and detect the correlations between LID AVF and various performance metrics. We propose to employ Bayesian additive regression trees to accurately model the variation of L1D AVF and to quantitatively explain the important effects of several key performance metrics on L1D AVF. Then, we employ bump hunting technique to reduce the complexity of L1D AVF prediction and extract some simple selecting rules based on several key performance metrics, thus enabling a simplified and fast estimation of L1D AVF. Based on the simplified and fast estimation of L1D AVF, intervals of high L1D AVF can be identified online, enabling us to develop the AVF-aware ECC technique to reduce the overhead of ECC. Experimental results show that compared with traditional ECC technique

  10. Operational life prediction on gating image intensifier

    Science.gov (United States)

    Dong, Yu-hui; Shen, Zhi-guo; Li, Zhong-li

    2009-07-01

    Operational life is one of the important parameters to evaluate second and super second generation image intensifiers. It can be used not only to monitor manufacturing technique in product line, then the technology on photocathode processing, MCP degassing and MCP producing can be adjusted promptly, but also to eliminate the image intensifiers which have hidden risk on operational life as early as possible. Recently gating image intensifiers are used widely, method to estimate the operational life of gating image intensifier related to its practical operate mode and working condition need to be established urgently. The least square method to analyze the operational life test data in product line was introduced in this paper. Now the data can be analyzed with convenient statistic analyze function on Excel. Using "worksheet function" and "chart wizard" and "data analysis" on Excel to do the least square method calculation, spreadsheets are established to do complex data calculation with worksheet functions. Based on them, formulas to monitor the technology parameters were derived, and the conclusion that the operational life was only related to the decrease slope of photocathode exponential fit curve was made. The decrease slope of photocathode sensitivity exponential fit curve and the decrease percent of the exponential fit photocathode sensitivity can be used to evaluate the qualification of the operational life rapidly. The mathematic models for operational life prediction on image intensifier and gating image intensifier are established respectively based on the acceptable values of the decrease percent of the exponential fit photocathode sensitivity and the expecting signal to noise ratio. The equations predicting the operational life related to duty cycle and input light level on gating image intensifier were derived, and the relationship between them were discussed too. The theory foundation were made herein, so the user can select proper gating image

  11. Large-Scale Multi-Resolution Representations for Accurate Interactive Image and Volume Operations

    KAUST Repository

    Sicat, Ronell B.

    2015-11-25

    and voxel footprints in input images and volumes. We show that the continuous pdfs encoded in the sparse pdf map representation enable accurate multi-resolution non-linear image operations on gigapixel images. Similarly, we show that sparse pdf volumes enable more consistent multi-resolution volume rendering compared to standard approaches, on both artificial and real world large-scale volumes. The supplementary videos demonstrate our results. In the standard approach, users heavily rely on panning and zooming interactions to navigate the data within the limits of their display devices. However, panning across the whole spatial domain and zooming across all resolution levels of large-scale images to search for interesting regions is not practical. Assisted exploration techniques allow users to quickly narrow down millions to billions of possible regions to a more manageable number for further inspection. However, existing approaches are not fully user-driven because they typically already prescribe what being of interest means. To address this, we introduce the patch sets representation for large-scale images. Patches inside a patch set are grouped and encoded according to similarity via a permutohedral lattice (p-lattice) in a user-defined feature space. Fast set operations on p-lattices facilitate patch set queries that enable users to describe what is interesting. In addition, we introduce an exploration framework—GigaPatchExplorer—for patch set-based image exploration. We show that patch sets in our framework are useful for a variety of user-driven exploration tasks in gigapixel images and whole collections thereof.

  12. 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.

    2007-01-01

    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 neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing. PMID:17482218

  13. Measuring reflective-band imaging systems for performance prediction

    Science.gov (United States)

    Slonopas, Andre; Preece, Bradley L.; Haefner, David P.

    2017-05-01

    An objective performance of the reflective-band imaging systems is required in order to provide the warfighter with the right technology for a specific task. Various methods to measure and model performance in the visible (Vis) spectral regions have been proposed in the literature. This correspondence shows the influence of the spectral region averaging on the monochromatic modulation transfer function (MTF). This works unequivocally shows that the illumination source plays a crucial role in the accurate predictive analysis of the system performance. For accurate analysis the illumination sources need to be carefully considered for the atmospheric conditions. This work shows the possibility of using an LED configuration in the system performance analysis. Such configurations need rigorous calibration in order to become a valuable asset in system characterization.

  14. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

    Energy Technology Data Exchange (ETDEWEB)

    Gan, Yangzhou; Zhao, Qunfei [Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240 (China); Xia, Zeyang, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn; Hu, Ying [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and The Chinese University of Hong Kong, Shenzhen 518055 (China); Xiong, Jing, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 510855 (China); Zhang, Jianwei [TAMS, Department of Informatics, University of Hamburg, Hamburg 22527 (Germany)

    2015-01-15

    Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm{sup 3}) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm{sup 3}, 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm{sup 3}, 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0

  15. Accurate representation of interference colours (Michel-Lévy chart): from rendering to image colour correction.

    Science.gov (United States)

    Linge Johnsen, S A; Bollmann, J; Lee, H W; Zhou, Y

    2017-09-21

    Here a work flow towards an accurate representation of interference colours (Michel-Lévy chart) digitally captured on a polarised light microscope using dry and oil immersion objectives is presented. The work flow includes accurate rendering of interference colours considering the colour temperature of the light source of the microscope and chromatic adaptation to white points of RGB colour spaces as well as the colour correction of the camera using readily available colour targets. The quality of different colour correction profiles was tested independently on an IT8.7/1 target. The best performing profile was using the XYZ cLUT algorithm and it revealed a ΔE00 of 1.9 (6.4 no profile) at 5× and 1.1 (8.4 no profile) at 100× magnification, respectively. The overall performance of the workflow was tested by comparing rendered interference colours with colour-corrected images of a quartz wedge captured over a retardation range from 80-2500 nm at 5× magnification. Uncorrected images of the quartz wedge in sRGB colour space revealed a mean ΔE00 of 12.3, which could be reduced to a mean of 4.9 by applying a camera correction profile based on an IT8.7/1 target and the Matrix only algorithm (ΔE00 < 1.0 signifies colour differences imperceptible by the human eye). ΔE00 varied significantly over the retardation range of 80-2500 nm of the quartz wedge, but the reasons for this variation is not well understood and the quality of colour correction might be further improved in future by using custom made colour targets specifically designed for the analysis of high-order interference colours. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

  16. Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks.

    Science.gov (United States)

    Schlegl, Thomas; Waldstein, Sebastian M; Vogl, Wolf-Dieter; Schmidt-Erfurth, Ursula; Langs, Georg

    2015-01-01

    Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount of knowledge encoded in imaging data and corresponding reports generated during clinical routine. Weakly supervised learning approaches can link volume-level labels to image content but suffer from the typical label distributions in medical imaging data where only a small part consists of clinically relevant abnormal structures. In this paper we propose to use a semantic representation of clinical reports as a learning target that is predicted from imaging data by a convolutional neural network. We demonstrate how we can learn accurate voxel-level classifiers based on weak volume-level semantic descriptions on a set of 157 optical coherence tomography (OCT) volumes. We specifically show how semantic information increases classification accuracy for intraretinal cystoid fluid (IRC), subretinal fluid (SRF) and normal retinal tissue, and how the learning algorithm links semantic concepts to image content and geometry.

  17. Adaptive Super-Spatial Prediction Approach For Lossless Image Compression

    Directory of Open Access Journals (Sweden)

    Arpita C. Raut,

    2014-04-01

    Full Text Available Existing prediction based lossless image compression schemes perform prediction of an image data using their spatial neighborhood technique which can’t predict high-frequency image structure components, such as edges, patterns, and textures very well which will limit the image compression efficiency. To exploit these structure components, adaptive super-spatial prediction approach is developed. The super-spatial prediction approach is adaptive to compress high frequency structure components from the grayscale image. The motivation behind the proposed prediction approach is taken from motion prediction in video coding, which attempts to find an optimal prediction of structure components within the previously encoded image regions. This prediction approach is efficient for image regions with significant structure components with respect to parameters as compression ratio, bit rate as compared to CALIC (Context-based adaptive lossless image coding.

  18. Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

    Directory of Open Access Journals (Sweden)

    Hui Feng

    2017-07-01

    Full Text Available Pigments absorb light, transform it into energy, and provide reaction sites for photosynthesis; thus, the quantification of pigment distribution is vital to plant research. Traditional methods for the quantification of pigments are time-consuming and not suitable for the high-throughput digitization of rice pigment distribution. In this study, using a hyperspectral imaging system, we developed an integrated image analysis pipeline for automatically processing enormous amounts of hyperspectral data. We also built models for accurately quantifying 4 pigments (chlorophyll a, chlorophyll b, total chlorophyll and carotenoid from rice leaves and determined the important bands (700-760 nm associated with these pigments. At the tillering stage, the R2 values and mean absolute percentage errors of the models were 0.827–0.928 and 6.94–12.84%, respectively. The hyperspectral data and these models can be combined for digitizing the distribution of the chlorophyll with high resolution (0.11 mm/pixel. In summary, the integrated hyperspectral image analysis pipeline and selected models can be used to quantify the chlorophyll distribution in rice leaves. The use of this technique will benefit rice functional genomics and rice breeding.

  19. Accurate 3D kinematic measurement of temporomandibular joint using X-ray fluoroscopic images

    Science.gov (United States)

    Yamazaki, Takaharu; Matsumoto, Akiko; Sugamoto, Kazuomi; Matsumoto, Ken; Kakimoto, Naoya; Yura, Yoshiaki

    2014-04-01

    Accurate measurement and analysis of 3D kinematics of temporomandibular joint (TMJ) is very important for assisting clinical diagnosis and treatment of prosthodontics and orthodontics, and oral surgery. This study presents a new 3D kinematic measurement technique of the TMJ using X-ray fluoroscopic images, which can easily obtain the TMJ kinematic data in natural motion. In vivo kinematics of the TMJ (maxilla and mandibular bone) is determined using a feature-based 2D/3D registration, which uses beads silhouette on fluoroscopic images and 3D surface bone models with beads. The 3D surface models of maxilla and mandibular bone with beads were created from CT scans data of the subject using the mouthpiece with the seven strategically placed beads. In order to validate the accuracy of pose estimation for the maxilla and mandibular bone, computer simulation test was performed using five patterns of synthetic tantalum beads silhouette images. In the clinical applications, dynamic movement during jaw opening and closing was conducted, and the relative pose of the mandibular bone with respect to the maxilla bone was determined. The results of computer simulation test showed that the root mean square errors were sufficiently smaller than 1.0 mm and 1.0 degree. In the results of clinical application, during jaw opening from 0.0 to 36.8 degree of rotation, mandibular condyle exhibited 19.8 mm of anterior sliding relative to maxillary articular fossa, and these measurement values were clinically similar to the previous reports. Consequently, present technique was thought to be suitable for the 3D TMJ kinematic analysis.

  20. Towards more accurate wind and solar power prediction by improving NWP model physics

    Science.gov (United States)

    Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

    2014-05-01

    The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during

  1. Ensuring convergence in total-variation-based reconstruction for accurate microcalcification imaging in breast X-ray CT

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Heide; Sidky, Emil Y.; Pan, Xiaochuan

    2011-01-01

    , shows potential for reconstruction from sparse-view data. For iterative methods it is important to ensure convergence to an accurate solution, since important diagnostic image features, such as presence of microcalcifications indicating breast cancer, may not be visible in a non-converged reconstruction......, and this can have clinical significance. To prevent excessively long computational times, which is a practical concern for the large image arrays in CT, it is desirable to keep the number of iterations low, while still ensuring a sufficiently accurate reconstruction for the specific imaging task....... This motivates the study of accurate convergence criteria for iterative image reconstruction. In simulation studies with a realistic breast phantom with microcalcifications we investigate the issue of ensuring sufficiently converged solution for reliable reconstruction. Our results show that it can...

  2. Image Capture with Synchronized Multiple-Cameras for Extraction of Accurate Geometries

    Science.gov (United States)

    Koehl, M.; Delacourt, T.; Boutry, C.

    2016-06-01

    This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing GoPro Hero4 cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (Faro Focus 3D) to allow the accuracy assessment.

  3. CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks

    Directory of Open Access Journals (Sweden)

    Kinjo Akira R

    2006-09-01

    Full Text Available Abstract Background One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes. Results We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, Q3 = 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively. Conclusion CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.

  4. 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

    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 essen...

  5. Improved Ecosystem Predictions of the California Current System via Accurate Light Calculations

    Science.gov (United States)

    2011-09-30

    absorption , scatter, and backscatter coefficients) effects. However, once an accurate value of the scalar irradiance Eo(z,λ) has been computed to... photosynthesis . It is possible to compute PAR to the bottom of the euphotic zone in a fraction of a second of computer time, with errors of no more than a few

  6. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.

    Directory of Open Access Journals (Sweden)

    Tatjana Braun

    2015-12-01

    Full Text Available Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.

  7. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

    Science.gov (United States)

    Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

    2016-03-01

    Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

  8. Enabling Computational Technologies for the Accurate Prediction/Description of Molecular Interactions in Condensed Phases

    Science.gov (United States)

    2014-10-08

    models to compute accurately the molecular interactions between a mobile or stationary phase and a target substrate or analyte , which are fundamental...mobile or stationary phase and a target substrate or analyte , which are fundamental to diverse technologies, e.g., sensor or separation design. With...D. G., New Orleans, LA, April 9, 2013. 223rd Electrochemical Society Meeting, Continuum Solvation Models for Computational Electrochemistry

  9. Accurate Prediction of the Ammonia Probes of a Variable Proton-to-Electron Mass Ratio

    CERN Document Server

    Owens, Alec; Thiel, Walter; Špirko, Vladimir

    2015-01-01

    A comprehensive study of the mass sensitivity of the vibration-rotation-inversion transitions of $^{14}$NH$_3$, $^{15}$NH$_3$, $^{14}$ND$_3$, and $^{15}$ND$_3$ is carried out variationally using the TROVE approach. Variational calculations are robust and accurate, offering a new way to compute sensitivity coefficients. Particular attention is paid to the $\\Delta k=\\pm 3$ transitions between the accidentally coinciding rotation-inversion energy levels of the $\

  10. Lipid fingerprint image accurately conveys human colon cell pathophysiologic state: A solid candidate as biomarker.

    Science.gov (United States)

    Bestard-Escalas, Joan; Garate, Jone; Maimó-Barceló, Albert; Fernández, Roberto; Lopez, Daniel Horacio; Lage, Sergio; Reigada, Rebeca; Khorrami, Sam; Ginard, Daniel; Reyes, José; Amengual, Isabel; Fernández, José A; Barceló-Coblijn, Gwendolyn

    2016-12-01

    Membrane lipids are gaining increasing attention in the clinical biomarker field, as they are associated with different pathologic processes such as cancer or neurodegenerative diseases. Analyzing human colonoscopic sections by matrix assisted laser/desorption ionization (MALDI) mass spectrometry imaging techniques, we identified a defined number of lipid species changing concomitant to the colonocyte differentiation and according to a quite simple mathematical expression. These species felt into two lipid families tightly associated in signaling: phosphatidylinositols and arachidonic acid-containing lipids. On the other hand, an opposed pattern was observed in lamina propria for AA-containing lipids, coinciding with the physiological distribution of the immunological response cells in this tissue. Importantly, the lipid gradient was accompanied by a gradient in expression of enzymes involved in lipid mobilization. Finally, both lipid and protein gradients were lost in adenomatous polyps. The latter allowed us to assess how different a single lipid species is handled in a pathological context depending on the cell type. The strict patterns of distribution in lipid species and lipid enzymes described here unveil the existence of fine regulatory mechanisms orchestrating the lipidome according to the physiological state of the cell. In addition, these results provide solid evidence that the cell lipid fingerprint image can be used to predict precisely the physiological and pathological status of a cell, reinforcing its translational impact in clinical research.

  11. Complete Soil Texture is Accurately Predicted by Visible Near-Infrared Spectroscopy

    DEFF Research Database (Denmark)

    Hermansen, Cecilie; Knadel, Maria; Møldrup, Per

    2017-01-01

    Core Ideas: Two PSC models are fitted to detailed measurements of clay, silt, and sand fractions.Both models well describe the PSCs of a broad soil data base.Within and between field variations in PSC and OM are well predicted by vis-NIRS.The Fredlund model performs slightly better in data-fittin......-fitting and vis-NIRS predicted PSCs.New vis-NIRS concept enables soil type classification in any texture system worldwide....

  12. 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

  13. Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques.

    Science.gov (United States)

    Franklin, Jessica M; Shrank, William H; Lii, Joyce; Krumme, Alexis K; Matlin, Olga S; Brennan, Troyen A; Choudhry, Niteesh K

    2016-02-01

    Despite the proliferation of databases with increasingly rich patient data, prediction of medication adherence remains poor. We proposed and evaluated approaches for improved adherence prediction. We identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin. A total of 643 variables were identified at baseline from prior claims and linked Census data. In addition, we identified three postbaseline predictors, indicators of adherence to statins during each of the first 3 months of follow-up. We estimated 10 models predicting subsequent adherence, using logistic regression and boosted logistic regression, a nonparametric data-mining technique. Models were also estimated within strata defined by the index days supply. In 77,703 statin initiators, prediction using baseline variables only was poor with maximum cross-validated C-statistics of 0.606 and 0.577 among patients with index supply ≤30 days and >30 days, respectively. Using only indicators of initial statin adherence improved prediction accuracy substantially among patients with shorter initial dispensings (C = 0.827/0.518), and, when combined with investigator-specified variables, prediction accuracy was further improved (C = 0.842/0.596). Observed adherence immediately after initiation predicted future adherence for patients whose initial dispensings were relatively short. © Health Research and Educational Trust.

  14. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Dyar, M. Darby; McCanta, Molly; Breves, Elly; Carey, C. J.; Lanzirotti, Antonio

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.

  15. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Dyar, M. Darby; McCanta, Molly; Breves, Elly; Carey, C. J.; Lanzirotti, Antonio

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.

  16. Implicit Beliefs about Ideal Body Image Predict Body Image Dissatisfaction

    Directory of Open Access Journals (Sweden)

    Niclas eHeider

    2015-10-01

    Full Text Available We examined whether implicit measures of actual and ideal body image can be used to predict body dissatisfaction in young female adults. Participants completed two Implicit Relational Assessment Procedures (IRAPs to examine their implicit beliefs concerning actual (e.g., I am thin and desired ideal body image (e.g., I want to be thin. Body dissatisfaction was examined via self-report questionnaires and rating scales. As expected, differences in body dissatisfaction exerted a differential influence on the two IRAP scores. Specifically, the implicit belief that one is thin was lower in participants who exhibited a high degree of body dissatisfaction than in participants who exhibited a low degree of body dissatisfaction. In contrast, the implicit desire to be thin (i.e., thin ideal body image was stronger in participants who exhibited a high level of body dissatisfaction than in participants who were less dissatisfied with their body. Adding further weight to the idea that both IRAP measures captured different underlying constructs, we also observed that they correlated differently with body mass index, explicit body dissatisfaction, and explicit measures of actual and ideal body image. More generally, these findings underscore the advantage of using implicit measures that incorporate relational information relative to implicit measures that allow for an assessment of associative relations only.

  17. Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

    Science.gov (United States)

    Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

    2011-12-01

    Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy.

  18. Automatic lung segmentation in CT images with accurate handling of the hilar region.

    Science.gov (United States)

    De Nunzio, Giorgio; Tommasi, Eleonora; Agrusti, Antonella; Cataldo, Rosella; De Mitri, Ivan; Favetta, Marco; Maglio, Silvio; Massafra, Andrea; Quarta, Maurizio; Torsello, Massimo; Zecca, Ilaria; Bellotti, Roberto; Tangaro, Sabina; Calvini, Piero; Camarlinghi, Niccolò; Falaschi, Fabio; Cerello, Piergiorgio; Oliva, Piernicola

    2011-02-01

    A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent 'fusion' between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the

  19. An automatic and accurate method of full heart segmentation from CT image based on linear gradient model

    Science.gov (United States)

    Yang, Zili

    2017-07-01

    Heart segmentation is an important auxiliary method in the diagnosis of many heart diseases, such as coronary heart disease and atrial fibrillation, and in the planning of tumor radiotherapy. Most of the existing methods for full heart segmentation treat the heart as a whole part and cannot accurately extract the bottom of the heart. In this paper, we propose a new method based on linear gradient model to segment the whole heart from the CT images automatically and accurately. Twelve cases were tested in order to test this method and accurate segmentation results were achieved and identified by clinical experts. The results can provide reliable clinical support.

  20. Hybrid exchange-correlation functional for accurate prediction of the electronic and structural properties of ferroelectric oxides

    OpenAIRE

    D., I. Bilc; R., Orlando; R., Shaltaf; G., M. Rignanese; J., Íñiguez; Ph., Ghosez

    2008-01-01

    Using a linear combination of atomic orbitals approach, we report a systematic comparison of various Density Functional Theory (DFT) and hybrid exchange-correlation functionals for the prediction of the electronic and structural properties of prototypical ferroelectric oxides. It is found that none of the available functionals is able to provide, at the same time, accurate electronic and structural properties of the cubic and tetragonal phases of BaTiO$_3$ and PbTiO$_3$. Some, although not al...

  1. An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

    Science.gov (United States)

    Koot, Yvonne E. M.; van Hooff, Sander R.; Boomsma, Carolien M.; van Leenen, Dik; Groot Koerkamp, Marian J. A.; Goddijn, Mariëtte; Eijkemans, Marinus J. C.; Fauser, Bart C. J. M.; Holstege, Frank C. P.; Macklon, Nick S.

    2016-01-01

    The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention. PMID:26797113

  2. Empirical approaches to more accurately predict benthic-pelagic coupling in biogeochemical ocean models

    Science.gov (United States)

    Dale, Andy; Stolpovsky, Konstantin; Wallmann, Klaus

    2016-04-01

    The recycling and burial of biogenic material in the sea floor plays a key role in the regulation of ocean chemistry. Proper consideration of these processes in ocean biogeochemical models is becoming increasingly recognized as an important step in model validation and prediction. However, the rate of organic matter remineralization in sediments and the benthic flux of redox-sensitive elements are difficult to predict a priori. In this communication, examples of empirical benthic flux models that can be coupled to earth system models to predict sediment-water exchange in the open ocean are presented. Large uncertainties hindering further progress in this field include knowledge of the reactivity of organic carbon reaching the sediment, the importance of episodic variability in bottom water chemistry and particle rain rates (for both the deep-sea and margins) and the role of benthic fauna. How do we meet the challenge?

  3. A random protein-creatinine ratio accurately predicts baseline proteinuria in early pregnancy.

    Science.gov (United States)

    Hirshberg, Adi; Draper, Jennifer; Curley, Cara; Sammel, Mary D; Schwartz, Nadav

    2014-12-01

    Data surrounding the use of a random urine protein:creatinine ratio (PCR) in the diagnosis of preeclampsia is conflicting. We sought to determine whether PCR in early pregnancy can replace the 24-hour urine collection as the primary screening test in patients at risk for baseline proteinuria. Women requiring a baseline evaluation for proteinuria supplied a urine sample the morning after their 24-hour collection. The PCR was analyzed as a predictor of significant proteinuria (≥150 mg). A regression equation to estimate the 24-hour protein value from the PCR was then developed. Sixty of 135 subjects enrolled completed the study. The median 24-hour urine protein and PCR were 90 mg (IQR: 50-145) and 0.063 (IQR: 0.039-0.083), respectively. Fifteen patients (25%) had significant proteinuria. PCR was strongly correlated with the 24-hour protein value (r = 0.99, p proteinuria (AUC = 0.86). A PCR cut-point of 0.079 yielded a sensitivity of 93.3% and a specificity of 57.8%. The resulting regression equation [total protein = 46.5 + 904.2*PCR] accurately estimates the actual 24-hour protein (95% CI: ±88 mg). A random urine PCR accurately estimates the 24-hour protein excretion in the first half of pregnancy and can be used as the primary screening test for baseline proteinuria in at-risk patients.

  4. Achieving accurate and efficient prediction of HVAC diaphragm noise at realistic Reynolds and Mach numbers

    NARCIS (Netherlands)

    Guilloud, G.; Schram, C.; Golliard, J.

    2009-01-01

    Despite the aeroacoustic expertise reached nowadays in air and ground transportation, energy sector or domestic appliances, reaching a decibel accuracy of an acoustic prediction for industrial cases is still challenging. Strong investments are made nowadays by oil and gas companies to determine and

  5. Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising

    CERN Document Server

    Donoho, David; Montanari, Andrea

    2011-01-01

    Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula precisely delineating the allowable degree of of undersampling of generalized sparse objects. The formula applies to Approximate Message Passing (AMP) algorithms for compressed sensing, which are here generalized to employ denoising operators besides the traditional scalar shrinkers (soft thresholding, positive soft thresholding and capping). This paper gives several examples including scalar shrinkers not derivable from convex optimization -- the firm shrinkage nonlinearity and the minimax} nonlinearity -- and also nonscalar denoisers -- block thresholding (both block soft and block James-Stein), monotone regression, and total variation minimization. Let the variables \\epsilon = k/N and \\delta = n/N denote the generalized sparsity and undersampling fractions for sampling the k-gene...

  6. Are predictive equations for estimating resting energy expenditure accurate in Asian Indian male weightlifters?

    Directory of Open Access Journals (Sweden)

    Mini Joseph

    2017-01-01

    Full Text Available Background: The accuracy of existing predictive equations to determine the resting energy expenditure (REE of professional weightlifters remains scarcely studied. Our study aimed at assessing the REE of male Asian Indian weightlifters with indirect calorimetry and to compare the measured REE (mREE with published equations. A new equation using potential anthropometric variables to predict REE was also evaluated. Materials and Methods: REE was measured on 30 male professional weightlifters aged between 17 and 28 years using indirect calorimetry and compared with the eight formulas predicted by Harris–Benedicts, Mifflin-St. Jeor, FAO/WHO/UNU, ICMR, Cunninghams, Owen, Katch-McArdle, and Nelson. Pearson correlation coefficient, intraclass correlation coefficient, and multiple linear regression analysis were carried out to study the agreement between the different methods, association with anthropometric variables, and to formulate a new prediction equation for this population. Results: Pearson correlation coefficients between mREE and the anthropometric variables showed positive significance with suprailiac skinfold thickness, lean body mass (LBM, waist circumference, hip circumference, bone mineral mass, and body mass. All eight predictive equations underestimated the REE of the weightlifters when compared with the mREE. The highest mean difference was 636 kcal/day (Owen, 1986 and the lowest difference was 375 kcal/day (Cunninghams, 1980. Multiple linear regression done stepwise showed that LBM was the only significant determinant of REE in this group of sportspersons. A new equation using LBM as the independent variable for calculating REE was computed. REE for weightlifters = −164.065 + 0.039 (LBM (confidence interval −1122.984, 794.854]. This new equation reduced the mean difference with mREE by 2.36 + 369.15 kcal/day (standard error = 67.40. Conclusion: The significant finding of this study was that all the prediction equations

  7. Safe surgery: how accurate are we at predicting intra-operative blood loss?

    LENUS (Irish Health Repository)

    2012-02-01

    Introduction Preoperative estimation of intra-operative blood loss by both anaesthetist and operating surgeon is a criterion of the World Health Organization\\'s surgical safety checklist. The checklist requires specific preoperative planning when anticipated blood loss is greater than 500 mL. The aim of this study was to assess the accuracy of surgeons and anaesthetists at predicting intra-operative blood loss. Methods A 6-week prospective study of intermediate and major operations in an academic medical centre was performed. An independent observer interviewed surgical and anaesthetic consultants and registrars, preoperatively asking each to predict expected blood loss in millilitre. Intra-operative blood loss was measured and compared with these predictions. Parameters including the use of anticoagulation and anti-platelet therapy as well as intra-operative hypothermia and hypotension were recorded. Results One hundred sixty-eight operations were included in the study, including 142 elective and 26 emergency operations. Blood loss was predicted to within 500 mL of measured blood loss in 89% of cases. Consultant surgeons tended to underestimate blood loss, doing so in 43% of all cases, while consultant anaesthetists were more likely to overestimate (60% of all operations). Twelve patients (7%) had underestimation of blood loss of more than 500 mL by both surgeon and anaesthetist. Thirty per cent (n = 6\\/20) of patients requiring transfusion of a blood product within 24 hours of surgery had blood loss underestimated by more than 500 mL by both surgeon and anaesthetist. There was no significant difference in prediction between patients on anti-platelet or anticoagulation therapy preoperatively and those not on the said therapies. Conclusion Predicted intra-operative blood loss was within 500 mL of measured blood loss in 89% of operations. In 30% of patients who ultimately receive a blood transfusion, both the surgeon and anaesthetist significantly underestimate

  8. FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues.

    Directory of Open Access Journals (Sweden)

    Yasser El-Manzalawy

    Full Text Available A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles. Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein

  9. Revisiting the blind tests in crystal structure prediction: accurate energy ranking of molecular crystals.

    Science.gov (United States)

    Asmadi, Aldi; Neumann, Marcus A; Kendrick, John; Girard, Pascale; Perrin, Marc-Antoine; Leusen, Frank J J

    2009-12-24

    In the 2007 blind test of crystal structure prediction hosted by the Cambridge Crystallographic Data Centre (CCDC), a hybrid DFT/MM method correctly ranked each of the four experimental structures as having the lowest lattice energy of all the crystal structures predicted for each molecule. The work presented here further validates this hybrid method by optimizing the crystal structures (experimental and submitted) of the first three CCDC blind tests held in 1999, 2001, and 2004. Except for the crystal structures of compound IX, all structures were reminimized and ranked according to their lattice energies. The hybrid method computes the lattice energy of a crystal structure as the sum of the DFT total energy and a van der Waals (dispersion) energy correction. Considering all four blind tests, the crystal structure with the lowest lattice energy corresponds to the experimentally observed structure for 12 out of 14 molecules. Moreover, good geometrical agreement is observed between the structures determined by the hybrid method and those measured experimentally. In comparison with the correct submissions made by the blind test participants, all hybrid optimized crystal structures (apart from compound II) have the smallest calculated root mean squared deviations from the experimentally observed structures. It is predicted that a new polymorph of compound V exists under pressure.

  10. Accurate single-sequence prediction of solvent accessible surface area using local and global features.

    Science.gov (United States)

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-11-01

    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org.

  11. Accurate structure prediction of peptide–MHC complexes for identifying highly immunogenic antigens

    Energy Technology Data Exchange (ETDEWEB)

    Park, Min-Sun; Park, Sung Yong; Miller, Keith R.; Collins, Edward J.; Lee, Ha Youn

    2013-11-01

    Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide–MHC complex. Here, we present an in silico protocol for predicting peptide–MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformational changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide–MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.

  12. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

    Directory of Open Access Journals (Sweden)

    Josué Pagán

    2015-06-01

    Full Text Available Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN. The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID that are capable of providing average forecast windows of 47 min and a low rate of false positives.

  13. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

    Science.gov (United States)

    Pagán, Josué; Irene De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L.; Vivancos Mora, J.; Moya, José M.; Ayala, José L.

    2015-01-01

    Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

  14. Accurate single-sequence prediction of solvent accessible surface area using local and global features

    Science.gov (United States)

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-01-01

    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org. PMID:25204636

  15. Correlative imaging across microscopy platforms using the fast and accurate relocation of microscopic experimental regions (FARMER) method

    Science.gov (United States)

    Huynh, Toan; Daddysman, Matthew K.; Bao, Ying; Selewa, Alan; Kuznetsov, Andrey; Philipson, Louis H.; Scherer, Norbert F.

    2017-05-01

    Imaging specific regions of interest (ROIs) of nanomaterials or biological samples with different imaging modalities (e.g., light and electron microscopy) or at subsequent time points (e.g., before and after off-microscope procedures) requires relocating the ROIs. Unfortunately, relocation is typically difficult and very time consuming to achieve. Previously developed techniques involve the fabrication of arrays of features, the procedures for which are complex, and the added features can interfere with imaging the ROIs. We report the Fast and Accurate Relocation of Microscopic Experimental Regions (FARMER) method, which only requires determining the coordinates of 3 (or more) conspicuous reference points (REFs) and employs an algorithm based on geometric operators to relocate ROIs in subsequent imaging sessions. The 3 REFs can be quickly added to various regions of a sample using simple tools (e.g., permanent markers or conductive pens) and do not interfere with the ROIs. The coordinates of the REFs and the ROIs are obtained in the first imaging session (on a particular microscope platform) using an accurate and precise encoded motorized stage. In subsequent imaging sessions, the FARMER algorithm finds the new coordinates of the ROIs (on the same or different platforms), using the coordinates of the manually located REFs and the previously recorded coordinates. FARMER is convenient, fast (3-15 min/session, at least 10-fold faster than manual searches), accurate (4.4 μm average error on a microscope with a 100x objective), and precise (almost all errors are diverse set of samples and imaging methods: live mammalian cells at different time points; fixed bacterial cells on two microscopes with different imaging modalities; and nanostructures on optical and electron microscopes. FARMER can be readily adapted to any imaging system with an encoded motorized stage and can facilitate multi-session and multi-platform imaging experiments in biology, materials science

  16. Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

    Science.gov (United States)

    Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

    2014-07-01

    With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions.

  17. 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

    as the original MMSE in predicting dementia. STUDY DESIGN AND SETTING: A population-based post hoc examination of the performance characteristics of the MMSE for detecting dementia in an existing data set of 243 elderly persons. RESULTS: Sensitivity, specificity, and predictive values were computed......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.......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...

  18. Pneumococcal pneumonia - Are the new severity scores more accurate in predicting adverse outcomes?

    Science.gov (United States)

    Ribeiro, C; Ladeira, I; Gaio, A R; Brito, M C

    2013-01-01

    The site-of-care decision is one of the most important factors in the management of patients with community-acquired pneumonia. The severity scores are validated prognostic tools for community-acquired pneumonia mortality and treatment site decision. The aim of this paper was to compare the discriminatory power of four scores - the classic PSI and CURB65 ant the most recent SCAP and SMART-COP - in predicting major adverse events: death, ICU admission, need for invasive mechanical ventilation or vasopressor support in patients admitted with pneumococcal pneumonia. A five year retrospective study of patients admitted for pneumococcal pneumonia. Patients were stratified based on admission data and assigned to low-, intermediate-, and high-risk classes for each score. Results were obtained comparing low versus non-low risk classes. We studied 142 episodes of hospitalization with 2 deaths and 10 patients needing mechanical ventilation and vasopressor support. The majority of patients were classified as low risk by all scores - we found high negative predictive values for all adverse events studied, the most negative value corresponding to the SCAP score. The more recent scores showed better accuracy for predicting ICU admission and need for ventilation or vasopressor support (mostly for the SCAP score with higher AUC values for all adverse events). The rate of all adverse outcomes increased directly with increasing risk class in all scores. The new gravity scores appear to have a higher discriminatory power in all adverse events in our study, particularly, the SCAP score. Copyright © 2012 Sociedade Portuguesa de Pneumologia. Published by Elsevier España. All rights reserved.

  19. Heat capacities of xenotime-type ceramics: An accurate ab initio prediction

    Science.gov (United States)

    Ji, Yaqi; Beridze, George; Bosbach, Dirk; Kowalski, Piotr M.

    2017-10-01

    Because of ability to incorporate actinides into their structure, the lanthanide phosphate ceramics (LnPO4) are considered as potential matrices for the disposal of nuclear waste. Here we present highly reliable ab initio prediction of the variation of heat capacities and the standard entropies of these compounds in zircon structure along lanthanide series (Ln = Dy, …,Lu) and validate them against the existing experimental data. These data are helpful for assessment of thermodynamic parameters of these materials in the context of using them as matrices for immobilization of radionuclides for the purpose of nuclear waste management.

  20. 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/.

  1. How Five Student Characteristics Accurately Predict For-Profit University Graduation Odds

    Directory of Open Access Journals (Sweden)

    Tim Gramling

    2013-07-01

    Full Text Available President Obama’s goal is for America to lead the world in college graduates by 2020. Although for-profit institutions have increased their output of graduates at ten times the rate of nonprofits over the past decade, Congress and the U.S. Department of Education have argued that these institutions exploit the ambitions of lower-performing students. In response, this study examined how student characteristics predicted graduation odds at a large, regionally accredited for-profit institution campus. A logistic regression predicted graduation for the full population of 2,548 undergraduate students enrolled from 2005 to 2009 with scheduled graduation by June 30, 2011. Sixteen independent predictors were identified from school records and organized in the Bean and Metzner framework. The regression model was more robust than any in the literature, with a Nagelkerke R2 of .663. Only five factors had a significant impact on log odds: (a grade point average (GPA, where higher values increased odds; (b half time enrollment, which had lower odds than full time; (c Blacks, who had higher odds than Whites; (d credits required, where fewer credits increased odds; and (e primary expected family contribution, where higher values increased odds. These findings imply that public policy will not increase college graduates by focusing on institution characteristics.

  2. Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences

    KAUST Repository

    Chen, Peng

    2013-07-23

    Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013 Wiley Periodicals, Inc.

  3. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods.

  4. Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method.

    Science.gov (United States)

    Huang, Wen-Lin; Chen, Hung-Ming; Hwang, Shiow-Fen; Ho, Shinn-Ying

    2007-01-01

    Amphiphilic pseudo-amino acid composition (Am-Pse-AAC) with extra sequence-order information is a useful feature for representing enzymes. This study first utilizes the k-nearest neighbor (k-NN) rule to analyze the distribution of enzymes in the Am-Pse-AAC feature space. This analysis indicates the distributions of multiple classes of enzymes are highly overlapped. To cope with the overlap problem, this study proposes an efficient non-parametric classifier for predicting enzyme subfamily class using an adaptive fuzzy r-nearest neighbor (AFK-NN) method, where k and a fuzzy strength parameter m are adaptively specified. The fuzzy membership values of a query sample Q are dynamically determined according to the position of Q and its weighted distances to the k nearest neighbors. Using the same enzymes of the oxidoreductases family for comparisons, the prediction accuracy of AFK-NN is 76.6%, which is better than those of Support Vector Machine (73.6%), the decision tree method C5.0 (75.4%) and the existing covariant-discriminate algorithm (70.6%) using a jackknife test. To evaluate the generalization ability of AFK-NN, the datasets for all six families of entirely sequenced enzymes are established from the newly updated SWISS-PROT and ENZYME database. The accuracy of AFK-NN on the new large-scale dataset of oxidoreductases family is 83.3%, and the mean accuracy of the six families is 92.1%.

  5. Computational methods toward accurate RNA structure prediction using coarse-grained and all-atom models.

    Science.gov (United States)

    Krokhotin, Andrey; Dokholyan, Nikolay V

    2015-01-01

    Computational methods can provide significant insights into RNA structure and dynamics, bridging the gap in our understanding of the relationship between structure and biological function. Simulations enrich and enhance our understanding of data derived on the bench, as well as provide feasible alternatives to costly or technically challenging experiments. Coarse-grained computational models of RNA are especially important in this regard, as they allow analysis of events occurring in timescales relevant to RNA biological function, which are inaccessible through experimental methods alone. We have developed a three-bead coarse-grained model of RNA for discrete molecular dynamics simulations. This model is efficient in de novo prediction of short RNA tertiary structure, starting from RNA primary sequences of less than 50 nucleotides. To complement this model, we have incorporated additional base-pairing constraints and have developed a bias potential reliant on data obtained from hydroxyl probing experiments that guide RNA folding to its correct state. By introducing experimentally derived constraints to our computer simulations, we are able to make reliable predictions of RNA tertiary structures up to a few hundred nucleotides. Our refined model exemplifies a valuable benefit achieved through integration of computation and experimental methods.

  6. ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives.

    Science.gov (United States)

    Wang, Jyun-Rong; Huang, Wen-Lin; Tsai, Ming-Ju; Hsu, Kai-Ti; Huang, Hui-Ling; Ho, Shinn-Ying

    2017-03-01

    Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models. We propose an evolutionary screening algorithm (ESA) to select effective negatives among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA-UbiSite use a set of well-selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA-UbiSite's test accuracy from 0.75 to 0.92 and can improve other post-translational modification site prediction methods. An ESA-UbiSite-based web server has been established at http://iclab.life.nctu.edu.tw/iclab_webtools/ESAUbiSite/ . syho@mail.nctu.edu.tw. Supplementary data are available at Bioinformatics online.

  7. MRI-aided tissues interface characterization: An accurate signal propagation time calculation method for UWB breast tumor imaging

    Science.gov (United States)

    Wang, Liang; Xiao, Xia; Kikkawa, Takamaro

    2016-12-01

    Radar-based ultrawideband (UWB) microwave imaging is expected to be a safe, low-cost tool for breast cancer detection. However, since radar wave travels at different speeds in different tissues, propagation time is hard to be estimated in heterogeneous breast. Wrongly estimated propagation time leads to error of tumor location in resulting image, aka imaging error. In this paper, we develop a magnetic resonance imaging-aided (MRI-aided) propagation time calculation technique which is independent from radar imaging system but can help decrease the imaging error. The technique can eliminate the influence of the rough interface between fat layer and gland layer in breast and get relative accurate thicknesses of two layers. The propagation time in each layer is calculated and summed. The summed propagation time is used in Confocal imaging algorithm to increase the accuracy of resulting image. 25 patients' breast models with glands of varying size are classified into four categories for imaging simulation tests. Imaging accuracy in terms of tumor location along x-direction has been improved for 21 among 25 cases, as a result, overall around 50% improvement compared to conventional UWB imaging.

  8. 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

  9. Fast and accurate prediction for aerodynamic forces and moments acting on satellites flying in Low-Earth Orbit

    Science.gov (United States)

    Jin, Xuhon; Huang, Fei; Hu, Pengju; Cheng, Xiaoli

    2016-11-01

    A fundamental prerequisite for satellites operating in a Low Earth Orbit (LEO) is the availability of fast and accurate prediction of non-gravitational aerodynamic forces, which is characterised by the free molecular flow regime. However, conventional computational methods like the analytical integral method and direct simulation Monte Carlo (DSMC) technique are found failing to deal with flow shadowing and multiple reflections or computationally expensive. This work develops a general computer program for the accurate calculation of aerodynamic forces in the free molecular flow regime using the test particle Monte Carlo (TPMC) method, and non-gravitational aerodynamic forces actiong on the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite is calculated for different freestream conditions and gas-surface interaction models by the computer program.

  10. Non-Empirically Tuned Range-Separated DFT Accurately Predicts Both Fundamental and Excitation Gaps in DNA and RNA Nucleobases

    CERN Document Server

    Foster, Michael E; 10.1021/ct300420f

    2012-01-01

    Using a non-empirically tuned range-separated DFT approach, we study both the quasiparticle properties (HOMO-LUMO fundamental gaps) and excitation energies of DNA and RNA nucleobases (adenine, thymine, cytosine, guanine, and uracil). Our calculations demonstrate that a physically-motivated, first-principles tuned DFT approach accurately reproduces results from both experimental benchmarks and more computationally intensive techniques such as many-body GW theory. Furthermore, in the same set of nucleobases, we show that the non-empirical range-separated procedure also leads to significantly improved results for excitation energies compared to conventional DFT methods. The present results emphasize the importance of a non-empirically tuned range-separation approach for accurately predicting both fundamental and excitation gaps in DNA and RNA nucleobases.

  11. A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications

    Energy Technology Data Exchange (ETDEWEB)

    Bronevetsky, G; de Supinski, B; Schulz, M

    2009-02-13

    Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.

  12. Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.

    Science.gov (United States)

    Faraggi, Eshel; Kouza, Maksim; Zhou, Yaoqi; Kloczkowski, Andrzej

    2017-01-01

    A fast accessible surface area (ASA) predictor is presented. In this new approach no residue mutation profiles generated by multiple sequence alignments are used as inputs. Instead, we use only single sequence information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for ASAquick are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org .

  13. nuMap:A Web Platform for Accurate Prediction of Nucleosome Positioning

    Institute of Scientific and Technical Information of China (English)

    Bader A Alharbi; Thamir H Alshammari; Nathan L Felton; Victor B Zhurkin; Feng Cui

    2014-01-01

    Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and param-eters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site.

  14. Simplified versus geometrically accurate models of forefoot anatomy to predict plantar pressures: A finite element study.

    Science.gov (United States)

    Telfer, Scott; Erdemir, Ahmet; Woodburn, James; Cavanagh, Peter R

    2016-01-25

    Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in 3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required.

  15. Towards Relaxing the Spherical Solar Radiation Pressure Model for Accurate Orbit Predictions

    Science.gov (United States)

    Lachut, M.; Bennett, J.

    2016-09-01

    The well-known cannonball model has been used ubiquitously to capture the effects of atmospheric drag and solar radiation pressure on satellites and/or space debris for decades. While it lends itself naturally to spherical objects, its validity in the case of non-spherical objects has been debated heavily for years throughout the space situational awareness community. One of the leading motivations to improve orbit predictions by relaxing the spherical assumption, is the ongoing demand for more robust and reliable conjunction assessments. In this study, we explore the orbit propagation of a flat plate in a near-GEO orbit under the influence of solar radiation pressure, using a Lambertian BRDF model. Consequently, this approach will account for the spin rate and orientation of the object, which is typically determined in practice using a light curve analysis. Here, simulations will be performed which systematically reduces the spin rate to demonstrate the point at which the spherical model no longer describes the orbital elements of the spinning plate. Further understanding of this threshold would provide insight into when a higher fidelity model should be used, thus resulting in improved orbit propagations. Therefore, the work presented here is of particular interest to organizations and researchers that maintain their own catalog, and/or perform conjunction analyses.

  16. The human skin/chick chorioallantoic membrane model accurately predicts the potency of cosmetic allergens.

    Science.gov (United States)

    Slodownik, Dan; Grinberg, Igor; Spira, Ram M; Skornik, Yehuda; Goldstein, Ronald S

    2009-04-01

    The current standard method for predicting contact allergenicity is the murine local lymph node assay (LLNA). Public objection to the use of animals in testing of cosmetics makes the development of a system that does not use sentient animals highly desirable. The chorioallantoic membrane (CAM) of the chick egg has been extensively used for the growth of normal and transformed mammalian tissues. The CAM is not innervated, and embryos are sacrificed before the development of pain perception. The aim of this study was to determine whether the sensitization phase of contact dermatitis to known cosmetic allergens can be quantified using CAM-engrafted human skin and how these results compare with published EC3 data obtained with the LLNA. We studied six common molecules used in allergen testing and quantified migration of epidermal Langerhans cells (LC) as a measure of their allergic potency. All agents with known allergic potential induced statistically significant migration of LC. The data obtained correlated well with published data for these allergens generated using the LLNA test. The human-skin CAM model therefore has great potential as an inexpensive, non-radioactive, in vivo alternative to the LLNA, which does not require the use of sentient animals. In addition, this system has the advantage of testing the allergic response of human, rather than animal skin.

  17. Towards Accurate Prediction of Turbulent, Three-Dimensional, Recirculating Flows with the NCC

    Science.gov (United States)

    Iannetti, A.; Tacina, R.; Jeng, S.-M.; Cai, J.

    2001-01-01

    The National Combustion Code (NCC) was used to calculate the steady state, nonreacting flow field of a prototype Lean Direct Injection (LDI) swirler. This configuration used nine groups of eight holes drilled at a thirty-five degree angle to induce swirl. These nine groups created swirl in the same direction, or a corotating pattern. The static pressure drop across the holes was fixed at approximately four percent. Computations were performed on one quarter of the geometry, because the geometry is considered rotationally periodic every ninety degrees. The final computational grid used was approximately 2.26 million tetrahedral cells, and a cubic nonlinear k - epsilon model was used to model turbulence. The NCC results were then compared to time averaged Laser Doppler Velocimetry (LDV) data. The LDV measurements were performed on the full geometry, but four ninths of the geometry was measured. One-, two-, and three-dimensional representations of both flow fields are presented. The NCC computations compare both qualitatively and quantitatively well to the LDV data, but differences exist downstream. The comparison is encouraging, and shows that NCC can be used for future injector design studies. To improve the flow prediction accuracy of turbulent, three-dimensional, recirculating flow fields with the NCC, recommendations are given.

  18. Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

    Energy Technology Data Exchange (ETDEWEB)

    Margot Gerritsen

    2008-10-31

    Gas-injection processes are widely and increasingly used for enhanced oil recovery (EOR). In the United States, for example, EOR production by gas injection accounts for approximately 45% of total EOR production and has tripled since 1986. The understanding of the multiphase, multicomponent flow taking place in any displacement process is essential for successful design of gas-injection projects. Due to complex reservoir geometry, reservoir fluid properties and phase behavior, the design of accurate and efficient numerical simulations for the multiphase, multicomponent flow governing these processes is nontrivial. In this work, we developed, implemented and tested a streamline based solver for gas injection processes that is computationally very attractive: as compared to traditional Eulerian solvers in use by industry it computes solutions with a computational speed orders of magnitude higher and a comparable accuracy provided that cross-flow effects do not dominate. We contributed to the development of compositional streamline solvers in three significant ways: improvement of the overall framework allowing improved streamline coverage and partial streamline tracing, amongst others; parallelization of the streamline code, which significantly improves wall clock time; and development of new compositional solvers that can be implemented along streamlines as well as in existing Eulerian codes used by industry. We designed several novel ideas in the streamline framework. First, we developed an adaptive streamline coverage algorithm. Adding streamlines locally can reduce computational costs by concentrating computational efforts where needed, and reduce mapping errors. Adapting streamline coverage effectively controls mass balance errors that mostly result from the mapping from streamlines to pressure grid. We also introduced the concept of partial streamlines: streamlines that do not necessarily start and/or end at wells. This allows more efficient coverage and avoids

  19. Lossless compression of hyperspectral images using hybrid context prediction.

    Science.gov (United States)

    Liang, Yuan; Li, Jianping; Guo, Ke

    2012-03-26

    In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.

  20. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke, E-mail: ksheng@mednet.ucla.edu [Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90024 (United States)

    2015-11-15

    attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

  1. How Accurate Are the Anthropometry Equations in in Iranian Military Men in Predicting Body Composition?

    Science.gov (United States)

    Shakibaee, Abolfazl; Faghihzadeh, Soghrat; Alishiri, Gholam Hossein; Ebrahimpour, Zeynab; Faradjzadeh, Shahram; Sobhani, Vahid; Asgari, Alireza

    2015-01-01

    Background: The body composition varies according to different life styles (i.e. intake calories and caloric expenditure). Therefore, it is wise to record military personnel’s body composition periodically and encourage those who abide to the regulations. Different methods have been introduced for body composition assessment: invasive and non-invasive. Amongst them, the Jackson and Pollock equation is most popular. Objectives: The recommended anthropometric prediction equations for assessing men’s body composition were compared with dual-energy X-ray absorptiometry (DEXA) gold standard to develop a modified equation to assess body composition and obesity quantitatively among Iranian military men. Patients and Methods: A total of 101 military men aged 23 - 52 years old with a mean age of 35.5 years were recruited and evaluated in the present study (average height, 173.9 cm and weight, 81.5 kg). The body-fat percentages of subjects were assessed both with anthropometric assessment and DEXA scan. The data obtained from these two methods were then compared using multiple regression analysis. Results: The mean and standard deviation of body fat percentage of the DEXA assessment was 21.2 ± 4.3 and body fat percentage obtained from three Jackson and Pollock 3-, 4- and 7-site equations were 21.1 ± 5.8, 22.2 ± 6.0 and 20.9 ± 5.7, respectively. There was a strong correlation between these three equations and DEXA (R² = 0.98). Conclusions: The mean percentage of body fat obtained from the three equations of Jackson and Pollock was very close to that of body fat obtained from DEXA; however, we suggest using a modified Jackson-Pollock 3-site equation for volunteer military men because the 3-site equation analysis method is simpler and faster than other methods. PMID:26715964

  2. Accurate prediction of a minimal region around a genetic association signal that contains the causal variant.

    Science.gov (United States)

    Bochdanovits, Zoltán; Simón-Sánchez, Javier; Jonker, Marianne; Hoogendijk, Witte J; van der Vaart, Aad; Heutink, Peter

    2014-02-01

    In recent years, genome-wide association studies have been very successful in identifying loci for complex traits. However, typically these findings involve noncoding and/or intergenic SNPs without a clear functional effect that do not directly point to a gene. Hence, the challenge is to identify the causal variant responsible for the association signal. Typically, the first step is to identify all genetic variation in the locus region, usually by resequencing a large number of case chromosomes. Among all variants, the causal one needs to be identified in further functional studies. Because the experimental follow up can be very laborious, restricting the number of variants to be scrutinized can yield a great advantage. An objective method for choosing the size of the region to be followed up would be highly valuable. Here, we propose a simple method to call the minimal region around a significant association peak that is very likely to contain the causal variant. We model linkage disequilibrium (LD) in cases from the observed single SNP association signals, and predict the location of the causal variant by quantifying how well this relationship fits the data. Simulations showed that our approach identifies genomic regions of on average ∼50 kb with up to 90% probability to contain the causal variant. We apply our method to two genome-wide association data sets and localize both the functional variant REP1 in the α-synuclein gene that conveys susceptibility to Parkinson's disease and the APOE gene responsible for the association signal in the Alzheimer's disease data set.

  3. Implementing an Accurate and Rapid Sparse Sampling Approach for Low-Dose Atomic Resolution STEM Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Kovarik, Libor; Stevens, Andrew J.; Liyu, Andrey V.; Browning, Nigel D.

    2016-10-17

    Aberration correction for scanning transmission electron microscopes (STEM) has dramatically increased spatial image resolution for beam-stable materials, but it is the sample stability rather than the microscope that often limits the practical resolution of STEM images. To extract physical information from images of beam sensitive materials it is becoming clear that there is a critical dose/dose-rate below which the images can be interpreted as representative of the pristine material, while above it the observation is dominated by beam effects. Here we describe an experimental approach for sparse sampling in the STEM and in-painting image reconstruction in order to reduce the electron dose/dose-rate to the sample during imaging. By characterizing the induction limited rise-time and hysteresis in scan coils, we show that sparse line-hopping approach to scan randomization can be implemented that optimizes both the speed of the scan and the amount of the sample that needs to be illuminated by the beam. The dose and acquisition time for the sparse sampling is shown to be effectively decreased by factor of 5x relative to conventional acquisition, permitting imaging of beam sensitive materials to be obtained without changing the microscope operating parameters. The use of sparse line-hopping scan to acquire STEM images is demonstrated with atomic resolution aberration corrected Z-contrast images of CaCO3, a material that is traditionally difficult to image by TEM/STEM because of dose issues.

  4. 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

    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 pres......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...

  5. Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

    Energy Technology Data Exchange (ETDEWEB)

    Margot Gerritsen

    2008-10-31

    Gas-injection processes are widely and increasingly used for enhanced oil recovery (EOR). In the United States, for example, EOR production by gas injection accounts for approximately 45% of total EOR production and has tripled since 1986. The understanding of the multiphase, multicomponent flow taking place in any displacement process is essential for successful design of gas-injection projects. Due to complex reservoir geometry, reservoir fluid properties and phase behavior, the design of accurate and efficient numerical simulations for the multiphase, multicomponent flow governing these processes is nontrivial. In this work, we developed, implemented and tested a streamline based solver for gas injection processes that is computationally very attractive: as compared to traditional Eulerian solvers in use by industry it computes solutions with a computational speed orders of magnitude higher and a comparable accuracy provided that cross-flow effects do not dominate. We contributed to the development of compositional streamline solvers in three significant ways: improvement of the overall framework allowing improved streamline coverage and partial streamline tracing, amongst others; parallelization of the streamline code, which significantly improves wall clock time; and development of new compositional solvers that can be implemented along streamlines as well as in existing Eulerian codes used by industry. We designed several novel ideas in the streamline framework. First, we developed an adaptive streamline coverage algorithm. Adding streamlines locally can reduce computational costs by concentrating computational efforts where needed, and reduce mapping errors. Adapting streamline coverage effectively controls mass balance errors that mostly result from the mapping from streamlines to pressure grid. We also introduced the concept of partial streamlines: streamlines that do not necessarily start and/or end at wells. This allows more efficient coverage and avoids

  6. Easy-to-use, general, and accurate multi-Kinect calibration and its application to gait monitoring for fall prediction.

    Science.gov (United States)

    Staranowicz, Aaron N; Ray, Christopher; Mariottini, Gian-Luca

    2015-01-01

    Falls are the most-common causes of unintentional injury and death in older adults. Many clinics, hospitals, and health-care providers are urgently seeking accurate, low-cost, and easy-to-use technology to predict falls before they happen, e.g., by monitoring the human walking pattern (or "gait"). Despite the wide popularity of Microsoft's Kinect and the plethora of solutions for gait monitoring, no strategy has been proposed to date to allow non-expert users to calibrate the cameras, which is essential to accurately fuse the body motion observed by each camera in a single frame of reference. In this paper, we present a novel multi-Kinect calibration algorithm that has advanced features when compared to existing methods: 1) is easy to use, 2) it can be used in any generic Kinect arrangement, and 3) it provides accurate calibration. Extensive real-world experiments have been conducted to validate our algorithm and to compare its performance against other multi-Kinect calibration approaches, especially to show the improved estimate of gait parameters. Finally, a MATLAB Toolbox has been made publicly available for the entire research community.

  7. A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race.

    Science.gov (United States)

    Hourihan, Kathleen L; Benjamin, Aaron S; Liu, Xiping

    2012-09-01

    The Cross-Race Effect (CRE) in face recognition is the well-replicated finding that people are better at recognizing faces from their own race, relative to other races. The CRE reveals systematic limitations on eyewitness identification accuracy and suggests that some caution is warranted in evaluating cross-race identification. The CRE is a problem because jurors value eyewitness identification highly in verdict decisions. In the present paper, we explore how accurate people are in predicting their ability to recognize own-race and other-race faces. Caucasian and Asian participants viewed photographs of Caucasian and Asian faces, and made immediate judgments of learning during study. An old/new recognition test replicated the CRE: both groups displayed superior discriminability of own-race faces, relative to other-race faces. Importantly, relative metamnemonic accuracy was also greater for own-race faces, indicating that the accuracy of predictions about face recognition is influenced by race. This result indicates another source of concern when eliciting or evaluating eyewitness identification: people are less accurate in judging whether they will or will not recognize a face when that face is of a different race than they are. This new result suggests that a witness's claim of being likely to recognize a suspect from a lineup should be interpreted with caution when the suspect is of a different race than the witness.

  8. Machine learning to analyze images of shocked materials for precise and accurate measurements

    Energy Technology Data Exchange (ETDEWEB)

    Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.; Meehan, B. T.; Ramos, Kyle J.; Bolme, Cindy A.; Sandberg, Richard L.; Nelson, Keith A.

    2017-09-14

    A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast images of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.

  9. An efficient method for accurate segmentation of LV in contrast-enhanced cardiac MR images

    Science.gov (United States)

    Suryanarayana K., Venkata; Mitra, Abhishek; Srikrishnan, V.; Jo, Hyun Hee; Bidesi, Anup

    2016-03-01

    Segmentation of left ventricle (LV) in contrast-enhanced cardiac MR images is a challenging task because of high variability in the image intensity. This is due to a) wash-in and wash-out of the contrast agent over time and b) poor contrast around the epicardium (outer wall) region. Current approaches for segmentation of the endocardium (inner wall) usually involve application of a threshold within the region of interest, followed by refinement techniques like active contours. A limitation of this method is under-segmentation of the inner wall because of gradual loss of contrast at the wall boundary. On the other hand, the challenge in outer wall segmentation is the lack of reliable boundaries because of poor contrast. There are four main contributions in this paper to address the aforementioned issues. First, a seed image is selected using variance based approach on 4D time-frame images over which initial endocardium and epicardium is segmented. Secondly, we propose a patch based feature which overcomes the problem of gradual contrast loss for LV endocardium segmentation. Third, we propose a novel Iterative-Edge-Refinement (IER) technique for epicardium segmentation. Fourth, we propose a greedy search algorithm for propagating the initial contour segmented on seed-image across other time frame images. We have experimented our technique on five contrast-enhanced cardiac MR Datasets (4D) having a total of 1097 images. The segmentation results for all 1097 images have been visually inspected by a clinical expert and have shown good accuracy.

  10. Comparison of an Imaging Software and Manual Prediction of Soft Tissue Changes after Orthognathic Surgery

    Directory of Open Access Journals (Sweden)

    M. S. Ahmad Akhoundi

    2012-01-01

    Full Text Available Objective: Accurate prediction of the surgical outcome is important in treating dentofacial deformities. Visualized treatment objectives usually involve manual surgical simulation based on tracing of cephalometric radiographs. Recent technical advancements have led to the use of computer assisted imaging systems in treatment planning for orthognathic surgical cases. The purpose of this study was to examine and compare the ability and reliability of digitization using Dolphin Imaging Software with traditional manual techniques and to compare orthognathic prediction with actual outcomes.Materials and Methods: Forty patients consisting of 35 women and 5 men (32 class III and 8 class II with no previous surgery were evaluated by manual tracing and indirect digitization using Dolphin Imaging Software. Reliability of each method was assessed then the two techniques were compared using paired t test.Result: The nasal tip presented the least predicted error and higher reliability. The least accurate regions in vertical plane were subnasal and upper lip, and subnasal and pogonion in horizontal plane. There were no statistically significant differences between the predictions of groups with and without genioplasty.Conclusion: Computer-generated image prediction was suitable for patient education and communication. However, efforts are still needed to improve accuracy and reliability of the prediction program and to include changes in soft tissue tension and muscle strain.

  11. Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images.

    Science.gov (United States)

    Wang, Jiheng; Rehman, Abdul; Zeng, Kai; Wang, Shiqi; Wang, Zhou

    2015-11-01

    Objective quality assessment of distorted stereoscopic images is a challenging problem, especially when the distortions in the left and right views are asymmetric. Existing studies suggest that simply averaging the quality of the left and right views well predicts the quality of symmetrically distorted stereoscopic images, but generates substantial prediction bias when applied to asymmetrically distorted stereoscopic images. In this paper, we first build a database that contains both single-view and symmetrically and asymmetrically distorted stereoscopic images. We then carry out a subjective test, where we find that the quality prediction bias of the asymmetrically distorted images could lean toward opposite directions (overestimate or underestimate), depending on the distortion types and levels. Our subjective test also suggests that eye dominance effect does not have strong impact on the visual quality decisions of stereoscopic images. Furthermore, we develop an information content and divisive normalization-based pooling scheme that improves upon structural similarity in estimating the quality of single-view images. Finally, we propose a binocular rivalry-inspired multi-scale model to predict the quality of stereoscopic images from that of the single-view images. Our results show that the proposed model, without explicitly identifying image distortion types, successfully eliminates the prediction bias, leading to significantly improved quality prediction of the stereoscopic images.

  12. Improved method for predicting the peak signal-to-noise ratio quality of decoded images in fractal image coding

    Science.gov (United States)

    Wang, Qiang; Bi, Sheng

    2017-01-01

    To predict the peak signal-to-noise ratio (PSNR) quality of decoded images in fractal image coding more efficiently and accurately, an improved method is proposed. After some derivations and analyses, we find that the linear correlation coefficients between coded range blocks and their respective best-matched domain blocks can determine the dynamic range of their collage errors, which can also provide the minimum and the maximum of the accumulated collage error (ACE) of uncoded range blocks. Moreover, the dynamic range of the actual percentage of accumulated collage error (APACE), APACEmin to APACEmax, can be determined as well. When APACEmin reaches a large value, such as 90%, APACEmin to APACEmax will be limited in a small range and APACE can be computed approximately. Furthermore, with ACE and the approximate APACE, the ACE of all range blocks and the average collage error (ACER) can be obtained. Finally, with the logarithmic relationship between ACER and the PSNR quality of decoded images, the PSNR quality of decoded images can be predicted directly. Experiments show that compared with the previous similar method, the proposed method can predict the PSNR quality of decoded images more accurately and needs less computation time simultaneously.

  13. Burnout prediction using advance image analysis coal characterization techniques

    Energy Technology Data Exchange (ETDEWEB)

    Edward Lester; Dave Watts; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical Environmental and Mining Engineering

    2003-07-01

    The link between petrographic composition and burnout has been investigated previously by the authors. However, these predictions were based on 'bulk' properties of the coal, including the proportion of each maceral or the reflectance of the macerals in the whole sample. Combustion studies relating burnout with microlithotype analysis, or similar, remain less common partly because the technique is more complex than maceral analysis. Despite this, it is likely that any burnout prediction based on petrographic characteristics will become more accurate if it includes information about the maceral associations and the size of each particle. Chars from 13 coals, 106-125 micron size fractions, were prepared using a Drop Tube Furnace (DTF) at 1300{degree}C and 200 millisecond and 1% Oxygen. These chars were then refired in the DTF at 1300{degree}C 5% oxygen and residence times of 200, 400 and 600 milliseconds. The progressive burnout of each char was compared with the characteristics of the initial coals. This paper presents an extension of previous studies in that it relates combustion behaviour to coals that have been characterized on a particle by particle basis using advanced image analysis techniques. 13 refs., 7 figs.

  14. Improved image quality in pinhole SPECT by accurate modeling of the point spread function in low magnification systems

    Energy Technology Data Exchange (ETDEWEB)

    Pino, Francisco [Unitat de Biofísica, Facultat de Medicina, Universitat de Barcelona, Barcelona 08036, Spain and Servei de Física Mèdica i Protecció Radiològica, Institut Català d’Oncologia, L’Hospitalet de Llobregat 08907 (Spain); Roé, Nuria [Unitat de Biofísica, Facultat de Medicina, Universitat de Barcelona, Barcelona 08036 (Spain); Aguiar, Pablo, E-mail: pablo.aguiar.fernandez@sergas.es [Fundación Ramón Domínguez, Complexo Hospitalario Universitario de Santiago de Compostela 15706, Spain and Grupo de Imagen Molecular, Instituto de Investigacións Sanitarias de Santiago de Compostela (IDIS), Galicia 15782 (Spain); Falcon, Carles; Ros, Domènec [Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain and CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona 08036 (Spain); Pavía, Javier [Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 080836 (Spain); CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona 08036 (Spain); and Servei de Medicina Nuclear, Hospital Clínic, Barcelona 08036 (Spain)

    2015-02-15

    Purpose: Single photon emission computed tomography (SPECT) has become an important noninvasive imaging technique in small-animal research. Due to the high resolution required in small-animal SPECT systems, the spatially variant system response needs to be included in the reconstruction algorithm. Accurate modeling of the system response should result in a major improvement in the quality of reconstructed images. The aim of this study was to quantitatively assess the impact that an accurate modeling of spatially variant collimator/detector response has on image-quality parameters, using a low magnification SPECT system equipped with a pinhole collimator and a small gamma camera. Methods: Three methods were used to model the point spread function (PSF). For the first, only the geometrical pinhole aperture was included in the PSF. For the second, the septal penetration through the pinhole collimator was added. In the third method, the measured intrinsic detector response was incorporated. Tomographic spatial resolution was evaluated and contrast, recovery coefficients, contrast-to-noise ratio, and noise were quantified using a custom-built NEMA NU 4–2008 image-quality phantom. Results: A high correlation was found between the experimental data corresponding to intrinsic detector response and the fitted values obtained by means of an asymmetric Gaussian distribution. For all PSF models, resolution improved as the distance from the point source to the center of the field of view increased and when the acquisition radius diminished. An improvement of resolution was observed after a minimum of five iterations when the PSF modeling included more corrections. Contrast, recovery coefficients, and contrast-to-noise ratio were better for the same level of noise in the image when more accurate models were included. Ring-type artifacts were observed when the number of iterations exceeded 12. Conclusions: Accurate modeling of the PSF improves resolution, contrast, and recovery

  15. How accurate are the fusion of cone-beam CT and 3-D stereophotographic images?

    Directory of Open Access Journals (Sweden)

    Yasas S N Jayaratne

    Full Text Available BACKGROUND: Cone-beam Computed Tomography (CBCT and stereophotography are two of the latest imaging modalities available for three-dimensional (3-D visualization of craniofacial structures. However, CBCT provides only limited information on surface texture. This can be overcome by combining the bone images derived from CBCT with 3-D photographs. The objectives of this study were 1 to evaluate the feasibility of integrating 3-D Photos and CBCT images 2 to assess degree of error that may occur during the above processes and 3 to identify facial regions that would be most appropriate for 3-D image registration. METHODOLOGY: CBCT scans and stereophotographic images from 29 patients were used for this study. Two 3-D images corresponding to the skin and bone were extracted from the CBCT data. The 3-D photo was superimposed on the CBCT skin image using relatively immobile areas of the face as a reference. 3-D colour maps were used to assess the accuracy of superimposition were distance differences between the CBCT and 3-D photo were recorded as the signed average and the Root Mean Square (RMS error. PRINCIPAL FINDINGS: The signed average and RMS of the distance differences between the registered surfaces were -0.018 (±0.129 mm and 0.739 (±0.239 mm respectively. The most errors were found in areas surrounding the lips and the eyes, while minimal errors were noted in the forehead, root of the nose and zygoma. CONCLUSIONS: CBCT and 3-D photographic data can be successfully fused with minimal errors. When compared to RMS, the signed average was found to under-represent the registration error. The virtual 3-D composite craniofacial models permit concurrent assessment of bone and soft tissues during diagnosis and treatment planning.

  16. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging

    Science.gov (United States)

    Hughes, Timothy J.; Kandathil, Shaun M.; Popelier, Paul L. A.

    2015-02-01

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G**, B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol-1, decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol-1.

  17. Automatic Rotation Recovery Algorithm for Accurate Digital Image and Video Watermarks Extraction

    Directory of Open Access Journals (Sweden)

    Nasr addin Ahmed Salem Al-maweri

    2016-11-01

    Full Text Available Research in digital watermarking has evolved rapidly in the current decade. This evolution brought various different methods and algorithms for watermarking digital images and videos. Introduced methods in the field varies from weak to robust according to how tolerant the method is implemented to keep the existence of the watermark in the presence of attacks. Rotation attacks applied to the watermarked media is one of the serious attacks which many, if not most, algorithms cannot survive. In this paper, a new automatic rotation recovery algorithm is proposed. This algorithm can be plugged to any image or video watermarking algorithm extraction component. The main job for this method is to detect the geometrical distortion happens to the watermarked image/images sequence; recover the distorted scene to its original state in a blind and automatic way and then send it to be used by the extraction procedure. The work is limited to have a recovery process to zero padded rotations for now, cropped images after rotation is left as future work. The proposed algorithm is tested on top of extraction component. Both recovery accuracy and the extracted watermarks accuracy showed high performance level.

  18. Predicting Visible Image Degradation by Colour Image Difference Formulae

    Institute of Scientific and Technical Information of China (English)

    Eriko Bando; Jon Y. Hardeberg; David Connah; Ivar Farup

    2004-01-01

    It carried out a CRT monitor based psychophysical experiment to investigate the quality of three colour image difference metrics, the CIEAE ab equation, the iCAM and the S-CIELAB metrics. Six original images were reproduced through six gamut mapping algorithms for the observer experiment. The result indicates that the colour image difference calculated by each metric does not directly relate to perceived image difference.

  19. SU-E-J-23: An Accurate Algorithm to Match Imperfectly Matched Images for Lung Tumor Detection Without Markers

    Energy Technology Data Exchange (ETDEWEB)

    Rozario, T; Bereg, S [University of Texas at Dallas, Richardson, TX (United States); Chiu, T; Liu, H; Kearney, V; Jiang, L; Mao, W [UT Southwestern Medical Center, Dallas, TX (United States)

    2014-06-01

    Purpose: In order to locate lung tumors on projection images without internal markers, digitally reconstructed radiograph (DRR) is created and compared with projection images. Since lung tumors always move and their locations change on projection images while they are static on DRRs, a special DRR (background DRR) is generated based on modified anatomy from which lung tumors are removed. In addition, global discrepancies exist between DRRs and projections due to their different image originations, scattering, and noises. This adversely affects comparison accuracy. A simple but efficient comparison algorithm is reported. Methods: This method divides global images into a matrix of small tiles and similarities will be evaluated by calculating normalized cross correlation (NCC) between corresponding tiles on projections and DRRs. The tile configuration (tile locations) will be automatically optimized to keep the tumor within a single tile which has bad matching with the corresponding DRR tile. A pixel based linear transformation will be determined by linear interpolations of tile transformation results obtained during tile matching. The DRR will be transformed to the projection image level and subtracted from it. The resulting subtracted image now contains only the tumor. A DRR of the tumor is registered to the subtracted image to locate the tumor. Results: This method has been successfully applied to kV fluoro images (about 1000 images) acquired on a Vero (Brainlab) for dynamic tumor tracking on phantom studies. Radiation opaque markers are implanted and used as ground truth for tumor positions. Although, other organs and bony structures introduce strong signals superimposed on tumors at some angles, this method accurately locates tumors on every projection over 12 gantry angles. The maximum error is less than 2.6 mm while the total average error is 1.0 mm. Conclusion: This algorithm is capable of detecting tumor without markers despite strong background signals.

  20. Fast and accurate image recognition algorithms for fresh produce food safety sensing

    Science.gov (United States)

    Yang, Chun-Chieh; Kim, Moon S.; Chao, Kuanglin; Kang, Sukwon; Lefcourt, Alan M.

    2011-06-01

    This research developed and evaluated the multispectral algorithms derived from hyperspectral line-scan fluorescence imaging under violet LED excitation for detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680 nm, 684 nm, 720 nm, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on fast-speed apple processing lines.

  1. Two-dimensional Moiré phase analysis for accurate strain distribution measurement and application in crack prediction.

    Science.gov (United States)

    Wang, Qinghua; Ri, Shien; Tsuda, Hiroshi; Koyama, Motomichi; Tsuzaki, Kaneaki

    2017-06-12

    Aimed at the low accuracy problem of shear strain measurement in Moiré methods, a two-dimensional (2D) Moiré phase analysis method is proposed for full-field deformation measurement with high accuracy. A grid image is first processed by the spatial phase-shifting sampling Moiré technique to get the Moiré phases in two directions, which are then conjointly analyzed for measuring 2D displacement and strain distributions. The strain especially the shear strain measurement accuracy is remarkably improved, and dynamic deformation is measurable from automatic batch processing of single-shot grid images. As an application, the 2D microscale strain distributions of a titanium alloy were measured, and the crack occurrence location was successfully predicted from strain concentration.

  2. 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.

  3. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

    Science.gov (United States)

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.; Barillot, T. R.; Ilchen, M.; Lutman, A. A.; Marinelli, A.; Maxwell, T.; Achner, A.; Agåker, M.; Berrah, N.; Bostedt, C.; Bozek, J. D.; Buck, J.; Bucksbaum, P. H.; Montero, S. Carron; Cooper, B.; Cryan, J. P.; Dong, M.; Feifel, R.; Frasinski, L. J.; Fukuzawa, H.; Galler, A.; Hartmann, G.; Hartmann, N.; Helml, W.; Johnson, A. S.; Knie, A.; Lindahl, A. O.; Liu, J.; Motomura, K.; Mucke, M.; O'Grady, C.; Rubensson, J.-E.; Simpson, E. R.; Squibb, R. J.; Såthe, C.; Ueda, K.; Vacher, M.; Walke, D. J.; Zhaunerchyk, V.; Coffee, R. N.; Marangos, J. P.

    2017-06-01

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.

  4. A data mining based approach to predict spatiotemporal changes in satellite images

    Science.gov (United States)

    Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben

    2011-06-01

    The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

  5. SPECT-OPT multimodal imaging enables accurate evaluation of radiotracers for beta-cell mass assessments

    NARCIS (Netherlands)

    Eter, W.A.; Parween, S.; Joosten, L.; Frielink, C.; Eriksson, M.; Brom, M.; Ahlgren, U.; Gotthardt, M.

    2016-01-01

    Single Photon Emission Computed Tomography (SPECT) has become a promising experimental approach to monitor changes in beta-cell mass (BCM) during diabetes progression. SPECT imaging of pancreatic islets is most commonly cross-validated by stereological analysis of histological pancreatic sections

  6. 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.

  7. A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

    Directory of Open Access Journals (Sweden)

    Vineet Kumar

    2016-01-01

    Full Text Available Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair. In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform (CHT is used to find the pupil circle parameters in the edge-map. Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0, CASIA-Iris-Lamp (version 3.0, and MMU (version 2.0. The average accuracy of the proposed method is 99.72% and average time cost per image is 0.727 sec.

  8. An accurate and practical method for inference of weak gravitational lensing from galaxy images

    CERN Document Server

    Bernstein, Gary M; Krawiec, Christina; March, Marisa C

    2015-01-01

    We demonstrate recovery of weak gravitational lensing shear at parts-per-thousand accuracy using an implementation of the Bayesian Fourier Domain (BFD) method proposed by Bernstein \\& Armstrong (2014, BA14). The BFD formalism is rigorously correct for Nyquist-sampled, background-limited, uncrowded image of background galaxies. BFD does not assign shapes to galaxies, instead compressing the pixel data D into a vector of moments M, such that we have an analytic expression for the probability P(M|g) of obtaining the observations with gravitational lensing distortion g along the line of sight. We extend the BA14 formalism to include detection and selection of galaxies without inducing biases on the inferred g. We describe a practical algorithm for conducting BFD's integrations over the population of unlensed source galaxies. Our BFD implementation measures ~10 galaxies per second per core on current hardware, a speed that will be largely independent of the number of images taken of each target. Initial tests ...

  9. Accurate positioning for head and neck cancer patients using 2D and 3D image guidance

    Science.gov (United States)

    Kang, Hyejoo; Lovelock, Dale M.; Yorke, Ellen D.; Kriminiski, Sergey; Lee, Nancy; Amols, Howard I.

    2011-01-01

    Our goal is to determine an optimized image-guided setup by comparing setup errors determined by two-dimensional (2D) and three-dimensional (3D) image guidance for head and neck cancer (HNC) patients immobilized by customized thermoplastic masks. Nine patients received weekly imaging sessions, for a total of 54, throughout treatment. Patients were first set up by matching lasers to surface marks (initial) and then translationally corrected using manual registration of orthogonal kilovoltage (kV) radiographs with DRRs (2D-2D) on bony anatomy. A kV cone beam CT (kVCBCT) was acquired and manually registered to the simulation CT using only translations (3D-3D) on the same bony anatomy to determine further translational corrections. After treatment, a second set of kVCBCT was acquired to assess intrafractional motion. Averaged over all sessions, 2D-2D registration led to translational corrections from initial setup of 3.5 ± 2.2 (range 0–8) mm. The addition of 3D-3D registration resulted in only small incremental adjustment (0.8 ± 1.5 mm). We retrospectively calculated patient setup rotation errors using an automatic rigid-body algorithm with 6 degrees of freedom (DoF) on regions of interest (ROI) of in-field bony anatomy (mainly the C2 vertebral body). Small rotations were determined for most of the imaging sessions; however, occasionally rotations > 3° were observed. The calculated intrafractional motion with automatic registration was < 3.5 mm for eight patients, and < 2° for all patients. We conclude that daily manual 2D-2D registration on radiographs reduces positioning errors for mask-immobilized HNC patients in most cases, and is easily implemented. 3D-3D registration adds little improvement over 2D-2D registration without correcting rotational errors. We also conclude that thermoplastic masks are effective for patient immobilization. PMID:21330971

  10. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    Science.gov (United States)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  11. 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.

  12. 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

    2017-03-01

    Constrained-occupancy delta-self-consistent-field (Δ SCF ) 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 1 s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The Δ SCF 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 Δ SCF 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.

  13. A comparison of current prediction imaging programs

    Science.gov (United States)

    Smith, J. Dempsey; Thomas, Paul M.; Proffit, William R.

    2013-01-01

    To investigate perceived differences in the ability of current software to simulate the actual outcome of orthognathic surgery, we chose 10 difficult test cases with vertical discrepancies and “retreated” them using the actual surgical changes. Five programs—Dentofacial Planner Plus, Dolphin Imaging, Orthoplan, Quick Ceph Image, and Vistadent—were evaluated, by using both the default result and a refined result created with each program’s enhancement tools. Three panels (orthodontists, oral-maxillofacial surgeons, and laypersons) judged the default images and the retouched simulations by ranking the simulations in side-by-side comparisons and by rating each simulation relative to the actual outcome on a 6-point scale. For the default and retouched images, Dentofacial Planner Plus was judged the best default simulation 79% and 59% of the time, respectively, and its default images received the best (lowest) mean score (2.46) on the 6-point scale. It also scored best (2.26) when the retouched images were compared, but the scores for Dolphin Imaging (2.83) and Quick Ceph (3.03) improved. Retouching had little impact on the scores for the other programs. Although the results show differences in simulation ability, selecting a software package depends on many factors. Performance and ease of use, cost, compatibility, and other features such as image and practice management tools are all important considerations. Users concerned with operating system compatibility and practice management integration might want to consider Dolphin Imaging and Quick Ceph, the programs comprising the second tier. PMID:15127020

  14. An automatic method for fast and accurate liver segmentation in CT images using a shape detection level set method

    Science.gov (United States)

    Lee, Jeongjin; Kim, Namkug; Lee, Ho; Seo, Joon Beom; Won, Hyung Jin; Shin, Yong Moon; Shin, Yeong Gil

    2007-03-01

    Automatic liver segmentation is still a challenging task due to the ambiguity of liver boundary and the complex context of nearby organs. In this paper, we propose a faster and more accurate way of liver segmentation in CT images with an enhanced level set method. The speed image for level-set propagation is smoothly generated by increasing number of iterations in anisotropic diffusion filtering. This prevents the level-set propagation from stopping in front of local minima, which prevails in liver CT images due to irregular intensity distributions of the interior liver region. The curvature term of shape modeling level-set method captures well the shape variations of the liver along the slice. Finally, rolling ball algorithm is applied for including enhanced vessels near the liver boundary. Our approach are tested and compared to manual segmentation results of eight CT scans with 5mm slice distance using the average distance and volume error. The average distance error between corresponding liver boundaries is 1.58 mm and the average volume error is 2.2%. The average processing time for the segmentation of each slice is 5.2 seconds, which is much faster than the conventional ones. Accurate and fast result of our method will expedite the next stage of liver volume quantification for liver transplantations.

  15. The detection and prediction for oil spill on the sea based on the infrared images

    Science.gov (United States)

    Chen, Xu; Liu, Lei; Huang, Wei

    2016-07-01

    Detection for oil pollution is an important part of the marine environment protection in maritime security. In order to realize all-weather, rapid and accurate oil spill area detection, infrared images of oil spill on the sea is processed on account of infrared thermal imaging's visual capacity in darkness and frog. The detection for oil spill is realized and the location as well as the area of oil spill is calculated. The prediction integrated model of oil spill spreading is established and the prediction simulation for oil spill area is realized by changing the oil varieties, environmental factors and time, etc. The results show that this simulation is accurate, fast, intuitive and simple. It has certain significance for realizing the early warning of oil spill area detection automatically, intelligently and quickly.

  16. Medical Image Compression using Wavelet Decomposition for Prediction Method

    CERN Document Server

    Ramesh, S M

    2010-01-01

    In this paper offers a simple and lossless compression method for compression of medical images. Method is based on wavelet decomposition of the medical images followed by the correlation analysis of coefficients. The correlation analyses are the basis of prediction equation for each sub band. Predictor variable selection is performed through coefficient graphic method to avoid multicollinearity problem and to achieve high prediction accuracy and compression rate. The method is applied on MRI and CT images. Results show that the proposed approach gives a high compression rate for MRI and CT images comparing with state of the art methods.

  17. Transrectal high-intensity focused ultrasound ablation of prostate cancer: Effective treatment requiring accurate imaging

    Energy Technology Data Exchange (ETDEWEB)

    Rouviere, Olivier [Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Pavillon P Radio, Hopital Edouard Herriot, F-69437 Lyon cedex 03 (France); Universite de Lyon, Lyon F-69003 (France); Universite de Lyon 1, Faculte de Medecine Lyon Nord, Lyon F-69003 (France); INSERM, Unit 556, 151 cours Albert Thomas, F-69424 Lyon cedex 03 (France)], E-mail: Olivier.rouviere@netcourrier.com; Souchon, Remi [INSERM, Unit 556, 151 cours Albert Thomas, F-69424 Lyon cedex 03 (France)], E-mail: souchon@lyon.inserm.fr; Salomir, Rares [INSERM, Unit 556, 151 cours Albert Thomas, F-69424 Lyon cedex 03 (France)], E-mail: salomir@lyon.inserm.fr; Gelet, Albert [Hospices Civils de Lyon, Department of Urology, Pavillon P Radio, Hopital Edouard Herriot, F-69437 Lyon cedex 03 (France)], E-mail: Albert.gelet@chu-lyon.fr; Chapelon, Jean-Yves [INSERM, Unit 556, 151 cours Albert Thomas, F-69424 Lyon cedex 03 (France)], E-mail: chapelon@lyon.inserm.fr; Lyonnet, Denis [Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Pavillon P Radio, Hopital Edouard Herriot, F-69437 Lyon cedex 03 (France); Universite de Lyon, Lyon F-69003 (France); Universite de Lyon 1, Faculte de Medecine Lyon Nord, Lyon F-69003 (France); INSERM, Unit 556, 151 cours Albert Thomas, F-69424 Lyon cedex 03 (France)], E-mail: Denis.lyonnet@chu-lyon.fr

    2007-09-15

    Transrectal HIFU ablation has become a reasonable option for the treatment of localized prostate cancer in non-surgical patients, with 5-year disease-free survival similar to that of radiation therapy. It is also a promising salvage therapy of local recurrence after radiation therapy. These favourable results are partly due to recent improvements in prostate cancer imaging. However, further improvements are needed in patient selection, pre-operative localization of the tumor foci, assessment of the volume treated and early detection of recurrence. A better knowledge of the factors influencing the HIFU-induced tissue destruction and a better pre-operative assessment of them by imaging techniques should improve treatment outcome. Whereas prostate HIFU ablation is currently performed under transrectal ultrasound guidance, MR guidance with real-time operative monitoring of temperature will be available in the near future. If this technique will give better targeting and more uniform tissue destruction, its cost-effectiveness will have to be carefully evaluated. Finally, a recently reported synergistic effect between HIFU ablation and chemotherapy opens possibilities for treatment in high-risk or clinically advanced tumors.

  18. A hybrid method for efficient and accurate simulations of diffusion compartment imaging signals

    Science.gov (United States)

    Rensonnet, Gaëtan; Jacobs, Damien; Macq, Benoît; Taquet, Maxime

    2015-12-01

    Diffusion-weighted imaging is sensitive to the movement of water molecules through the tissue microstructure and can therefore be used to gain insight into the tissue cellular architecture. While the diffusion signal arising from simple geometrical microstructure is known analytically, it remains unclear what diffusion signal arises from complex microstructural configurations. Such knowledge is important to design optimal acquisition sequences, to understand the limitations of diffusion-weighted imaging and to validate novel models of the brain microstructure. We present a novel framework for the efficient simulation of high-quality DW-MRI signals based on the hybrid combination of exact analytic expressions in simple geometric compartments such as cylinders and spheres and Monte Carlo simulations in more complex geometries. We validate our approach on synthetic arrangements of parallel cylinders representing the geometry of white matter fascicles, by comparing it to complete, all-out Monte Carlo simulations commonly used in the literature. For typical configurations, equal levels of accuracy are obtained with our hybrid method in less than one fifth of the computational time required for Monte Carlo simulations.

  19. Accurate determination of the diffusion coefficient of proteins by Fourier analysis with whole column imaging detection.

    Science.gov (United States)

    Zarabadi, Atefeh S; Pawliszyn, Janusz

    2015-02-17

    Analysis in the frequency domain is considered a powerful tool to elicit precise information from spectroscopic signals. In this study, the Fourier transformation technique is employed to determine the diffusion coefficient (D) of a number of proteins in the frequency domain. Analytical approaches are investigated for determination of D from both experimental and data treatment viewpoints. The diffusion process is modeled to calculate diffusion coefficients based on the Fourier transformation solution to Fick's law equation, and its results are compared to time domain results. The simulations characterize optimum spatial and temporal conditions and demonstrate the noise tolerance of the method. The proposed model is validated by its application for the electropherograms from the diffusion path of a set of proteins. Real-time dynamic scanning is conducted to monitor dispersion by employing whole column imaging detection technology in combination with capillary isoelectric focusing (CIEF) and the imaging plug flow (iPF) experiment. These experimental techniques provide different peak shapes, which are utilized to demonstrate the Fourier transformation ability in extracting diffusion coefficients out of irregular shape signals. Experimental results confirmed that the Fourier transformation procedure substantially enhanced the accuracy of the determined values compared to those obtained in the time domain.

  20. D-optimal design of b-values for accurate intra-voxel incoherent motion imaging

    CERN Document Server

    Sansone, Mario; Petrillo, Antonella

    2016-01-01

    The aim of this paper is to optimally design the set of b-values for diffusion weighted MRI with the aim of accurate estimation of intra- voxel incoherent motion (IVIM) parameters (f perfusion fraction, Ds slow diffusion, Df fast diffusion) according to the model developed by Le Bihan. Previous studies have addressed the design in a Monte Carlo fash- ion. Due to huge computation times, this approach is feasible only for a limited number of values of the parameters (local design): however, as the parameters of a specific exam are not known a priori, it would be desirable to optimise b-values over a region of parameters. In order to overcome this issue, we propose to use a D-optimal design approach. The optimal combination of b-values can be chosen from a candidate set of predefined values taken from the literature. Our study has two key results: first, the optimal design does not depend on perfusion fraction: this allow to perform a search over a 2D parameter space instead of 3D; second, as an exhaustive searc...

  1. SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Jia, J; Cao, R; Pei, X; Wang, H; Hu, L [Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui, 230031 (China); Engineering Technology Research Center of Accurate Radiotherapy of Anhui Province, Hefei 230031 (China); Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, SuZhou (China)

    2015-06-15

    Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy.

  2. Early functional magnetic resonance imaging activations predict language outcome after stroke.

    Science.gov (United States)

    Saur, Dorothee; Ronneberger, Olaf; Kümmerer, Dorothee; Mader, Irina; Weiller, Cornelius; Klöppel, Stefan

    2010-04-01

    An accurate prediction of system-specific recovery after stroke is essential to provide rehabilitation therapy based on the individual needs. We explored the usefulness of functional magnetic resonance imaging scans from an auditory language comprehension experiment to predict individual language recovery in 21 aphasic stroke patients. Subjects with an at least moderate language impairment received extensive language testing 2 weeks and 6 months after left-hemispheric stroke. A multivariate machine learning technique was used to predict language outcome 6 months after stroke. In addition, we aimed to predict the degree of language improvement over 6 months. 76% of patients were correctly separated into those with good and bad language performance 6 months after stroke when based on functional magnetic resonance imaging data from language relevant areas. Accuracy further improved (86% correct assignments) when age and language score were entered alongside functional magnetic resonance imaging data into the fully automatic classifier. A similar accuracy was reached when predicting the degree of language improvement based on imaging, age and language performance. No prediction better than chance level was achieved when exploring the usefulness of diffusion weighted imaging as well as functional magnetic resonance imaging acquired two days after stroke. This study demonstrates the high potential of current machine learning techniques to predict system-specific clinical outcome even for a disease as heterogeneous as stroke. Best prediction of language recovery is achieved when the brain activation potential after system-specific stimulation is assessed in the second week post stroke. More intensive early rehabilitation could be provided for those with a predicted poor recovery and the extension to other systems, for example, motor and attention seems feasible.

  3. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

    Science.gov (United States)

    Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N. C.; Tomaszewski, John; González, Fabio A.; Madabhushi, Anant

    2017-04-01

    With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.

  4. The Effect of Country Based Image in Accurance of Brand in Cultural Destinations

    Directory of Open Access Journals (Sweden)

    Hulya Kurgun

    2010-04-01

    Full Text Available Despite its location and historical and cultural attractions, Izmir has been unable to consistently achieve its tourism goals, as evidenced by fluctuating numbers in tourism earnings and a small share of the international tourism market. This discrepancy might be attributed to Turkey’s image in the minds of world travelers, as well as to a low recognition of Izmir. The purpose of this study is twofold: (1 to identify visitors’ impressions that have been effective on their choice of Turkey as a vacation destination and (2 to determine whether there is a relationship between these impressions and their perceptions about Izmir. According to the study results there is a relationship between the variables related to participants choosing Turkey and their impressions about Izmir.

  5. Cystic metastasis versus brain abscess: Role of MR imaging in accurate diagnosis and implications on treatment

    Directory of Open Access Journals (Sweden)

    Shetty Prakash

    2010-01-01

    Full Text Available Intracranial cystic metastases are difficult to differentiate from non neoplastic lesions like a cystic abscess on routine magnetic resonance imaging (MRI sequences in patients with a known primary cancer. Diffusion weighted MRI sequences can help in differentiating between the two. This carries significant implications on the intent and nature of treatment. We present a case of a forty five-year- old patient of squamous cell carcinoma of larynx who developed an intracranial cystic ring enhancing lesion following chemotherapy. Though routine MRI was equivocal, MR diffusion was highly suggestive of an abscess and this led to surgical excision which confirmed the diagnosis. We discuss this case and review the literature regarding the role of newer MRI sequences and the ramifications on patient treatment.

  6. Improved highly accurate localized motion imaging for monitoring high-intensity focused ultrasound therapy

    Science.gov (United States)

    Qu, Xiaolei; Azuma, Takashi; Sugiyama, Ryusuke; Kanazawa, Kengo; Seki, Mika; Sasaki, Akira; Takeuchi, Hideki; Fujiwara, Keisuke; Itani, Kazunori; Tamano, Satoshi; Takagi, Shu; Sakuma, Ichiro; Matsumoto, Yoichiro

    2016-07-01

    Visualizing an area subjected to high-intensity focused ultrasound (HIFU) therapy is necessary for controlling the amount of HIFU exposure. One of the promising monitoring methods is localized motion imaging (LMI), which estimates coagulation length by detecting the change in stiffness. In this study, we improved the accuracy of our previous LMI by dynamic cross-correlation window (DCCW) and maximum vibration amount (MVA) methods. The DCCW method was used to increase the accuracy of estimating vibration amplitude, and the MVA method was employed to increase signal-noise ratio of the decrease ratio at the coagulated area. The qualitative comparison of results indicated that the two proposed methods could suppress the effect of noise. Regarding the results of the quantitative comparison, coagulation length was estimated with higher accuracy by the improved LMI method, and the root-mean-square error (RMSE) was reduced from 2.51 to 1.69 mm.

  7. Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering

    CERN Document Server

    Hervier, Thibault; Goulette, François

    2012-01-01

    This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with measurements from other motion sensors, or even with prior knowledge on the motion. The data fusion is performed by a recently introduced specific extended Kalman filter, the so-called Invariant EKF, and is directly based on the estimated covariance of the ICP. The resulting filter is very natural, and is proved to possess strong properties. Experiments with a Kinect sensor and a three-axis gyroscope prove clear improvement in the accuracy of the localization, and thus in the accuracy of the built 3D map.

  8. Accounting for tissue heterogeneity in infrared spectroscopic imaging for accurate diagnosis of thyroid carcinoma subtypes.

    Science.gov (United States)

    Martinez-Marin, David; Sreedhar, Hari; Varma, Vishal K; Eloy, Catarina; Sobrinho-Simões, Manuel; Kajdacsy-Balla, André; Walsh, Michael J

    2017-07-01

    Fourier transform infrared (FT-IR) microscopy was used to image tissue samples from twenty patients diagnosed with thyroid carcinoma. The spectral data were then used to differentiate between follicular thyroid carcinoma and follicular variant of papillary thyroid carcinoma using principle component analysis coupled with linear discriminant analysis and a Naïve Bayesian classifier operating on a set of computed spectral metrics. Classification of patients' disease type was accomplished by using average spectra from a wide region containing follicular cells, colloid, and fibrosis; however, classification of disease state at the pixel level was only possible when the extracted spectra were limited to follicular epithelial cells in the samples, excluding the relatively uninformative areas of fibrosis. The results demonstrate the potential of FT-IR microscopy as a tool to assist in the difficult diagnosis of these subtypes of thyroid cancer, and also highlights the importance of selectively and separately analyzing spectral information from different features of a tissue of interest.

  9. Whole-body FDG PET/CT is more accurate than conventional imaging for staging primary breast cancer patients

    Energy Technology Data Exchange (ETDEWEB)

    Riegger, C.; Heusner, T.A. [Univ Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf (Germany); University of Duisburg-Essen, Medical Faculty, Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen (Germany); Herrmann, J.; Hahn, S.; Lauenstein, T. [University of Duisburg-Essen, Medical Faculty, Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen (Germany); Nagarajah, J.; Bockisch, A. [University of Duisburg-Essen, Medical Faculty, Department of Nuclear Medicine, Essen (Germany); Hecktor, J.; Kuemmel, S. [University of Duisburg-Essen, Medical Faculty, Department of Gynecology and Obstetrics, Essen (Germany); Otterbach, F. [University of Duisburg-Essen, Institute of Pathology and Neuropathology, Essen (Germany); Antoch, G. [Univ Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf (Germany)

    2012-05-15

    This retrospective study aimed (1) to compare the diagnostic accuracy of whole-body FDG PET/CT for initial breast cancer staging with the accuracy of a conventional, multimodal imaging algorithm, and (2) to assess potential alteration in patient management based on the FDG PET/CT findings. Patients with primary breast cancer (106 women, mean age 57 {+-} 13 years) underwent whole-body FDG PET/CT and conventional imaging (X-ray mammography, MR mammography, chest plain radiography, bone scintigraphy and breast, axillary and liver ultrasonography). The diagnostic accuracies of FDG PET/CT and a conventional algorithm were compared. Diagnostic accuracy was assessed in terms of primary tumour detection rate, correct assessment of primary lesion focality, T stage and the detection rates for lymph node and distant metastases. Histopathology, imaging or clinical follow-up served as the standards of reference. FDG PET/CT was significantly more accurate for detecting axillary lymph node and distant metastases (p = 0.0125 and p < 0.005, respectively). No significant differences were detected for other parameters. Synchronous tumours or locoregional extraaxillary lymph node or distant metastases were detected in 14 patients (13%) solely by FDG PET/CT. Management of 15 patients (14%) was altered based on the FDG PET/CT findings, including 3 patients with axillary lymph node metastases, 5 patients with extraaxillary lymph node metastases, 4 patients with distant metastases and 3 patients with synchronous malignancies. Full-dose, intravenous contrast-enhanced FDG PET/CT was more accurate than conventional imaging for initial breast cancer staging due to the higher detection rate of metastases and synchronous tumours, although the study had several limitations including a retrospective design, a possible selection bias and a relevant false-positive rate for the detection of axillary lymph node metastases. FDG PET/CT resulted in a change of treatment in a substantial proportion of

  10. A composite score combining waist circumference and body mass index more accurately predicts body fat percentage in 6-to 13-year-old children

    NARCIS (Netherlands)

    Aeberli, I.; Gut-Knabenhans, M.; Kusche-Ammann, R.S.; Molinari, L.; Zimmermann, M.B.

    2013-01-01

    Body mass index (BMI) and waist circumference (WC) are widely used to predict % body fat (BF) and classify degrees of pediatric adiposity. However, both measures have limitations. The aim of this study was to evaluate whether a combination of WC and BMI would more accurately predict %BF than either

  11. Fast and Accurate Collocation of the Visible Infrared Imaging Radiometer Suite Measurements with Cross-Track Infrared Sounder

    Directory of Open Access Journals (Sweden)

    Likun Wang

    2016-01-01

    Full Text Available Given the fact that Cross-track Infrared Sounder (CrIS and the Visible Infrared Imaging Radiometer Suite (VIIRS are currently onboard the Suomi National Polar-orbiting Partnership (Suomi NPP satellite and will continue to be carried on the same platform as future Joint Polar Satellite System (JPSS satellites for the next decade, it is desirable to develop a fast and accurate collocation scheme to collocate VIIRS products and measurements with CrIS for applications that rely on combining measurements from two sensors such as inter-calibration, geolocation assessment, and cloud detection. In this study, an accurate and fast collocation method to collocate VIIRS measurements within CrIS instantaneous field of view (IFOV directly based on line-of-sight (LOS pointing vectors is developed and discussed in detail. We demonstrate that this method is not only accurate and precise from a mathematical perspective, but also easy to implement computationally. More importantly, with optimization, this method is very fast and efficient and thus can meet operational requirements. Finally, this collocation method can be extended to a wide variety of sensors on different satellite platforms.

  12. Methodology for Quantitative Characterization of Fluorophore Photoswitching to Predict Superresolution Microscopy Image Quality

    Science.gov (United States)

    Bittel, Amy M.; Nickerson, Andrew; Saldivar, Isaac S.; Dolman, Nick J.; Nan, Xiaolin; Gibbs, Summer L.

    2016-07-01

    Single-molecule localization microscopy (SMLM) image quality and resolution strongly depend on the photoswitching properties of fluorophores used for sample labeling. Development of fluorophores with optimized photoswitching will considerably improve SMLM spatial and spectral resolution. Currently, evaluating fluorophore photoswitching requires protein-conjugation before assessment mandating specific fluorophore functionality, which is a major hurdle for systematic characterization. Herein, we validated polyvinyl alcohol (PVA) as a single-molecule environment to efficiently quantify the photoswitching properties of fluorophores and identified photoswitching properties predictive of quality SMLM images. We demonstrated that the same fluorophore photoswitching properties measured in PVA films and using antibody adsorption, a protein-conjugation environment analogous to labeled cells, were significantly correlated to microtubule width and continuity, surrogate measures of SMLM image quality. Defining PVA as a fluorophore photoswitching screening platform will facilitate SMLM fluorophore development and optimal image buffer assessment through facile and accurate photoswitching property characterization, which translates to SMLM fluorophore imaging performance.

  13. Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography

    Energy Technology Data Exchange (ETDEWEB)

    Voisin, Sophie; Tourassi, Georgia D. [Biomedical Science and Engineering Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Pinto, Frank [School of Engineering, Science, and Technology, Virginia State University, Petersburg, Virginia 23806 (United States); Morin-Ducote, Garnetta; Hudson, Kathleen B. [Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, Tennessee 37920 (United States)

    2013-10-15

    Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.

  14. Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

    Energy Technology Data Exchange (ETDEWEB)

    Voisin, Sophie [ORNL; Pinto, Frank M [ORNL; Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Hudson, Kathy [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL

    2013-01-01

    Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADs images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.

  15. Déjà vu: Motion Prediction in Static Images

    NARCIS (Netherlands)

    Pintea, S.L.; van Gemert, J.C.; Smeulders, A.W.M.

    2014-01-01

    This paper proposes motion prediction in single still images by learning it from a set of videos. The building assumption is that similar motion is characterized by similar appearance. The proposed method learns local motion patterns given a specific appearance and adds the predicted motion in a num

  16. High IFIT1 expression predicts improved clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.

    Science.gov (United States)

    Zhang, Jin-Feng; Chen, Yao; Lin, Guo-Shi; Zhang, Jian-Dong; Tang, Wen-Long; Huang, Jian-Huang; Chen, Jin-Shou; Wang, Xing-Fu; Lin, Zhi-Xiong

    2016-06-01

    Interferon-induced protein with tetratricopeptide repeat 1 (IFIT1) plays a key role in growth suppression and apoptosis promotion in cancer cells. Interferon was reported to induce the expression of IFIT1 and inhibit the expression of O-6-methylguanine-DNA methyltransferase (MGMT).This study aimed to investigate the expression of IFIT1, the correlation between IFIT1 and MGMT, and their impact on the clinical outcome in newly diagnosed glioblastoma. The expression of IFIT1 and MGMT and their correlation were investigated in the tumor tissues from 70 patients with newly diagnosed glioblastoma. The effects on progression-free survival and overall survival were evaluated. Of 70 cases, 57 (81.4%) tissue samples showed high expression of IFIT1 by immunostaining. The χ(2) test indicated that the expression of IFIT1 and MGMT was negatively correlated (r = -0.288, P = .016). Univariate and multivariate analyses confirmed high IFIT1 expression as a favorable prognostic indicator for progression-free survival (P = .005 and .017) and overall survival (P = .001 and .001), respectively. Patients with 2 favorable factors (high IFIT1 and low MGMT) had an improved prognosis as compared with others. The results demonstrated significantly increased expression of IFIT1 in newly diagnosed glioblastoma tissue. The negative correlation between IFIT1 and MGMT expression may be triggered by interferon. High IFIT1 can be a predictive biomarker of favorable clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.

  17. Accurate prediction of retention in hydrophilic interaction chromatography (HILIC) by back calculation of high pressure liquid chromatography (HPLC) gradient profiles.

    Science.gov (United States)

    Wang, Nu; Boswell, Paul G

    2017-08-26

    Gradient retention times are difficult to project from the underlying retention factor (k) vs. solvent composition (φ) relationships. A major reason for this difficulty is that gradients produced by HPLC pumps are imperfect - gradient delay, gradient dispersion, and solvent mis-proportioning are all difficult to account for in calculations. However, we recently showed that a gradient "back-calculation" methodology can measure these imperfections and take them into account. In RPLC, when the back-calculation methodology was used, error in projected gradient retention times is as low as could be expected based on repeatability in the k vs. φ relationships. HILIC, however, presents a new challenge: the selectivity of HILIC columns drift strongly over time. Retention is repeatable in short time, but selectivity frequently drifts over the course of weeks. In this study, we set out to understand if the issue of selectivity drift can be avoid by doing our experiments quickly, and if there any other factors that make it difficult to predict gradient retention times from isocratic k vs. φ relationships when gradient imperfections are taken into account with the back-calculation methodology. While in past reports, the accuracy of retention projections was >5%, the back-calculation methodology brought our error down to ∼1%. This result was 6-43 times more accurate than projections made using ideal gradients and 3-5 times more accurate than the same retention projections made using offset gradients (i.e., gradients that only took gradient delay into account). Still, the error remained higher in our HILIC projections than in RPLC. Based on the shape of the back-calculated gradients, we suspect the higher error is a result of prominent gradient distortion caused by strong, preferential water uptake from the mobile phase into the stationary phase during the gradient - a factor our model did not properly take into account. It appears that, at least with the stationary phase

  18. Cone beam computed tomography: An accurate imaging technique in comparison with orthogonal portal imaging in intensity-modulated radiotherapy for prostate cancer

    Directory of Open Access Journals (Sweden)

    Om Prakash Gurjar

    2016-03-01

    Full Text Available Purpose: Various factors cause geometric uncertainties during prostate radiotherapy, including interfractional and intrafractional patient motions, organ motion, and daily setup errors. This may lead to increased normal tissue complications when a high dose to the prostate is administered. More-accurate treatment delivery is possible with daily imaging and localization of the prostate. This study aims to measure the shift of the prostate by using kilovoltage (kV cone beam computed tomography (CBCT after position verification by kV orthogonal portal imaging (OPI.Methods: Position verification in 10 patients with prostate cancer was performed by using OPI followed by CBCT before treatment delivery in 25 sessions per patient. In each session, OPI was performed by using an on-board imaging (OBI system and pelvic bone-to-pelvic bone matching was performed. After applying the noted shift by using OPI, CBCT was performed by using the OBI system and prostate-to-prostate matching was performed. The isocenter shifts along all three translational directions in both techniques were combined into a three-dimensional (3-D iso-displacement vector (IDV.Results: The mean (SD IDV (in centimeters calculated during the 250 imaging sessions was 0.931 (0.598, median 0.825 for OPI and 0.515 (336, median 0.43 for CBCT, p-value was less than 0.0001 which shows extremely statistical significant difference.Conclusion: Even after bone-to-bone matching by using OPI, a significant shift in prostate was observed on CBCT. This study concludes that imaging with CBCT provides a more accurate prostate localization than the OPI technique. Hence, CBCT should be chosen as the preferred imaging technique.

  19. Accurate segmentation of leukocyte in blood cell images using Atanassov's intuitionistic fuzzy and interval Type II fuzzy set theory.

    Science.gov (United States)

    Chaira, Tamalika

    2014-06-01

    In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yager's intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.

  20. Integration of multi-modality imaging for accurate 3D reconstruction of human coronary arteries in vivo

    Science.gov (United States)

    Giannoglou, George D.; Chatzizisis, Yiannis S.; Sianos, George; Tsikaderis, Dimitrios; Matakos, Antonis; Koutkias, Vassilios; Diamantopoulos, Panagiotis; Maglaveras, Nicos; Parcharidis, George E.; Louridas, George E.

    2006-12-01

    In conventional intravascular ultrasound (IVUS)-based three-dimensional (3D) reconstruction of human coronary arteries, IVUS images are arranged linearly generating a straight vessel volume. However, with this approach real vessel curvature is neglected. To overcome this limitation an imaging method was developed based on integration of IVUS and biplane coronary angiography (BCA). In 17 coronary arteries from nine patients, IVUS and BCA were performed. From each angiographic projection, a single end-diastolic frame was selected and in each frame the IVUS catheter was interactively detected for the extraction of 3D catheter path. Ultrasound data was obtained with a sheath-based catheter and recorded on S-VHS videotape. S-VHS data was digitized and lumen and media-adventitia contours were semi-automatically detected in end-diastolic IVUS images. Each pair of contours was aligned perpendicularly to the catheter path and rotated in space by implementing an algorithm based on Frenet-Serret rules. Lumen and media-adventitia contours were interpolated through generation of intermediate contours creating a real 3D lumen and vessel volume, respectively. The absolute orientation of the reconstructed lumen was determined by back-projecting it onto both angiographic planes and comparing the projected lumen with the actual angiographic lumen. In conclusion, our method is capable of performing rapid and accurate 3D reconstruction of human coronary arteries in vivo. This technique can be utilized for reliable plaque morphometric, geometrical and hemodynamic analyses.

  1. Improving full-cardiac cycle strain estimation from tagged CMR by accurate modeling of 3D image appearance characteristics

    Directory of Open Access Journals (Sweden)

    Matt Nitzken

    2016-03-01

    Full Text Available To improve the tagged cardiac magnetic resonance (CMR image analysis, we propose a 3D (2D space + 1D time energy minimization framework, based on learning first- and second-order visual appearance models from voxel intensities. The former model approximates the marginal empirical distribution of intensities with two linear combinations of discrete Gaussians (LCDG. The second-order model considers an image of a sample from a translation–rotation invariant 3D Markov–Gibbs random field (MGRF with multiple pairwise spatiotemporal interactions within and between adjacent temporal frames. Abilities of the framework to accurately recover noise-corrupted strain slopes were experimentally evaluated and validated on 3D geometric phantoms and independently on in vivo data. In multiple noise and motion conditions, the proposed method outperformed comparative image filtering in restoring strain curves and reliably improved HARP strain tracking during the entirety of the cardiac cycle. According to these results, our framework can augment popular spectral domain techniques, such as HARP, by optimizing the spectral domain characteristics and thereby providing more reliable estimates of strain parameters.

  2. Predicting rhesus monkey eye movements during natural-image search.

    Science.gov (United States)

    Segraves, Mark A; Kuo, Emory; Caddigan, Sara; Berthiaume, Emily A; Kording, Konrad P

    2017-03-01

    There are three prominent factors that can predict human visual-search behavior in natural scenes: the distinctiveness of a location (salience), similarity to the target (relevance), and features of the environment that predict where the object might be (context). We do not currently know how well these factors are able to predict macaque visual search, which matters because it is arguably the most popular model for asking how the brain controls eye movements. Here we trained monkeys to perform the pedestrian search task previously used for human subjects. Salience, relevance, and context models were all predictive of monkey eye fixations and jointly about as precise as for humans. We attempted to disrupt the influence of scene context on search by testing the monkeys with an inverted set of the same images. Surprisingly, the monkeys were able to locate the pedestrian at a rate similar to that for upright images. The best predictions of monkey fixations in searching inverted images were obtained by rotating the results of the model predictions for the original image. The fact that the same models can predict human and monkey search behavior suggests that the monkey can be used as a good model for understanding how the human brain enables natural-scene search.

  3. SU-E-J-134: An Augmented-Reality Optical Imaging System for Accurate Breast Positioning During Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Nazareth, D; Malhotra, H; French, S [Roswell Park Cancer Institute, Buffalo, NY (United States); Hoffmann, K [Neurosurgery at SUNY at Buffalo, Buffalo, NY (United States); Merrow, C [Bassett Healthcare, Oneonta, NY (United States)

    2014-06-01

    Purpose: Breast radiotherapy, particularly electronic compensation, may involve large dose gradients and difficult patient positioning problems. We have developed a simple self-calibrating augmented-reality system, which assists in accurately and reproducibly positioning the patient, by displaying her live image from a single camera superimposed on the correct perspective projection of her 3D CT data. Our method requires only a standard digital camera capable of live-view mode, installed in the treatment suite at an approximately-known orientation and position (rotation R; translation T). Methods: A 10-sphere calibration jig was constructed and CT imaged to provide a 3D model. The (R,T) relating the camera to the CT coordinate system were determined by acquiring a photograph of the jig and optimizing an objective function, which compares the true image points to points calculated with a given candidate R and T geometry. Using this geometric information, 3D CT patient data, viewed from the camera's perspective, is plotted using a Matlab routine. This image data is superimposed onto the real-time patient image, acquired by the camera, and displayed using standard live-view software. This enables the therapists to view both the patient's current and desired positions, and guide the patient into assuming the correct position. The method was evaluated using an in-house developed bolus-like breast phantom, mounted on a supporting platform, which could be tilted at various angles to simulate treatment-like geometries. Results: Our system allowed breast phantom alignment, with an accuracy of about 0.5 cm and 1 ± 0.5 degree. Better resolution could be possible using a camera with higher-zoom capabilities. Conclusion: We have developed an augmented-reality system, which combines a perspective projection of a CT image with a patient's real-time optical image. This system has the potential to improve patient setup accuracy during breast radiotherapy, and could

  4. Defining the Most Accurate Measurable Dimension(s of the Liver in Predicting Liver Volume Based on CT Volumetery and Reconstruction

    Directory of Open Access Journals (Sweden)

    Reza Saadat Mostafavi

    2010-05-01

    Full Text Available Background/Objective: The presence of liver volume has a great effect on diagnosis and management of different diseases such as lymphoproliferative conditions. "nPatients and Methods: Abdominal CT scan of 100 patients without any findings for liver disease (in history and imaging was subjected to volumetry and reconstruction. Along with the liver volume, in axial series, the AP diameter of the left lobe (in midline and right lobe (mid-clavicular and lateral maximum diameter of the liver in the mid-axiliary line and maximum diameter to IVC were calculated. In the coronal mid-axillary and sagittal mid-clavicular plane, maximum superior-inferior dimensions were calculated with their various combinations (multiplying. Regression analysis between dimensions and volume were performed. "nResults: The most accurate combination was the superior inferior sagittal dimension multiplied by AP diameter of the right lobe (R squared 0.78, P-value<0.001 and the most solitary dimension was the lateral dimension to IVC in the axial plane (R squared 0.57, P-value<0.001 with an interval of 9-11cm for 68% of normal. "nConclusion: We recommend the lateral maximum diameter of liver from surface to IVC in the axial plane in ultrasound for liver volume prediction with an interval of 9-11cm for 68% of normal. Out of this range is regarded as abnormal.

  5. Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

    Science.gov (United States)

    Xiao, Rui; Gao, Junbin; Bossomaier, Terry

    2016-01-01

    A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression. PMID:27695102

  6. Submillimetre wave imaging and security: imaging performance and prediction

    Science.gov (United States)

    Appleby, R.; Ferguson, S.

    2016-10-01

    Within the European Commission Seventh Framework Programme (FP7), CONSORTIS (Concealed Object Stand-Off Real-Time Imaging for Security) has designed and is fabricating a stand-off system operating at sub-millimetre wave frequencies for the detection of objects concealed on people. This system scans people as they walk by the sensor. This paper presents the top level system design which brings together both passive and active sensors to provide good performance. The passive system operates in two bands between 100 and 600GHz and is based on a cryogen free cooled focal plane array sensor whilst the active system is a solid-state 340GHz radar. A modified version of OpenFX was used for modelling the passive system. This model was recently modified to include realistic location-specific skin temperature and to accept animated characters wearing up to three layers of clothing that move dynamically, such as those typically found in cinematography. Targets under clothing have been modelled and the performance simulated. The strengths and weaknesses of this modelling approach are discussed.

  7. Hyperspectral imaging-based spatially-resolved technique for accurate measurement of the optical properties of horticultural products

    Science.gov (United States)

    Cen, Haiyan

    Hyperspectral imaging-based spatially-resolved technique is promising for determining the optical properties and quality attributes of horticultural and food products. However, considerable challenges still exist for accurate determination of spectral absorption and scattering properties from intact horticultural products. The objective of this research was, therefore, to develop and optimize hyperspectral imaging-based spatially-resolved technique for accurate measurement of the optical properties of horticultural products. Monte Carlo simulations and experiments for model samples of known optical properties were performed to optimize the inverse algorithm of a single-layer diffusion model and the optical designs, for extracting the absorption (micro a) and reduced scattering (micros') coefficients from spatially-resolved reflectance profiles. The logarithm and integral data transformation and the relative weighting methods were found to greatly improve the parameter estimation accuracy with the relative errors of 10.4%, 10.7%, and 11.4% for micro a, and 6.6%, 7.0%, and 7.1% for micros', respectively. More accurate measurements of optical properties were obtained when the light beam was of Gaussian type with the diameter of less than 1 mm, and the minimum and maximum source-detector distances were 1.5 mm and 10--20 transport mean free paths, respectively. An optical property measuring prototype was built, based on the optimization results, and evaluated for automatic measurement of absorption and reduced scattering coefficients for the wavelengths of 500--1,000 nm. The instrument was used to measure the optical properties, and assess quality/maturity, of 500 'Redstar' peaches and 1039 'Golden Delicious' (GD) and 1040 'Delicious' (RD) apples. A separate study was also conducted on confocal laser scanning and scanning electron microscopic image analysis and compression test of fruit tissue specimens to measure the structural and mechanical properties of 'Golden

  8. Flare Prediction Using Photospheric and Coronal Image Data

    Science.gov (United States)

    Jonas, E.; Shankar, V.; Bobra, M.; Recht, B.

    2016-12-01

    We attempt to forecast M-and X-class solar flares using a machine-learning algorithm and five years of image data from both the Helioseismic and Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) instruments aboard the Solar Dynamics Observatory. HMI is the first instrument to continuously map the full-disk photospheric vector magnetic field from space (Schou et al., 2012). The AIA instrument maps the transition region and corona using various ultraviolet wavelengths (Lemen et al., 2012). HMI and AIA data are taken nearly simultaneously, providing an opportunity to study the entire solar atmosphere at a rapid cadence. Most flare forecasting efforts described in the literature use some parameterization of solar data - typically of the photospheric magnetic field within active regions. These numbers are considered to capture the information in any given image relevant to predicting solar flares. In our approach, we use HMI and AIA images of solar active regions and a deep convolutional kernel network to predict solar flares. This is effectively a series of shallow-but-wide random convolutional neural networks stacked and then trained with a large-scale block-weighted least squares solver. This algorithm automatically determines which patterns in the image data are most correlated with flaring activity and then uses these patterns to predict solar flares. Using the recently-developed KeystoneML machine learning framework, we construct a pipeline to process millions of images in a few hours on commodity cloud computing infrastructure. This is the first time vector magnetic field images have been combined with coronal imagery to forecast solar flares. This is also the first time such a large dataset of solar images, some 8.5 terabytes of images that together capture over 3000 active regions, has been used to forecast solar flares. We evaluate our method using various flare prediction windows defined in the literature (e.g. Ahmed et al., 2013) and a novel per

  9. Code-excited linear predictive coding of multispectral MR images

    Science.gov (United States)

    Hu, Jian-Hong; Wang, Yao; Cahill, Patrick

    1996-02-01

    This paper reports a multispectral code excited linear predictive coding method for the compression of well-registered multispectral MR images. Different linear prediction models and the adaptation schemes have been compared. The method which uses forward adaptive autoregressive (AR) model has proven to achieve a good compromise between performance, complexity and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over non-overlapping square macroblocks. Each macro-block is further divided into several micro-blocks and, the best excitation signals for each microblock are determined through an analysis-by-synthesis procedure. To satisfy the high quality requirement for medical images, the error between the original images and the synthesized ones are further specified using a vector quantizer. The MFCELP method has been applied to 26 sets of clinical MR neuro images (20 slices/set, 3 spectral bands/slice, 256 by 256 pixels/image, 12 bits/pixel). It provides a significant improvement over the discrete cosine transform (DCT) based JPEG method, a wavelet transform based embedded zero-tree wavelet (EZW) coding method, as well as the MSARMA method we developed before.

  10. 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…

  11. Predicting three-dimensional patellofemoral kinematics from static imaging-based alignment measures.

    Science.gov (United States)

    Freedman, Benjamin R; Sheehan, Frances T

    2013-03-01

    Patellofemoral pain syndrome causes significant discomfort and disability among much of the general population. Despite recent breakthroughs in dynamic three-dimensional imaging technologies to assess pathological patellofemoral motion, such tools remain costly for clinical diagnostics applications. Thus, this study investigated whether three-dimensional patellofemoral kinematics could be predicted from routine two-dimensional static measures of patellofemoral joint alignment quantified from magnetic resonance imaging (MRI) data acquired in full knee extension. Twenty-six volunteers clinically diagnosed with patellofemoral pain (19 F/7 M, 25.9 ± 11.1 years) and 26 control subjects (19 F/7 M, 25.3 ± 7.7 years) were included in this IRB-approved study. Static three-dimensional sagittal T1-weighted gradient recall echo and dynamic MRI scans were acquired. For the dynamic image acquisition, subjects cyclically flexed and extended their knee (at 30 cycles/min) while a full cine-phase contrast MRI set (24 time frames of anatomic images and x-, y-, and z-velocity images) was acquired. From these data, static measures of patellofemoral alignment and three-dimensional patellofemoral kinematics were derived. Single and multiple regressions between static and kinematic variables were evaluated. Although shown reliable, the static MRI measures could only partially predict patellofemoral kinematics, with r(2) -values ranging from 16% to 77%. This makes it imperitave that the current precise, accurate, 3D, dynamic imaging techniques be translated into clinical tools.

  12. Accurate, precise modeling of cell proliferation kinetics from time-lapse imaging and automated image analysis of agar yeast culture arrays

    Directory of Open Access Journals (Sweden)

    Zhao Lue

    2007-01-01

    Full Text Available Abstract Background Genome-wide mutant strain collections have increased demand for high throughput cellular phenotyping (HTCP. For example, investigators use HTCP to investigate interactions between gene deletion mutations and additional chemical or genetic perturbations by assessing differences in cell proliferation among the collection of 5000 S. cerevisiae gene deletion strains. Such studies have thus far been predominantly qualitative, using agar cell arrays to subjectively score growth differences. Quantitative systems level analysis of gene interactions would be enabled by more precise HTCP methods, such as kinetic analysis of cell proliferation in liquid culture by optical density. However, requirements for processing liquid cultures make them relatively cumbersome and low throughput compared to agar. To improve HTCP performance and advance capabilities for quantifying interactions, YeastXtract software was developed for automated analysis of cell array images. Results YeastXtract software was developed for kinetic growth curve analysis of spotted agar cultures. The accuracy and precision for image analysis of agar culture arrays was comparable to OD measurements of liquid cultures. Using YeastXtract, image intensity vs. biomass of spot cultures was linearly correlated over two orders of magnitude. Thus cell proliferation could be measured over about seven generations, including four to five generations of relatively constant exponential phase growth. Spot area normalization reduced the variation in measurements of total growth efficiency. A growth model, based on the logistic function, increased precision and accuracy of maximum specific rate measurements, compared to empirical methods. The logistic function model was also more robust against data sparseness, meaning that less data was required to obtain accurate, precise, quantitative growth phenotypes. Conclusion Microbial cultures spotted onto agar media are widely used for genotype

  13. Three-dimensional ultrasound image-guided robotic system for accurate microwave coagulation of malignant liver tumours.

    Science.gov (United States)

    Xu, Jing; Jia, Zhen-zhong; Song, Zhang-jun; Yang, Xiang-dong; Chen, Ken; Liang, Ping

    2010-09-01

    The further application of conventional ultrasound (US) image-guided microwave (MW) ablation of liver cancer is often limited by two-dimensional (2D) imaging, inaccurate needle placement and the resulting skill requirement. The three-dimensional (3D) image-guided robotic-assisted system provides an appealing alternative option, enabling the physician to perform consistent, accurate therapy with improved treatment effectiveness. Our robotic system is constructed by integrating an imaging module, a needle-driven robot, a MW thermal field simulation module, and surgical navigation software in a practical and user-friendly manner. The robot executes precise needle placement based on the 3D model reconstructed from freehand-tracked 2D B-scans. A qualitative slice guidance method for fine registration is introduced to reduce the placement error caused by target motion. By incorporating the 3D MW specific absorption rate (SAR) model into the heat transfer equation, the MW thermal field simulation module determines the MW power level and the coagulation time for improved ablation therapy. Two types of wrists are developed for the robot: a 'remote centre of motion' (RCM) wrist and a non-RCM wrist, which is preferred in real applications. The needle placement accuracies were < 3 mm for both wrists in the mechanical phantom experiment. The target accuracy for the robot with the RCM wrist was improved to 1.6 +/- 1.0 mm when real-time 2D US feedback was used in the artificial-tissue phantom experiment. By using the slice guidance method, the robot with the non-RCM wrist achieved accuracy of 1.8 +/- 0.9 mm in the ex vivo experiment; even target motion was introduced. In the thermal field experiment, a 5.6% relative mean error was observed between the experimental coagulated neurosis volume and the simulation result. The proposed robotic system holds promise to enhance the clinical performance of percutaneous MW ablation of malignant liver tumours. Copyright 2010 John Wiley

  14. Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

    Directory of Open Access Journals (Sweden)

    Iris I A Groen

    Full Text Available The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis. Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

  15. Does magnetic resonance imaging predict future low back pain?

    DEFF Research Database (Denmark)

    Steffens, D; Hancock, M J; Maher, C G;

    2014-01-01

    Magnetic resonance imaging (MRI) has the potential to identify pathology responsible for low back pain (LBP). However, the importance of findings on MRI remains controversial. We aimed to systematically review whether MRI findings of the lumbar spine predict future LBP in different samples...

  16. Image processing system performance prediction and product quality evaluation

    Science.gov (United States)

    Stein, E. K.; Hammill, H. B. (Principal Investigator)

    1976-01-01

    The author has identified the following significant results. A new technique for image processing system performance prediction and product quality evaluation was developed. It was entirely objective, quantitative, and general, and should prove useful in system design and quality control. The technique and its application to determination of quality control procedures for the Earth Resources Technology Satellite NASA Data Processing Facility are described.

  17. Lossless predictive coding for images with Bayesian treatment.

    Science.gov (United States)

    Liu, Jing; Zhai, Guangtao; Yang, Xiaokang; Chen, Li

    2014-12-01

    Adaptive predictor has long been used for lossless predictive coding of images. Most of existing lossless predictive coding techniques mainly focus on suitability of prediction model for training set with the underlying assumption of local consistency, which may not hold well on object boundaries and cause large predictive error. In this paper, we propose a novel approach based on the assumption that local consistency and patch redundancy exist simultaneously in natural images. We derive a family of linear models and design a new algorithm to automatically select one suitable model for prediction. From the Bayesian perspective, the model with maximum posterior probability is considered as the best. Two types of model evidence are included in our algorithm. One is traditional training evidence, which represents the models’ suitability for current pixel under the assumption of local consistency. The other is target evidence, which is proposed to express the preference for different models from the perspective of patch redundancy. It is shown that the fusion of training evidence and target evidence jointly exploits the benefits of local consistency and patch redundancy. As a result, our proposed predictor is more suitable for natural images with textures and object boundaries. Comprehensive experiments demonstrate that the proposed predictor achieves higher efficiency compared with the state-of-the-art lossless predictors.

  18. Accurate particle speed prediction by improved particle speed measurement and 3-dimensional particle size and shape characterization technique

    DEFF Research Database (Denmark)

    Cernuschi, Federico; Rothleitner, Christian; Clausen, Sønnik

    2017-01-01

    Accurate particle mass and velocity measurement is needed for interpreting test results in erosion tests of materials and coatings. The impact and damage of a surface is influenced by the kinetic energy of a particle, i.e. particle mass and velocity. Particle mass is usually determined with optic...

  19. Formation of NO from N2/O2 mixtures in a flow reactor: Toward an accurate prediction of thermal NO

    DEFF Research Database (Denmark)

    Abian, Maria; Alzueta, Maria U.; Glarborg, Peter

    2015-01-01

    We have conducted flow reactor experiments for NO formation from N2/O2 mixtures at high temperatures and atmospheric pressure, controlling accurately temperature and reaction time. Under these conditions, atomic oxygen equilibrates rapidly with O2. The experimental results were interpreted......, is recommended for use in kinetic modeling....

  20. Contribution of diffusion-weighted MR imaging for predicting benignity of complex adnexal masses

    Energy Technology Data Exchange (ETDEWEB)

    Thomassin-Naggara, Isabelle [Hopital Tenon, Assistance Publique Hopitaux de Pariss, Department of Radiology, Paris (France); Universite Rene Descartes, LRI-EA4062, Paris (France); Darai, Emile [Hopital Tenon, Assistance Publique Hopitaux de Pariss, Department of Gynecology-Obstetrics, Paris (France); Cuenod, Charles A.; Fournier, Laure [Universite Rene Descartes, LRI-EA4062, Paris (France); Hopital Europeen Georges Pompidou (HEGP), Assistance Publique Hopitaux de Paris, Department of Radiology, Paris (France); Toussaint, Irwin; Marsault, Claude; Bazot, Marc [Hopital Tenon, Assistance Publique Hopitaux de Pariss, Department of Radiology, Paris (France)

    2009-06-15

    The purpose of this study was to prospectively assess the contribution of diffusion-weighted MR imaging (DWI) for characterizing complex adnexal masses. Seventy-seven women (22-87 years old) with complex adnexal masses (30 benign and 47 malignant) underwent MR imaging including DWI before surgery. Conventional morphological MR imaging criteria were recorded in addition to b{sub 1,000} signal intensity and apparent diffusion coefficient (ADC) measurements of cystic and solid components. Positive likelihood ratios (PLR) were calculated for predicting benignity and malignancy. The most significant criteria for predicting benignity were low b{sub 1,000} signal intensity within the solid component (PLR = 10.9), low T2 signal intensity within the solid component (PLR = 5.7), absence of solid portion (PLR = 3.1), absence of ascites or peritoneal implants (PLR = 2.3) and absence of papillary projections (PLR = 2.3). ADC measurements did not contribute to differentiating benign from malignant adnexal masses. All masses that displayed simultaneously low signal intensity within the solid component on T2-weighted and on b{sub 1,000} diffusion-weighted images were benign. Alternatively, the presence of a solid component with intermediate T2 signal and high b{sub 1,000} signal intensity was associated with a PLR of 4.5 for a malignant adnexal tumour. DWI signal intensity is an accurate tool for predicting benignity of complex adnexal masses. (orig.)

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

    Science.gov (United States)

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

    2015-07-01

    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 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.

  2. Random forest algorithm yields accurate quantitative prediction models of benthic light at intertidal sites affected by toxic Lyngbya majuscula blooms

    NARCIS (Netherlands)

    Kehoe, M.J.; O’ Brien, K.; Grinham, A.; Rissik, D.; Ahern, K.S.; Maxwell, P.

    2012-01-01

    It is shown that targeted high frequency monitoring and modern machine learning methods lead to highly predictive models of benthic light flux. A state-of-the-art machine learning technique was used in conjunction with a high frequency data set to calibrate and test predictive benthic lights models

  3. Random forest algorithm yields accurate quantitative prediction models of benthic light at intertidal sites affected by toxic Lyngbya majuscula blooms

    NARCIS (Netherlands)

    Kehoe, M.J.; O’ Brien, K.; Grinham, A.; Rissik, D.; Ahern, K.S.; Maxwell, P.

    2012-01-01

    It is shown that targeted high frequency monitoring and modern machine learning methods lead to highly predictive models of benthic light flux. A state-of-the-art machine learning technique was used in conjunction with a high frequency data set to calibrate and test predictive benthic lights models

  4. Accurate spike time prediction from LFP in monkey visual cortex: A non-linear system identification approach

    NARCIS (Netherlands)

    Kostoglou, K.; Hadjipapas, A.; Lowet, E.; Roberts, M.; de Weerd, P.; Mitsis, G.D.

    2014-01-01

    Aims: The relationship between collective population activity (LFP) and spikes underpins network computation, yet it remains poorly understood. Previous studies utilized pre-defined LFP features to predict spiking from simultaneously recorded LFP, and have reported good prediction of spike bursts bu

  5. Prediction of Solar Flares Using Unique Signatures of Magnetic Field Images

    Science.gov (United States)

    Raboonik, Abbas; Safari, Hossein; Alipour, Nasibe; Wheatland, Michael S.

    2017-01-01

    Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and topology of solar magnetic fields. A new method for predicting large (M- and X-class) flares is presented, which uses machine learning methods applied to the Zernike moments (ZM) of magnetograms observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for a period of six years from 2010 June 2 to 2016 August 1. Magnetic field images consisting of the radial component of the magnetic field are converted to finite sets of ZMs and fed to the support vector machine classifier. ZMs have the capability to elicit unique features from any 2D image, which may allow more accurate classification. The results indicate whether an arbitrary active region has the potential to produce at least one large flare. We show that the majority of large flares can be predicted within 48 hr before their occurrence, with only 10 false negatives out of 385 flaring active region magnetograms and 21 false positives out of 179 non-flaring active region magnetograms. Our method may provide a useful tool for the prediction of solar flares, which can be employed alongside other forecasting methods.

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

    Science.gov (United States)

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

    2017-03-01

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

  7. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.

    Science.gov (United States)

    Kohonen, Pekka; Parkkinen, Juuso A; Willighagen, Egon L; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafström, Roland C

    2017-07-03

    Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion'-concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 10(8) data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.

  8. Predictive images of postoperative levator resection outcome using image processing software

    Directory of Open Access Journals (Sweden)

    Mawatari Y

    2016-09-01

    Full Text Available Yuki Mawatari,1 Mikiko Fukushima2 1Igo Ophthalmic Clinic, Kagoshima, 2Department of Ophthalmology, Faculty of Life Science, Kumamoto University, Chuo-ku, Kumamoto, Japan Purpose: This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection.Methods: Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection. Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop®. Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery.Results: Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2% were satisfied with their postoperative appearances, and 55 patients (84.8% positively responded to the usefulness of processed images to predict postoperative appearance.Conclusion: Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery. Keywords: levator resection, blepharoptosis, image processing, Adobe Photoshop® 

  9. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    Directory of Open Access Journals (Sweden)

    Liqi Li

    Full Text Available Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM in conjunction with integrated features from position-specific score matrix (PSSM, PROFEAT and Gene Ontology (GO. A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  10. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    Science.gov (United States)

    Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  11. Accurately Predicting the Density and Hydrostatic Compression of Hexahydro-1,3,5-Trinitro-1,3,5-Triazine from First Principles

    Institute of Scientific and Technical Information of China (English)

    SONG HuarJie; HUANG Feng-Lei

    2011-01-01

    @@ We predict the densities of crystalline hexahydro-1,3,5-trinitro-1,3,5-triazine(RDX)by introducing a factor of(1+1.5×10(-4)T)into the wavefunction-based potential of RDX constructed from first principles using the symmetry-adapted perturbation theory and the Williams-Stone-Misquitta method.The predicted values are within an accuracy of 1%of the density from O to 430K and closely reproduced the RDX densities under hydrostatic compression.This work heralds a promising approach to predicting accurately the densities of high explosives at temperatures and pressures to which they are often subjected,which is a long-standing issue in the field of energetic materials.%We predict the densities of crystalline hexahydro-l,3,5-trinitro-l,3,5-triazine (RDX) by introducing a factor of (1+1.5 x 10~* T) into the wavefunction-based potential of RDX constructed from first principles using the symmetry-adapted perturbation theory and the Williams-Stone-Misquitta method. The predicted values are within an accuracy of 1% of the density from 0 to 430 K and closely reproduced the RDX densities under hydrostatic compression. This work heralds a promising approach to predicting accurately the densities of high explosives at temperatures and pressures to which they are often subjected, which is a long-standing issue in the Beld of energetic materials.

  12. Accurate and computationally efficient prediction of thermochemical properties of biomolecules using the generalized connectivity-based hierarchy.

    Science.gov (United States)

    Sengupta, Arkajyoti; Ramabhadran, Raghunath O; Raghavachari, Krishnan

    2014-08-14

    In this study we have used the connectivity-based hierarchy (CBH) method to derive accurate heats of formation of a range of biomolecules, 18 amino acids and 10 barbituric acid/uracil derivatives. The hierarchy is based on the connectivity of the different atoms in a large molecule. It results in error-cancellation reaction schemes that are automated, general, and can be readily used for a broad range of organic molecules and biomolecules. Herein, we first locate stable conformational and tautomeric forms of these biomolecules using an accurate level of theory (viz. CCSD(T)/6-311++G(3df,2p)). Subsequently, the heats of formation of the amino acids are evaluated using the CBH-1 and CBH-2 schemes and routinely employed density functionals or wave function-based methods. The calculated heats of formation obtained herein using modest levels of theory and are in very good agreement with those obtained using more expensive W1-F12 and W2-F12 methods on amino acids and G3 results on barbituric acid derivatives. Overall, the present study (a) highlights the small effect of including multiple conformers in determining the heats of formation of biomolecules and (b) in concurrence with previous CBH studies, proves that use of the more effective error-cancelling isoatomic scheme (CBH-2) results in more accurate heats of formation with modestly sized basis sets along with common density functionals or wave function-based methods.

  13. Can preoperative MR imaging predict optic nerve invasion of retinoblastoma?

    Energy Technology Data Exchange (ETDEWEB)

    Song, Kyoung Doo, E-mail: kdsong0308@gmail.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Eo, Hong, E-mail: rtombow@gmail.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Kim, Ji Hye, E-mail: jhkate.kim@samsung.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Yoo, So-Young, E-mail: sy1131.yoo@samsung.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of); Jeon, Tae Yeon, E-mail: hathor97.jeon@samsung.com [Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50, Ilwon-Dong, Kangnam-Ku, Seoul 135-710 (Korea, Republic of)

    2012-12-15

    Purpose: To evaluate the accuracy of pre-operative MRI for the detection of optic nerve invasion in retinoblastoma. Materials and methods: Institutional review board approval and informed consent were waived for this retrospective study. A total of 41 patients were included. Inclusion criteria were histologically proven retinoblastoma, availability of diagnostic-quality preoperative MR images acquired during the 4 weeks before surgery, unilateral retinoblastoma, and normal-sized optic nerve. Two radiologists retrospectively reviewed the MR images independently. Five imaging findings (diffuse mild optic nerve enhancement, focal strong optic nerve enhancement, optic sheath enhancement, tumor location, and tumor size) were evaluated against optic nerve invasion of retinoblastoma. The predictive performance of all MR imaging findings for optic nerve invasion was also evaluated by the receiver operating characteristic curve analysis. Results: Optic nerve invasion was histopathologically confirmed in 24% of study population (10/41). The differences in diffuse mild enhancement, focal strong enhancement, optic sheath enhancement, and tumor location between patients with optic nerve invasion and patients without optic nerve invasion were not significant. Tumor sizes were 16.1 mm (SD: 2.2 mm) and 14.9 mm (SD: 3.6 mm) in patients with and without optic nerve involvement, respectively (P = 0.444). P-Values from binary logistic regression indicated that all five imaging findings were not significant predictors of tumor invasion of optic nerve. The AUC values of all MR imaging findings for the prediction of optic nerve invasion were 0.689 (95% confidence interval: 0.499–0.879) and 0.653 (95% confidence interval: 0.445–0.861) for observer 1 and observer 2, respectively. Conclusion: Findings of MRI in patients with normal-sized optic nerves have limited usefulness in preoperatively predicting the presence of optic nerve invasion in retinoblastoma.

  14. The choice for breast cancer surgery: can women accurately predict postoperative quality of life and disease-related stigma?

    Science.gov (United States)

    Waljee, Jennifer F; Ubel, Peter A; Atisha, Dunya M; Hu, Emily S; Alderman, Amy K

    2011-09-01

    To make an informed choice, breast cancer patients facing surgery must imagine the effect of surgery on their future life experiences. However, the accuracy of patient predictions of postoperative quality of life (QoL) and disease-related stigma is not well understood. Four groups of breast cancer patients at the University of Michigan Medical Center were surveyed by mail and interview (response rate 76.3%): (1) preoperative (N = 59), (2) mastectomy (N = 146), (3) mastectomy with reconstruction (N = 250), and (4) breast conservation (N = 705). Subjects rated their QoL (1 = lowest, 100 = highest) and stigma (1 = lowest, 5 = highest) and estimated QoL and stigma associated with mastectomy alone, mastectomy with reconstruction, and breast conserving surgery (BCS). Mean scores were compared using linear regression controlling for age, race, partnered status, and income. Preoperatively, women inaccurately predicted postoperative QoL and stigma for all surgical options, particularly for mastectomy. Preoperative patients underestimated the postoperative QoL for mastectomy alone (predicted: 56.8 vs actual: 83.7; P mastectomy following reconstruction (predicted: 73.4 vs actual: 83.9; P mastectomy (predicted: 3.25 vs actual: 2.43; P women overestimated stigma related to mastectomy with reconstruction (predicted: 2.54 vs actual: 2.03; P < .001) and BCS (predicted: 1.90 vs actual: 1.76; P < .001). Predicting QoL and stigma following breast cancer surgery is challenging for patients facing a diagnosis for surgery. Identifying strategies to better inform patients of surgical outcomes can improve the decision-making process.

  15. Neural network predicts carrot volume with only three images

    Science.gov (United States)

    Hahn, Federico; Sanchez, Sergio

    1998-09-01

    A mechanism turned a vision camera around a fixed carrot taking 100 images of it. A 3D reconstruction finite element algorithm reproduced the volume using finite area triangles and morphological operations to optimize memory utilization. Volume from several carrots were calculated and correlated against real volume achieving a 98% success rate. Three images 120 degrees apart were acquired and the main features extracted. A neural network system was trained using the features, increasing the measuring speed and obtaining together with a regression algorithm an accuracy of 95% in predicting the real volume.

  16. Deep Learning Driven Visual Path Prediction From a Single Image

    Science.gov (United States)

    Huang, Siyu; Li, Xi; Zhang, Zhongfei; He, Zhouzhou; Wu, Fei; Liu, Wei; Tang, Jinhui; Zhuang, Yueting

    2016-12-01

    Capabilities of inference and prediction are significant components of visual systems. In this paper, we address an important and challenging task of them: visual path prediction. Its goal is to infer the future path for a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and motion patterns underlying video sequences. In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of the considered models. Motivated by these observations, we propose a deep learning framework which simultaneously performs deep feature learning for visual representation in conjunction with spatio-temporal context modeling. After that, we propose a unified path planning scheme to make accurate future path prediction based on the analytic results of the context models. The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scene and motion pattern, consequently improving the performance of the visual path prediction task. In order to comprehensively evaluate the model's performance on the visual path prediction task, we construct two large benchmark datasets from the adaptation of video tracking datasets. The qualitative and quantitative experimental results show that our approach outperforms the existing approaches and owns a better generalization capability.

  17. Gantry-angle resolved VMAT pretreatment verification using EPID image prediction

    Energy Technology Data Exchange (ETDEWEB)

    Woodruff, Henry C.; Rowshanfarzad, Pejman [School of Mathematical and Physical Sciences, The University of Newcastle, NSW 2308 (Australia); Fuangrod, Todsaporn [School of Electrical Engineering and Computer Science, The University of Newcastle, NSW 2308 (Australia); McCurdy, Boyd M. C. [Division of Medical Physics, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, Manitoba R3E 0V9 (Canada); Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T 2N2 (Canada); Department of Radiology, University of Manitoba, Winnipeg, Manitoba R3T 2N2 (Canada); Greer, Peter B. [School of Mathematical and Physical Sciences, The University of Newcastle, NSW 2308, Australia and Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Locked Bag 7, Hunter region Mail Centre, Newcastle, NSW 2310 (Australia)

    2013-08-15

    Purpose: Pretreatment verification of volumetric modulated arc therapy (VMAT) dose delivery with electronic portal imaging device (EPID) uses images integrated over the entire delivery or over large subarcs. This work aims to develop a new method for gantry-angle-resolved verification of VMAT dose delivery using EPID.Methods: An EPID dose prediction model was used to calculate EPID images as a function of gantry angle for eight prostate patient deliveries. EPID image frames at 7.5 frames per second were acquired during delivery via a frame-grabber system. The gantry angle for each image was encoded in kV frames which were synchronized to the MV frames. Gamma analysis results as a function of gantry angle were assessed by integrating the frames over 2° subarcs with an angle-to-agreement tolerance of 0.5° about the measured image angle.Results: The model agreed with EPID images integrated over the entire delivery with average Gamma pass-rates at 2%, 2 mm of 99.7% (10% threshold). The accuracy of the kV derived gantry angle for each image was found to be 0.1° (1 SD) using a phantom test. For the gantry-resolved analysis all Gamma pass-rates were greater than 90% at 3%, 3 mm criteria (with only two exceptions), and more than 90% had a 95% pass-rate, with an average of 97.3%. The measured gantry angle lagged behind the predicted angle by a mean of 0.3°± 0.3°, with a maximum lag of 1.3°.Conclusions: The method provides a comprehensive and highly efficient pretreatment verification of VMAT delivery using EPID. Dose delivery accuracy is assessed as a function of gantry angle to ensure accurate treatment.

  18. A hybrid solution using computational prediction and measured data to accurately determine process corrections with reduced overlay sampling

    Science.gov (United States)

    Noyes, Ben F.; Mokaberi, Babak; Mandoy, Ram; Pate, Alex; Huijgen, Ralph; McBurney, Mike; Chen, Owen

    2017-03-01

    Reducing overlay error via an accurate APC feedback system is one of the main challenges in high volume production of the current and future nodes in the semiconductor industry. The overlay feedback system directly affects the number of dies meeting overlay specification and the number of layers requiring dedicated exposure tools through the fabrication flow. Increasing the former number and reducing the latter number is beneficial for the overall efficiency and yield of the fabrication process. An overlay feedback system requires accurate determination of the overlay error, or fingerprint, on exposed wafers in order to determine corrections to be automatically and dynamically applied to the exposure of future wafers. Since current and future nodes require correction per exposure (CPE), the resolution of the overlay fingerprint must be high enough to accommodate CPE in the overlay feedback system, or overlay control module (OCM). Determining a high resolution fingerprint from measured data requires extremely dense overlay sampling that takes a significant amount of measurement time. For static corrections this is acceptable, but in an automated dynamic correction system this method creates extreme bottlenecks for the throughput of said system as new lots have to wait until the previous lot is measured. One solution is using a less dense overlay sampling scheme and employing computationally up-sampled data to a dense fingerprint. That method uses a global fingerprint model over the entire wafer; measured localized overlay errors are therefore not always represented in its up-sampled output. This paper will discuss a hybrid system shown in Fig. 1 that combines a computationally up-sampled fingerprint with the measured data to more accurately capture the actual fingerprint, including local overlay errors. Such a hybrid system is shown to result in reduced modelled residuals while determining the fingerprint, and better on-product overlay performance.

  19. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

    Science.gov (United States)

    Kohonen, Pekka; Parkkinen, Juuso A.; Willighagen, Egon L.; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafström, Roland C.

    2017-01-01

    Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a ‘big data compacting and data fusion’—concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a ‘predictive toxicogenomics space’ (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy. PMID:28671182

  20. Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

    Science.gov (United States)

    Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

    2016-03-01

    Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

  1. A time accurate prediction of the viscous flow in a turbine stage including a rotor in motion

    Science.gov (United States)

    Shavalikul, Akamol

    In this current study, the flow field in the Pennsylvania State University Axial Flow Turbine Research Facility (AFTRF) was simulated. This study examined four sets of simulations. The first two sets are for an individual NGV and for an individual rotor. The last two sets use a multiple reference frames approach for a complete turbine stage with two different interface models: a steady circumferential average approach called a mixing plane model, and a time accurate flow simulation approach called a sliding mesh model. The NGV passage flow field was simulated using a three-dimensional Reynolds Averaged Navier-Stokes finite volume solver (RANS) with a standard kappa -- epsilon turbulence model. The mean flow distributions on the NGV surfaces and endwall surfaces were computed. The numerical solutions indicate that two passage vortices begin to be observed approximately at the mid axial chord of the NGV suction surface. The first vortex is a casing passage vortex which occurs at the corner formed by the NGV suction surface and the casing. This vortex is created by the interaction of the passage flow and the radially inward flow, while the second vortex, the hub passage vortex, is observed near the hub. These two vortices become stronger towards the NGV trailing edge. By comparing the results from the X/Cx = 1.025 plane and the X/Cx = 1.09 plane, it can be concluded that the NGV wake decays rapidly within a short axial distance downstream of the NGV. For the rotor, a set of simulations was carried out to examine the flow fields associated with different pressure side tip extension configurations, which are designed to reduce the tip leakage flow. The simulation results show that significant reductions in tip leakage mass flow rate and aerodynamic loss reduction are possible by using suitable tip platform extensions located near the pressure side corner of the blade tip. The computations used realistic turbine rotor inlet flow conditions in a linear cascade arrangement

  2. Prediction of Solar Flares Using Unique Signatures of Magnetic Field Images

    CERN Document Server

    Raboonik, Abbas; Alipour, Nasibe; Wheatland, Michael S

    2016-01-01

    Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and the topology of solar magnetic fields. A new method for predicting large (M and X class) flares is presented, which uses machine learning methods applied to the Zernike moments of magnetograms observed by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) for a period of six years from 2 June 2010 to 1 August 2016. Magnetic field images consisting of the radial component of the magnetic field are converted to finite sets of Zernike moments and fed to the Support Vector Machine (SVM) classifier. Zernike moments have the capability to elicit unique features from any 2-D image, which may allow more accurate classification. The results indicate whether an arbitrary active region has the potential to produce at least one large flare. We show that the majority of large flares can be predicted within 48 hours before their occurrence, with only ...

  3. Microdosing of a Carbon-14 Labeled Protein in Healthy Volunteers Accurately Predicts Its Pharmacokinetics at Therapeutic Dosages

    NARCIS (Netherlands)

    Vlaming, M.L.; Duijn, E. van; Dillingh, M.R.; Brands, R.; Windhorst, A.D.; Hendrikse, N.H.; Bosgra, S.; Burggraaf, J.; Koning, M.C. de; Fidder, A.; Mocking, J.A.; Sandman, H.; Ligt, R.A. de; Fabriek, B.O.; Pasman, W.J.; Seinen, W.; Alves, T.; Carrondo, M.; Peixoto, C.; Peeters, P.A.; Vaes, W.H.

    2015-01-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of

  4. Microdosing of a Carbon-14 Labeled Protein in Healthy Volunteers Accurately Predicts Its Pharmacokinetics at Therapeutic Dosages

    NARCIS (Netherlands)

    Vlaming, M.L.; Duijn, E. van; Dillingh, M.R.; Brands, R.; Windhorst, A.D.; Hendrikse, N.H.; Bosgra, S.; Burggraaf, J.; Koning, M.C. de; Fidder, A.; Mocking, J.A.; Sandman, H.; Ligt, R.A. de; Fabriek, B.O.; Pasman, W.J.; Seinen, W.; Alves, T.; Carrondo, M.; Peixoto, C.; Peeters, P.A.; Vaes, W.H.

    2015-01-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of NBE

  5. Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme.

    Science.gov (United States)

    Fathi Kazerooni, Anahita; Mohseni, Meysam; Rezaei, Sahar; Bakhshandehpour, Gholamreza; Saligheh Rad, Hamidreza

    2015-02-01

    Glioblastoma multiforme (GBM) brain tumor is heterogeneous in nature, so its quantification depends on how to accurately segment different parts of the tumor, i.e. viable tumor, edema and necrosis. This procedure becomes more effective when metabolic and functional information, provided by physiological magnetic resonance (MR) imaging modalities, like diffusion-weighted-imaging (DWI) and perfusion-weighted-imaging (PWI), is incorporated with the anatomical magnetic resonance imaging (MRI). In this preliminary tumor quantification work, the idea is to characterize different regions of GBM tumors in an MRI-based semi-automatic multi-parametric approach to achieve more accurate characterization of pathogenic regions. For this purpose, three MR sequences, namely T2-weighted imaging (anatomical MR imaging), PWI and DWI of thirteen GBM patients, were acquired. To enhance the delineation of the boundaries of each pathogenic region (peri-tumoral edema, viable tumor and necrosis), the spatial fuzzy C-means algorithm is combined with the region growing method. The results show that exploiting the multi-parametric approach along with the proposed semi-automatic segmentation method can differentiate various tumorous regions with over 80 % sensitivity, specificity and dice score. The proposed MRI-based multi-parametric segmentation approach has the potential to accurately segment tumorous regions, leading to an efficient design of the pre-surgical treatment planning.

  6. Diffusion-weighted imaging predicts cognition in pediatric brain injury.

    Science.gov (United States)

    Babikian, Talin; Tong, Karen A; Galloway, Nicholas R; Freier-Randall, Mary-Catherin; Obenaus, André; Ashwal, Stephen

    2009-12-01

    Apparent diffusion coefficient maps from diffusion-weighted imaging predict gross neurologic outcome in adults with traumatic brain injury. Few studies in children have been reported, and none have used apparent diffusion coefficient maps to predict long-term (>1 year) neurocognitive outcomes. In this study, pooled regional and total brain diffusion coefficients were used to predict long-term outcomes in 17 pediatric brain injury patients. Apparent diffusion coefficient values were grouped into peripheral and deep gray and white matter, posterior fossa, and total brain. Regions of interest excluded areas that appeared abnormal on T(2)-weighted images. Apparent diffusion coefficient values from peripheral regions were inversely correlated with cognitive functioning. No significant correlations were apparent between the cognitive scores and apparent diffusion coefficient values for deep tissue or the posterior fossa. Regression analyses suggested that combined peripheral gray and white matter apparent diffusion coefficients explained 42% of the variance in the combined neurocognitive index. Peripheral gray diffusion coefficients alone explained an additional 20% of variance after accounting for clinical variables. These results suggest that obtaining apparent diffusion coefficient values, specifically from peripheral brain regions, may predict long-term outcome after pediatric brain injury. Discrepancies in the literature on this topic, as well as possible explanations, including sampling and clinical considerations, are discussed.

  7. Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade

    Directory of Open Access Journals (Sweden)

    Sonal Kothari

    2013-01-01

    Full Text Available Background: Analysis of tissue biopsy whole-slide images (WSIs depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models. Materials and Methods: We use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa and ovarian serous adenocarcinoma (OvCa - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades. Results: When compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI, 0.77 average true rate (ATR, 0.55 true positive rate (TPR, and 0.98 true negative rate (TNR; and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively. Conclusion: The proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction

  8. Deep Learning Driven Visual Path Prediction from a Single Image.

    Science.gov (United States)

    Huang, Siyu; Li, Xi; Zhang, Zhongfei; He, Zhouzhou; Wu, Fei; Liu, Wei; Tang, Jinhui; Zhuang, Yueting

    2016-09-26

    Capabilities of inference and prediction are significant components of visual systems. Visual path prediction is an important and challenging task among them, with the goal to infer the future path of a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and underlying motion patterns in video sequences. In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of models. Motivated by these observations, we propose a deep learning framework which simultaneously performs deep feature learning for visual representation in conjunction with spatio-temporal context modeling. After that, a unified path planning scheme is proposed to make accurate path prediction based on the analytic results returned by the deep context models. The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scenes and motion patterns, consequently improving the performance on visual path prediction task. In experiments, we extensively evaluate the model's performance by constructing two large benchmark datasets from the adaptation of video tracking datasets. The qualitative and quantitative experimental results show that our approach outperforms the state-of-the-art approaches and owns a better generalization capability.

  9. Assessment of global left ventricular functional parameters : analysis of every second short-axis magnetic resonance imaging slices is as accurate as analysis of consecutive slices

    NARCIS (Netherlands)

    Lubbers, Daniel D.; Willems, Tineke P.; van der Vleuten, Pieter A.; Overbosch, Jelle; Goette, Marco J. W.; van Veldhuisen, Dirk J.; Oudkerk, Matthijs

    The purpose of this study was to assess whether accurate global left-ventricular (LV) functional parameters can be obtained by analyzing every second short-axis magnetic resonance imaging cine series instead of consecutive slices, in order to reduce post-processing time. Forty patients, were scanned

  10. Assessment of global left ventricular functional parameters : analysis of every second short-axis magnetic resonance imaging slices is as accurate as analysis of consecutive slices

    NARCIS (Netherlands)

    Lubbers, Daniel D.; Willems, Tineke P.; van der Vleuten, Pieter A.; Overbosch, Jelle; Goette, Marco J. W.; van Veldhuisen, Dirk J.; Oudkerk, Matthijs

    2008-01-01

    The purpose of this study was to assess whether accurate global left-ventricular (LV) functional parameters can be obtained by analyzing every second short-axis magnetic resonance imaging cine series instead of consecutive slices, in order to reduce post-processing time. Forty patients, were scanned

  11. How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method.

    Science.gov (United States)

    Sun, Meijun; Zhang, Dong; Liu, Li; Wang, Zheng

    2017-03-01

    Hyperspectral imaging (HSI) in the near-infrared (NIR) region (900-1700nm) was used for non-intrusive quality measurements (of sweetness and texture) in melons. First, HSI data from melon samples were acquired to extract the spectral signatures. The corresponding sample sweetness and hardness values were recorded using traditional intrusive methods. Partial least squares regression (PLSR), principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN) models were created to predict melon sweetness and hardness values from the hyperspectral data. Experimental results for the three types of melons show that PLSR produces the most accurate results. To reduce the high dimensionality of the hyperspectral data, the weighted regression coefficients of the resulting PLSR models were used to identify the most important wavelengths. On the basis of these wavelengths, each image pixel was used to visualize the sweetness and hardness in all the portions of each sample.

  12. Ability of Functional Independence Measure to accurately predict functional outcome of stroke-specific population: Systematic review

    OpenAIRE

    Madeleine Spencer, DPT, PT; Karen Skop, DPT, PT; Kristina Shesko, DPT, PT; Kristen Nollinger, DPT, PT; Douglas Chumney, DPT, PT; Roberta A. Newton, PT, PhD

    2010-01-01

    Stroke is a leading cause of functional impairments. The ability to quantify the functional ability of poststroke patients engaged in a rehabilitation program may assist in prediction of their functional outcome. The Functional Independence Measure (FIM) is widely used and accepted as a functional-level assessment tool that evaluates the functional status of patients throughout the rehabilitation process. From February to March 2009, we searched MEDLINE, Ovid, CINAHL, and EBSCO for full-text ...

  13. Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems.

    Directory of Open Access Journals (Sweden)

    Ram Samudrala

    2009-04-01

    Full Text Available The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates--effector proteins--are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

  14. From dimer to condensed phases at extreme conditions: accurate predictions of the properties of water by a Gaussian charge polarizable model.

    Science.gov (United States)

    Paricaud, Patrice; Predota, Milan; Chialvo, Ariel A; Cummings, Peter T

    2005-06-22

    Water exhibits many unusual properties that are essential for the existence of life. Water completely changes its character from ambient to supercritical conditions in a way that makes it possible to sustain life at extreme conditions, leading to conjectures that life may have originated in deep-sea vents. Molecular simulation can be very useful in exploring biological and chemical systems, particularly at extreme conditions for which experiments are either difficult or impossible; however this scenario entails an accurate molecular model for water applicable over a wide range of state conditions. Here, we present a Gaussian charge polarizable model (GCPM) based on the model developed earlier by Chialvo and Cummings [Fluid Phase Equilib. 150, 73 (1998)] which is, to our knowledge, the first that satisfies the water monomer and dimer properties, and simultaneously yields very accurate predictions of dielectric, structural, vapor-liquid equilibria, and transport properties, over the entire fluid range. This model would be appropriate for simulating biological and chemical systems at both ambient and extreme conditions. The particularity of the GCPM model is the use of Gaussian distributions instead of points to represent the partial charges on the water molecules. These charge distributions combined with a dipole polarizability and a Buckingham exp-6 potential are found to play a crucial role for the successful and simultaneous predictions of a variety of water properties. This work not only aims at presenting an accurate model for water, but also at proposing strategies to develop classical accurate models for the predictions of structural, dynamic, and thermodynamic properties.

  15. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination.

    Science.gov (United States)

    Li, Xiaowei; Liu, Taigang; Tao, Peiying; Wang, Chunhua; Chen, Lanming

    2015-12-01

    Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.

  16. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

    Science.gov (United States)

    Wang, Ting; He, Quanze; Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing.

  17. Accurate microRNA target prediction using detailed binding site accessibility and machine learning on proteomics data

    Directory of Open Access Journals (Sweden)

    Martin eReczko

    2012-01-01

    Full Text Available MicroRNAs (miRNAs are a class of small regulatory genes regulating gene expression by targetingmessenger RNA. Though computational methods for miRNA target prediction are the prevailingmeans to analyze their function, they still miss a large fraction of the targeted genes and additionallypredict a large number of false positives. Here we introduce a novel algorithm called DIANAmicroT-ANN which combines multiple novel target site features through an artificial neural network(ANN and is trained using recently published high-throughput data measuring the change of proteinlevels after miRNA overexpression, providing positive and negative targeting examples. The featurescharacterizing each miRNA recognition element include binding structure, conservation level and aspecific profile of structural accessibility. The ANN is trained to integrate the features of eachrecognition element along the 3’ untranslated region into a targeting score, reproducing the relativerepression fold change of the protein. Tested on two different sets the algorithm outperforms otherwidely used algorithms and also predicts a significant number of unique and reliable targets notpredicted by the other methods. For 542 human miRNAs DIANA-microT-ANN predicts 120,000targets not provided by TargetScan 5.0. The algorithm is freely available athttp://microrna.gr/microT-ANN.

  18. Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners.

    Directory of Open Access Journals (Sweden)

    Carlo Baldassi

    Full Text Available In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids, exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i the prediction of residue-residue contacts in proteins, and (ii the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code.

  19. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

    Directory of Open Access Journals (Sweden)

    Ting Wang

    Full Text Available Massively parallel sequencing (MPS combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs by sequencing cell-free fetal DNA (cffDNA from maternal plasma, so-called non-invasive prenatal testing (NIPT. However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR and false positive rate (FPR in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1% in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples, suggesting that it is reliable and robust enough for clinical testing.

  20. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing

    Science.gov (United States)

    Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing. PMID:27441628

  1. Dual X-ray absorptiometry accurately predicts carcass composition from live sheep and chemical composition of live and dead sheep.

    Science.gov (United States)

    Pearce, K L; Ferguson, M; Gardner, G; Smith, N; Greef, J; Pethick, D W

    2009-01-01

    Fifty merino wethers (liveweight range from 44 to 81kg, average of 58.6kg) were lot fed for 42d and scanned through a dual X-ray absorptiometry (DXA) as both a live animal and whole carcass (carcass weight range from 15 to 32kg, average of 22.9kg) producing measures of total tissue, lean, fat and bone content. The carcasses were subsequently boned out into saleable cuts and the weights and yield of boned out muscle, fat and bone recorded. The relationship between chemical lean (protein+water) was highly correlated with DXA carcass lean (r(2)=0.90, RSD=0.674kg) and moderately with DXA live lean (r(2)=0.72, RSD=1.05kg). The relationship between the chemical fat was moderately correlated with DXA carcass fat (r(2)=0.86, RSD=0.42kg) and DXA live fat (r(2)=0.70, RSD=0.71kg). DXA carcass and live animal bone was not well correlated with chemical ash (both r(2)=0.38, RSD=0.3). DXA carcass lean was moderately well predicted from DXA live lean with the inclusion of bodyweight in the regression (r(2)=0.82, RSD=0.87kg). DXA carcass fat was well predicted from DXA live fat (r(2)=0.86, RSD=0.54kg). DXA carcass lean and DXA carcass fat with the inclusion of carcass weight in the regression significantly predicted boned out muscle (r(2)=0.97, RSD=0.32kg) and fat weight, respectively (r(2)=0.92, RSD=0.34kg). The use of DXA live lean and DXA live fat with the inclusion of bodyweight to predict boned out muscle (r(2)=0.83, RSD=0.75kg) and fat (r(2)=0.86, RSD=0.46kg) weight, respectively, was moderate. The use of DXA carcass and live lean and fat to predict boned out muscle and fat yield was not correlated as weight. The future for the DXA will exist in the determination of body composition in live animals and carcasses in research experiments but there is potential for the DXA to be used as an online carcass grading system.

  2. Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition.

    Science.gov (United States)

    Kong, Liang; Zhang, Lichao; Lv, Jinfeng

    2014-03-07

    Extracting good representation from protein sequence is fundamental for protein structural classes prediction tasks. In this paper, we propose a novel and powerful method to predict protein structural classes based on the predicted secondary structure information. At the feature extraction stage, a 13-dimensional feature vector is extracted to characterize general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Specially, four segment-level features are designed to elevate discriminative ability for proteins from the α/β and α+β classes. After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction. We report extensive experiments comparing the proposed method to the state-of-the-art in protein structural classes prediction on three widely used low-similarity benchmark datasets: FC699, 1189 and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies which have exceeded the best reported results on all of the three datasets.

  3. Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk

    Science.gov (United States)

    Aghaei, Faranak; Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Wang, Yunzhi; Qiu, Yuchen; Liu, Hong; Zheng, Bin

    2017-03-01

    Although breast density has been widely considered an important breast cancer risk factor, it is not very effective to predict risk of developing breast cancer in a short-term or harboring cancer in mammograms. Based on our recent studies to build short-term breast cancer risk stratification models based on bilateral mammographic density asymmetry, we in this study explored a new quantitative image marker based on bilateral focal density asymmetry to predict the risk of harboring cancers in mammograms. For this purpose, we assembled a testing dataset involving 100 positive and 100 negative cases. In each of positive case, no any solid masses are visible on mammograms. We developed a computer-aided detection (CAD) scheme to automatically detect focal dense regions depicting on two bilateral mammograms of left and right breasts. CAD selects one focal dense region with the maximum size on each image and computes its asymmetrical ratio. We used this focal density asymmetry as a new imaging marker to divide testing cases into two groups of higher and lower focal density asymmetry. The first group included 70 cases in which 62.9% are positive, while the second group included 130 cases in which 43.1% are positive. The odds ratio is 2.24. As a result, this preliminary study supported the feasibility of applying a new focal density asymmetry based imaging marker to predict the risk of having mammography-occult cancers. The goal is to assist radiologists more effectively and accurately detect early subtle cancers using mammography and/or other adjunctive imaging modalities in the future.

  4. Functional brain imaging predicts public health campaign success.

    Science.gov (United States)

    Falk, Emily B; O'Donnell, Matthew Brook; Tompson, Steven; Gonzalez, Richard; Dal Cin, Sonya; Strecher, Victor; Cummings, Kenneth Michael; An, Lawrence

    2016-02-01

    Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a 'self-localizer' defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400,000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R(2) up to 0.65) and (ii) this relationship depends on message content-self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns.

  5. Modality prediction of biomedical literature images using multimodal feature representation

    Directory of Open Access Journals (Sweden)

    Pelka, Obioma

    2016-08-01

    Full Text Available This paper presents the modelling approaches performed to automatically predict the modality of images found in biomedical literature. Various state-of-the-art visual features such as Bag-of-Keypoints computed with dense SIFT descriptors, texture features and Joint Composite Descriptors were used for visual image representation. Text representation was obtained by vector quantisation on a Bag-of-Words dictionary generated using attribute importance derived from a χ-test. Computing the principal components separately on each feature, dimension reduction as well as computational load reduction was achieved. Various multiple feature fusions were adopted to supplement visual image information with corresponding text information. The improvement obtained when using multimodal features vs. visual or text features was detected, analysed and evaluated. Random Forest models with 100 to 500 deep trees grown by resampling, a multi class linear kernel SVM with C=0.05 and a late fusion of the two classifiers were used for modality prediction. A Random Forest classifier achieved a higher accuracy and computed Bag-of-Keypoints with dense SIFT descriptors proved to be a better approach than with Lowe SIFT.

  6. A 3D-CFD code for accurate prediction of fluid flows and fluid forces in seals

    Science.gov (United States)

    Athavale, M. M.; Przekwas, A. J.; Hendricks, R. C.

    1994-01-01

    Current and future turbomachinery requires advanced seal configurations to control leakage, inhibit mixing of incompatible fluids and to control the rotodynamic response. In recognition of a deficiency in the existing predictive methodology for seals, a seven year effort was established in 1990 by NASA's Office of Aeronautics Exploration and Technology, under the Earth-to-Orbit Propulsion program, to develop validated Computational Fluid Dynamics (CFD) concepts, codes and analyses for seals. The effort will provide NASA and the U.S. Aerospace Industry with advanced CFD scientific codes and industrial codes for analyzing and designing turbomachinery seals. An advanced 3D CFD cylindrical seal code has been developed, incorporating state-of-the-art computational methodology for flow analysis in straight, tapered and stepped seals. Relevant computational features of the code include: stationary/rotating coordinates, cylindrical and general Body Fitted Coordinates (BFC) systems, high order differencing schemes, colocated variable arrangement, advanced turbulence models, incompressible/compressible flows, and moving grids. This paper presents the current status of code development, code demonstration for predicting rotordynamic coefficients, numerical parametric study of entrance loss coefficients for generic annular seals, and plans for code extensions to labyrinth, damping, and other seal configurations.

  7. 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.

  8. A CMOS Imager with Focal Plane Compression using Predictive Coding

    Science.gov (United States)

    Leon-Salas, Walter D.; Balkir, Sina; Sayood, Khalid; Schemm, Nathan; Hoffman, Michael W.

    2007-01-01

    This paper presents a CMOS image sensor with focal-plane compression. The design has a column-level architecture and it is based on predictive coding techniques for image decorrelation. The prediction operations are performed in the analog domain to avoid quantization noise and to decrease the area complexity of the circuit, The prediction residuals are quantized and encoded by a joint quantizer/coder circuit. To save area resources, the joint quantizerlcoder circuit exploits common circuitry between a single-slope analog-to-digital converter (ADC) and a Golomb-Rice entropy coder. This combination of ADC and encoder allows the integration of the entropy coder at the column level. A prototype chip was fabricated in a 0.35 pm CMOS process. The output of the chip is a compressed bit stream. The test chip occupies a silicon area of 2.60 mm x 5.96 mm which includes an 80 X 44 APS array. Tests of the fabricated chip demonstrate the validity of the design.

  9. Women's age and embryo developmental speed accurately predict clinical pregnancy after single vitrified-warmed blastocyst transfer.

    Science.gov (United States)

    Kato, Keiichi; Ueno, Satoshi; Yabuuchi, Akiko; Uchiyama, Kazuo; Okuno, Takashi; Kobayashi, Tamotsu; Segawa, Tomoya; Teramoto, Shokichi

    2014-10-01

    The aim of this study was to establish a simple, objective blastocyst grading system using women's age and embryo developmental speed to predict clinical pregnancy after single vitrified-warmed blastocyst transfer. A 6-year retrospective cohort study was conducted in a private infertility centre. A total of 7341 single vitrified-armed blastocyst transfer cycles were included, divided into those carried out between 2006 and 2011 (6046 cycles) and 2012 (1295 cycles). Clinical pregnancy rate, ongoing pregnancy rate and delivery rates were stratified by women's age (149 h) as embryo developmental speed. In all the age groups, clinical pregnancy rate, ongoing pregnancy rate and delivery rates decreased as the embryo developmental speed decreased (P pregnancy rates observed in the 2006-2011 cohort. Subsequently, the novel grading score was validated in the 2012 cohort (1295 cycles), finding an excellent association. In conclusion, we established a novel blastocyst grading system using women's age and embryo developmental speed as objective parameters.

  10. Predictive value of multi-detector computed tomography for accurate diagnosis of serous cystadenoma: Radiologic-pathologic correlation

    Institute of Scientific and Technical Information of China (English)

    Anjuli A Shah; Nisha I Sainani; Avinash Kambadakone Ramesh; Zarine K Shah; Vikram Deshpande; Peter F Hahn; Dushyant V Sahani

    2009-01-01

    AIM:To identify multi-detector computed tomography (MDCT) features mos t predi c t i ve of serous cystadenomas (SCAs),correlating with histopathology,and to study the impact of cyst size and MDCT technique on reader performance.METHODS:The MDCT scans of 164 patients with surgically verified pancreatic cystic lesions were reviewed by two readers to study the predictive value of various morphological features for establishing a diagnosis of SCAs.Accuracy in lesion characterization and reader confidence were correlated with lesion size (≤3 cm or ≥3 cm) and scanning protocols (dedicated vs routine).RESULTS:28/164 cysts (mean size,39 mm;range,8-92 mm) were diagnosed as SCA on pathology.The MDCT features predictive of diagnosis of SCA were microcystic appearance (22/28,78.6%),surface lobulations (25/28,89.3%) and central scar (9/28,32.4%).Stepwise logistic regression analysis showed that only microcystic appearance was significant for CT diagnosis of SCA (P=0.0001).The sensitivity,specificity and PPV of central scar and of combined microcystic appearance and lobulations were 32.4%/100%/100% and 68%/100%/100%,respectively.The reader confidence was higher for lesions>3 cm (P=0.02) and for MDCT scans performed using thin collimation (1.25-2.5 mm) compared to routine 5 mm collimation exams (P>0.05).CONCLUSION:Central scar on MDCT is diagnostic of SCA but is seen in only one third of SCAs.Microcystic morphology is the most significant CT feature in diagnosis of SCA.A combination of microcystic appearance and surface lobulations offers accuracy comparable to central scar with higher sensitivity.

  11. Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems

    Energy Technology Data Exchange (ETDEWEB)

    Samudrala, Ram; Heffron, Fred; McDermott, Jason E.

    2009-04-24

    The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates, effector proteins, are not. We have used a machine learning approach to identify new secreted effectors. The method integrates evolutionary measures, such as the pattern of homologs in a range of other organisms, and sequence-based features, such as G+C content, amino acid composition and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from Salmonella typhimurium and validated on a corresponding set of effectors from Pseudomonas syringae, after eliminating effectors with detectable sequence similarity. The method was able to identify all of the known effectors in P. syringae with a specificity of 84% and sensitivity of 82%. The reciprocal validation, training on P. syringae and validating on S. typhimurium, gave similar results with a specificity of 86% when the sensitivity level was 87%. These results show that type III effectors in disparate organisms share common features. We found that maximal performance is attained by including an N-terminal sequence of only 30 residues, which agrees with previous studies indicating that this region contains the secretion signal. We then used the method to define the most important residues in this putative secretion signal. Finally, we present novel predictions of secreted effectors in S. typhimurium, some of which have been experimentally validated, and apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis. This approach is a novel and effective way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

  12. The value of metabolic imaging to predict tumour response after chemoradiation in locally advanced rectal cancer

    Directory of Open Access Journals (Sweden)

    Gómez-Río Manuel

    2010-12-01

    Full Text Available Abstract Background We aim to investigate the possibility of using 18F-positron emission tomography/computer tomography (PET-CT to predict the histopathologic response in locally advanced rectal cancer (LARC treated with preoperative chemoradiation (CRT. Methods The study included 50 patients with LARC treated with preoperative CRT. All patients were evaluated by PET-CT before and after CRT, and results were compared to histopathologic response quantified by tumour regression grade (patients with TRG 1-2 being defined as responders and patients with grade 3-5 as non-responders. Furthermore, the predictive value of metabolic imaging for pathologic complete response (ypCR was investigated. Results Responders and non-responders showed statistically significant differences according to Mandard's criteria for maximum standardized uptake value (SUVmax before and after CRT with a specificity of 76,6% and a positive predictive value of 66,7%. Furthermore, SUVmax values after CRT were able to differentiate patients with ypCR with a sensitivity of 63% and a specificity of 74,4% (positive predictive value 41,2% and negative predictive value 87,9%; This rather low sensitivity and specificity determined that PET-CT was only able to distinguish 7 cases of ypCR from a total of 11 patients. Conclusions We conclude that 18-F PET-CT performed five to seven weeks after the end of CRT can visualise functional tumour response in LARC. In contrast, metabolic imaging with 18-F PET-CT is not able to predict patients with ypCR accurately.

  13. Multispectral Image Analysis for Robust Prediction of Astaxanthin Coating

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Frosch, Stina; Nielsen, Michael Engelbrecht

    2013-01-01

    The aim of this study was to investigate the possibility of predicting the type and concentration level of astaxanthin coating of aquaculture feed pellets using multispectral image analysis. We used both natural and synthetic astaxanthin, and we used several different concentration levels...... of synthetic astaxanthin in combination with four different recipes of feed pellets. We used a VideometerLab with 20 spectral bands in the range of 385-1050 nm. We used linear discriminant analysis and sparse linear discriminant analysis for classification and variable selection. We used partial least squares...

  14. Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

    Directory of Open Access Journals (Sweden)

    Élise Fortin

    Full Text Available The optimal way to measure antimicrobial use in hospital populations, as a complement to surveillance of resistance is still unclear. Using respiratory isolates and antimicrobial prescriptions of nine intensive care units (ICUs, this study aimed to identify the indicator of antimicrobial use that predicted prevalence and incidence rates of resistance with the best accuracy.Retrospective cohort study including all patients admitted to three neonatal (NICU, two pediatric (PICU and four adult ICUs between April 2006 and March 2010. Ten different resistance/antimicrobial use combinations were studied. After adjustment for ICU type, indicators of antimicrobial use were successively tested in regression models, to predict resistance prevalence and incidence rates, per 4-week time period, per ICU. Binomial regression and Poisson regression were used to model prevalence and incidence rates, respectively. Multiplicative and additive models were tested, as well as no time lag and a one 4-week-period time lag. For each model, the mean absolute error (MAE in prediction of resistance was computed. The most accurate indicator was compared to other indicators using t-tests.Results for all indicators were equivalent, except for 1/20 scenarios studied. In this scenario, where prevalence of carbapenem-resistant Pseudomonas sp. was predicted with carbapenem use, recommended daily doses per 100 admissions were less accurate than courses per 100 patient-days (p = 0.0006.A single best indicator to predict antimicrobial resistance might not exist. Feasibility considerations such as ease of computation or potential external comparisons could be decisive in the choice of an indicator for surveillance of healthcare antimicrobial use.

  15. Biomass prediction model in maize based on satellite images

    Science.gov (United States)

    Mihai, Herbei; Florin, Sala

    2016-06-01

    Monitoring of crops by satellite techniques is very useful in the context of precision agriculture, regarding crops management and agricultural production. The present study has evaluated the interrelationship between maize biomass production and satellite indices (NDVI and NDBR) during five development stages (BBCH code), highlighting different levels of correlation. Biomass production recorded was between 2.39±0.005 t ha-1 (12-13 BBCH code) and 51.92±0.028 t ha-1 (83-85 BBCH code), in relation to vegetation stages studied. Values of chlorophyll content ranged from 24.1±0.25 SPAD unit (12-13 BBCH code) to 58.63±0.47 SPAD unit (71-73 BBCH code), and the obtained satellite indices ranged from 0.035641±0.002 and 0.320839±0.002 for NDVI indices respectively 0.035095±0.034 and 0.491038±0.018 in the case of NDBR indices. By regression analysis it was possible to obtain predictive models of biomass in maize based on the satellite indices, in statistical accurate conditions. The most accurate prediction was possible based on NDBR index (R2 = 0.986, F = 144.23, p<0.001, RMSE = 1.446), then based on chlorophyll content (R2 = 0.834, F = 16.14, p = 0.012, RMSE = 6.927) and NDVI index (R2 = 0.682, F = 3.869, p = 0.116, RMSE = 12.178).

  16. Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT

    CERN Document Server

    Sidky, Emil Y; Pan, Xiaochuan

    2009-01-01

    In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.

  17. [Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

    Science.gov (United States)

    Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

    2015-11-01

    introduced to eliminate the impact of nonlinear hyperspectral data to some extent through mapping the original nonlinear hyperspectral data to the high dimensional linear feature space, so the relationship between the nonlinear hyperspectral data and the encapsulation temperatures of EVA films was fully disclosed finally. Compared with the prediction results of three proposed models, the prediction performance of LMNN was superior to the other two, whose final recognition accuracy achieved 100%. The results indicated that the methods of combination of LMNN model with the hyperspectral imaging techniques was the best one for accurately and rapidly determining the encapsulation temperatures of EVA films of photovoltaic cells. In addition, this paper had created the ideal conditions for automatically monitoring and effectively controlling the encapsulation temperatures of EVA films in the photovoltaic cells production process.

  18. 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

  19. 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

  20. Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit.

    Science.gov (United States)

    Liu, Changhong; Liu, Wei; Chen, Wei; Yang, Jianbo; Zheng, Lei

    2015-04-15

    Tomato is an important health-stimulating fruit because of the antioxidant properties of its main bioactive compounds, dominantly lycopene and phenolic compounds. Nowadays, product differentiation in the fruit market requires an accurate evaluation of these value-added compounds. An experiment was conducted to simultaneously and non-destructively measure lycopene and phenolic compounds content in intact tomatoes using multispectral imaging combined with chemometric methods. Partial least squares (PLS), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) were applied to develop quantitative models. Compared with PLS and LS-SVM, BPNN model considerably improved the performance with coefficient of determination in prediction (RP(2))=0.938 and 0.965, residual predictive deviation (RPD)=4.590 and 9.335 for lycopene and total phenolics content prediction, respectively. It is concluded that multispectral imaging is an attractive alternative to the standard methods for determination of bioactive compounds content in intact tomatoes, providing a useful platform for infield fruit sorting/grading.

  1. Microdosing of a Carbon-14 Labeled Protein in Healthy Volunteers Accurately Predicts Its Pharmacokinetics at Therapeutic Dosages.

    Science.gov (United States)

    Vlaming, M L H; van Duijn, E; Dillingh, M R; Brands, R; Windhorst, A D; Hendrikse, N H; Bosgra, S; Burggraaf, J; de Koning, M C; Fidder, A; Mocking, J A J; Sandman, H; de Ligt, R A F; Fabriek, B O; Pasman, W J; Seinen, W; Alves, T; Carrondo, M; Peixoto, C; Peeters, P A M; Vaes, W H J

    2015-08-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of NBEs in humans can be successfully obtained early in the drug development process by the use of microdosing in a small group of healthy subjects combined with ultrasensitive accelerator mass spectrometry (AMS). After only minimal preclinical testing, we performed a first-in-human phase 0/phase 1 trial with a human recombinant therapeutic protein (RESCuing Alkaline Phosphatase, human recombinant placental alkaline phosphatase [hRESCAP]) to assess its safety and kinetics. Pharmacokinetic analysis showed dose linearity from microdose (53 μg) [(14) C]-hRESCAP to therapeutic doses (up to 5.3 mg) of the protein in healthy volunteers. This study demonstrates the value of a microdosing approach in a very small cohort for accelerating the clinical development of NBEs.

  2. 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

  3. Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping.

    Directory of Open Access Journals (Sweden)

    Xiaoxia Yin

    Full Text Available In this paper, we demonstrate a comprehensive method for segmenting the retinal vasculature in camera images of the fundus. This is of interest in the area of diagnostics for eye diseases that affect the blood vessels in the eye. In a departure from other state-of-the-art methods, vessels are first pre-grouped together with graph partitioning, using a spectral clustering technique based on morphological features. Local curvature is estimated over the whole image using eigenvalues of Hessian matrix in order to enhance the vessels, which appear as ridges in images of the retina. The result is combined with a binarized image, obtained using a threshold that maximizes entropy, to extract the retinal vessels from the background. Speckle type noise is reduced by applying a connectivity constraint on the extracted curvature based enhanced image. This constraint is varied over the image according to each region's predominant blood vessel size. The resultant image exhibits the central light reflex of retinal arteries and veins, which prevents the segmentation of whole vessels. To address this, the earlier entropy-based binarization technique is repeated on the original image, but crucially, with a different threshold to incorporate the central reflex vessels. The final segmentation is achieved by combining the segmented vessels with and without central light reflex. We carry out our approach on DRIVE and REVIEW, two publicly available collections of retinal images for research purposes. The obtained results are compared with state-of-the-art methods in the literature using metrics such as sensitivity (true positive rate, selectivity (false positive rate and accuracy rates for the DRIVE images and measured vessel widths for the REVIEW images. Our approach out-performs the methods in the literature.

  4. A new accurate ground-state potential energy surface of ethylene and predictions for rotational and vibrational energy levels

    Energy Technology Data Exchange (ETDEWEB)

    Delahaye, Thibault, E-mail: thibault.delahaye@univ-reims.fr; Rey, Michaël, E-mail: michael.rey@univ-reims.fr; Tyuterev, Vladimir G. [Groupe de Spectrométrie Moléculaire et Atmosphérique, UMR CNRS 7331, BP 1039, F-51687, Reims Cedex 2 (France); Nikitin, Andrei [Laboratory of Theoretical Spectroscopy, Institute of Atmospheric Optics, Russian Academy of Sciences, 634055 Tomsk, Russia and Quamer, State University of Tomsk (Russian Federation); Szalay, Péter G. [Institute of Chemistry, Eötvös Loránd University, P.O. Box 32, H-1518 Budapest (Hungary)

    2014-09-14

    In this paper we report a new ground state potential energy surface for ethylene (ethene) C{sub 2}H{sub 4} obtained from extended ab initio calculations. The coupled-cluster approach with the perturbative inclusion of the connected triple excitations CCSD(T) and correlation consistent polarized valence basis set cc-pVQZ was employed for computations of electronic ground state energies. The fit of the surface included 82 542 nuclear configurations using sixth order expansion in curvilinear symmetry-adapted coordinates involving 2236 parameters. A good convergence for variationally computed vibrational levels of the C{sub 2}H{sub 4} molecule was obtained with a RMS(Obs.–Calc.) deviation of 2.7 cm{sup −1} for fundamental bands centers and 5.9 cm{sup −1} for vibrational bands up to 7800 cm{sup −1}. Large scale vibrational and rotational calculations for {sup 12}C{sub 2}H{sub 4}, {sup 13}C{sub 2}H{sub 4}, and {sup 12}C{sub 2}D{sub 4} isotopologues were performed using this new surface. Energy levels for J = 20 up to 6000 cm{sup −1} are in a good agreement with observations. This represents a considerable improvement with respect to available global predictions of vibrational levels of {sup 13}C{sub 2}H{sub 4} and {sup 12}C{sub 2}D{sub 4} and rovibrational levels of {sup 12}C{sub 2}H{sub 4}.

  5. Infectious titres of sheep scrapie and bovine spongiform encephalopathy agents cannot be accurately predicted from quantitative laboratory test results.

    Science.gov (United States)

    González, Lorenzo; Thorne, Leigh; Jeffrey, Martin; Martin, Stuart; Spiropoulos, John; Beck, Katy E; Lockey, Richard W; Vickery, Christopher M; Holder, Thomas; Terry, Linda

    2012-11-01

    It is widely accepted that abnormal forms of the prion protein (PrP) are the best surrogate marker for the infectious agent of prion diseases and, in practice, the detection of such disease-associated (PrP(d)) and/or protease-resistant (PrP(res)) forms of PrP is the cornerstone of diagnosis and surveillance of the transmissible spongiform encephalopathies (TSEs). Nevertheless, some studies question the consistent association between infectivity and abnormal PrP detection. To address this discrepancy, 11 brain samples of sheep affected with natural scrapie or experimental bovine spongiform encephalopathy were selected on the basis of the magnitude and predominant types of PrP(d) accumulation, as shown by immunohistochemical (IHC) examination; contra-lateral hemi-brain samples were inoculated at three different dilutions into transgenic mice overexpressing ovine PrP and were also subjected to quantitative analysis by three biochemical tests (BCTs). Six samples gave 'low' infectious titres (10⁶·⁵ to 10⁶·⁷ LD₅₀ g⁻¹) and five gave 'high titres' (10⁸·¹ to ≥ 10⁸·⁷ LD₅₀ g⁻¹) and, with the exception of the Western blot analysis, those two groups tended to correspond with samples with lower PrP(d)/PrP(res) results by IHC/BCTs. However, no statistical association could be confirmed due to high individual sample variability. It is concluded that although detection of abnormal forms of PrP by laboratory methods remains useful to confirm TSE infection, infectivity titres cannot be predicted from quantitative test results, at least for the TSE sources and host PRNP genotypes used in this study. Furthermore, the near inverse correlation between infectious titres and Western blot results (high protease pre-treatment) argues for a dissociation between infectivity and PrP(res).

  6. Study protocol: multi-parametric magnetic resonance imaging for therapeutic response prediction in rectal cancer.

    Science.gov (United States)

    Pham, Trang Thanh; Liney, Gary; Wong, Karen; Rai, Robba; Lee, Mark; Moses, Daniel; Henderson, Christopher; Lin, Michael; Shin, Joo-Shik; Barton, Michael Bernard

    2017-07-04

    Response to neoadjuvant chemoradiotherapy (CRT) of rectal cancer is variable. Accurate imaging for prediction and early assessment of response would enable appropriate stratification of management to reduce treatment morbidity and improve therapeutic outcomes. Use of either diffusion weighted imaging (DWI) or dynamic contrast enhanced (DCE) imaging alone currently lacks sufficient sensitivity and specificity for clinical use to guide individualized treatment in rectal cancer. Multi-parametric MRI and analysis combining DWI and DCE may have potential to improve the accuracy of therapeutic response prediction and assessment. This protocol describes a prospective non-interventional single-arm clinical study. Patients with locally advanced rectal cancer undergoing preoperative CRT will prospectively undergo multi-parametric MRI pre-CRT, week 3 CRT, and post-CRT. The protocol consists of DWI using a read-out segmented sequence (RESOLVE), and DCE with pre-contrast T1-weighted (VIBE) scans for T1 calculation, followed by 60 phases at high temporal resolution (TWIST) after gadoversetamide injection. A 3-dimensional voxel-by-voxel technique will be used to produce colour-coded ADC and K(trans) histograms, and data evaluated in combination using scatter plots. MRI parameters will be correlated with surgical histopathology. Histopathology analysis will be standardized, with chemoradiotherapy response defined according to AJCC 7th Edition Tumour Regression Grade (TRG) criteria. Good response will be defined as TRG 0-1, and poor response will be defined as TRG 2-3. The combination of DWI and DCE can provide information on physiological tumour factors such as cellularity and perfusion that may affect radiotherapy response. If validated, multi-parametric MRI combining DWI and DCE can be used to stratify management in rectal cancer patients. Accurate imaging prediction of patients with a complete response to CRT would enable a 'watch and wait' approach, avoiding surgical morbidity

  7. Color calibration and fusion of lens-free and mobile-phone microscopy images for high-resolution and accurate color reproduction

    Science.gov (United States)

    Zhang, Yibo; Wu, Yichen; Zhang, Yun; Ozcan, Aydogan

    2016-06-01

    Lens-free holographic microscopy can achieve wide-field imaging in a cost-effective and field-portable setup, making it a promising technique for point-of-care and telepathology applications. However, due to relatively narrow-band sources used in holographic microscopy, conventional colorization methods that use images reconstructed at discrete wavelengths, corresponding to e.g., red (R), green (G) and blue (B) channels, are subject to color artifacts. Furthermore, these existing RGB colorization methods do not match the chromatic perception of human vision. Here we present a high-color-fidelity and high-resolution imaging method, termed “digital color fusion microscopy” (DCFM), which fuses a holographic image acquired at a single wavelength with a color-calibrated image taken by a low-magnification lens-based microscope using a wavelet transform-based colorization method. We demonstrate accurate color reproduction of DCFM by imaging stained tissue sections. In particular we show that a lens-free holographic microscope in combination with a cost-effective mobile-phone-based microscope can generate color images of specimens, performing very close to a high numerical-aperture (NA) benchtop microscope that is corrected for color distortions and chromatic aberrations, also matching the chromatic response of human vision. This method can be useful for wide-field imaging needs in telepathology applications and in resource-limited settings, where whole-slide scanning microscopy systems are not available.

  8. Large-format imaging plate and weissenberg camera for accurate protein crystallographic data collection using synchrotron radiation.

    Science.gov (United States)

    Sakabe, K; Sasaki, K; Watanabe, N; Suzuki, M; Wang, Z G; Miyahara, J; Sakabe, N

    1997-05-01

    Off-line and on-line protein data-collection systems using an imaging plate as a detector are described and their components reported. The off-line scanner IPR4080 was developed for a large-format imaging plate ;BASIII' of dimensions 400 x 400 mm and 400 x 800 mm. The characteristics of this scanner are a dynamic range of 10(5) photons pixel(-1), low background noise and high sensitivity. A means of reducing electronic noise and a method for finding the origin of the noise are discussed in detail. A dedicated screenless Weissenberg camera matching IPR4080 with synchrotron radiation was developed and installed on beamline BL6B at the Photon Factory. This camera can attach one or two sheets of 400 x 800 mm large-format imaging plate inside the film cassette by evacuation. The positional reproducibility of the imaging plate on the cassette is so good that the data can be processed by batch job. Data of 93% completeness up to 1.6 A resolution were collected on a single axis rotation and the value of R(merge) becomes 4% from a tetragonal lysozyme crystal using a set of two imaging-plate sheets. Comparing two types of imaging plates, the signal-to-noise ratio of the ST-VIP-type imaging plate is 25% better than that of the BASIII-type imaging plate for protein data collection using 1.0 and 0.7 A X-rays. A new on-line protein data-collection system with imaging plates is specially designed to use synchrotron radiation X-rays at maximum efficiency.

  9. Dynamic gadolinium-enhanced magnetic resonance imaging allows accurate assessment of the synovial inflammatory activity in rheumatoid arthritis knee joints: a comparison with synovial histology

    DEFF Research Database (Denmark)

    Axelsen, Mette Bjørndal; Stoltenberg, M.; Poggenborg, R.

    2012-01-01

    Objective: To determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluated using semi-automatic image processing software can accurately assess synovial inflammation in rheumatoid arthritis (RA) knee joints. Methods: In 17 RA patients undergoing knee surgery, the ave......Objective: To determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluated using semi-automatic image processing software can accurately assess synovial inflammation in rheumatoid arthritis (RA) knee joints. Methods: In 17 RA patients undergoing knee surgery......, the average grade of histological synovial inflammation was determined from four biopsies obtained during surgery. A preoperative series of T(1)-weighted dynamic fast low-angle shot (FLASH) MR images was obtained. Parameters characterizing contrast uptake dynamics, including the initial rate of enhancement...... capsule of the knee joint (Precise ROI). Intra- and interreader agreement was assessed using the intra-class correlation coefficient (ICC). Results: The IRE from the Quick ROI and the Precise ROI revealed high correlations to the grade of histological inflammation (Spearman's correlation coefficient (rho...

  10. Prediction of winter wheat grain protein content by ASTER image

    Science.gov (United States)

    Huang, Wenjiang; Song, Xiaoyu; Wang, Jihua; Wang, Zhijie; Zhao, Chunjiang

    2008-10-01

    The Advanced technology in space-borne determination of grain crude protein content (CP) by remote sensing can help optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP, while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a higher coefficient of determination of R2=0.7302. The method developed in this study could contribute towards developing optimal procedures for evaluating wheat grain quality by ASTER image at anthesis stage. The RMSE was 0.893 % for ASTER image model, and the R2 was 0.7194. It is thus feasible to forecast grain quality by NRI derived from ASTER image.

  11. Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

    Directory of Open Access Journals (Sweden)

    Hong Cheng

    2017-01-01

    Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4 Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.

  12. 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.

  13. 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.

  14. 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

    2016-01-01

    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. PMID:26927108

  15. Magnetic resonance imaging cannot predict histological tumour involvement of a circumferential surgical margin in rectal cancer.

    Science.gov (United States)

    Dent, O F; Chapuis, P H; Haboubi, N; Bokey, L

    2011-09-01

    Several recent studies have attempted to evaluate the accuracy of preoperative magnetic resonance imaging (MRI) in predicting the likelihood of tumour involvement of the postoperative circumferential resection margin (CRM) in rectal cancer with the intention of selecting patients who might benefit from neoadjuvant therapy and as a guide to surgery. The aim of this study was to assess whether such studies can provide a valid answer as to whether preoperative MRI can accurately predict CRM involvement by tumour. The study design and methodology of studies on this topic were critically examined. Features identified as affecting the efficacy of these studies were: representativeness of patients, definition of the margin assessed by MRI and by histology, lack of blinding of surgeons and pathologists to MRI results, effect of neoadjuvant treatment, and number of patients studied. Because of methodological inadequacies in studies completed to date, there is insufficient evidence of the ability of a positive MRI result to predict an involved CRM. Although MRI may be able to identify a tumour that has extended to the mesorectal fascia and/or intersphincteric plane, logically, it cannot indicate where the surgical boundary of the resection will ultimately lie, and therefore cannot validly predict an involved CRM and should not be relied upon for this purpose. © 2011 The Authors. Colorectal Disease © 2011 The Association of Coloproctology of Great Britain and Ireland.

  16. Multireference correlation consistent composite approach [MR-ccCA]: toward accurate prediction of the energetics of excited and transition state chemistry.

    Science.gov (United States)

    Oyedepo, Gbenga A; Wilson, Angela K

    2010-08-26

    The correlation consistent Composite Approach, ccCA [ Deyonker , N. J. ; Cundari , T. R. ; Wilson , A. K. J. Chem. Phys. 2006 , 124 , 114104 ] has been demonstrated to predict accurate thermochemical properties of chemical species that can be described by a single configurational reference state, and at reduced computational cost, as compared with ab initio methods such as CCSD(T) used in combination with large basis sets. We have developed three variants of a multireference equivalent of this successful theoretical model. The method, called the multireference correlation consistent composite approach (MR-ccCA), is designed to predict the thermochemical properties of reactive intermediates, excited state species, and transition states to within chemical accuracy (e.g., 1 kcal/mol for enthalpies of formation) of reliable experimental values. In this study, we have demonstrated the utility of MR-ccCA: (1) in the determination of the adiabatic singlet-triplet energy separations and enthalpies of formation for the ground states for a set of diradicals and unsaturated compounds, and (2) in the prediction of energetic barriers to internal rotation, in ethylene and its heavier congener, disilene. Additionally, we have utilized MR-ccCA to predict the enthalpies of formation of the low-lying excited states of all the species considered. MR-ccCA is shown to give quantitative results without reliance upon empirically derived parameters, making it suitable for application to study novel chemical systems with significant nondynamical correlation effects.

  17. Enhancement of a Turbulence Sub-Model for More Accurate Predictions of Vertical Stratifications in 3D Coastal and Estuarine Modeling

    Directory of Open Access Journals (Sweden)

    Wenrui Huang

    2010-03-01

    Full Text Available This paper presents an improvement of the Mellor and Yamada's 2nd order turbulence model in the Princeton Ocean Model (POM for better predictions of vertical stratifications of salinity in estuaries. The model was evaluated in the strongly stratified estuary, Apalachicola River, Florida, USA. The three-dimensional hydrodynamic model was applied to study the stratified flow and salinity intrusion in the estuary in response to tide, wind, and buoyancy forces. Model tests indicate that model predictions over estimate the stratification when using the default turbulent parameters. Analytic studies of density-induced and wind-induced flows indicate that accurate estimation of vertical eddy viscosity plays an important role in describing vertical profiles. Initial model revision experiments show that the traditional approach of modifying empirical constants in the turbulence model leads to numerical instability. In order to improve the performance of the turbulence model while maintaining numerical stability, a stratification factor was introduced to allow adjustment of the vertical turbulent eddy viscosity and diffusivity. Sensitivity studies indicate that the stratification factor, ranging from 1.0 to 1.2, does not cause numerical instability in Apalachicola River. Model simulations show that increasing the turbulent eddy viscosity by a stratification factor of 1.12 results in an optimal agreement between model predictions and observations in the case study presented in this study. Using the proposed stratification factor provides a useful way for coastal modelers to improve the turbulence model performance in predicting vertical turbulent mixing in stratified estuaries and coastal waters.

  18. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.

    Science.gov (United States)

    Martin, Eric; Mukherjee, Prasenjit; Sullivan, David; Jansen, Johanna

    2011-08-22

    Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be

  19. A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

    OpenAIRE

    Vineet Kumar; Abhijit Asati; Anu Gupta

    2016-01-01

    Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR) illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonunifor...

  20. Feasibility study for application of the compressed-sensing framework to interior computed tomography (ICT) for low-dose, high-accurate dental x-ray imaging

    Science.gov (United States)

    Je, U. K.; Cho, H. M.; Cho, H. S.; Park, Y. O.; Park, C. K.; Lim, H. W.; Kim, K. S.; Kim, G. A.; Park, S. Y.; Woo, T. H.; Choi, S. I.

    2016-02-01

    In this paper, we propose a new/next-generation type of CT examinations, the so-called Interior Computed Tomography (ICT), which may presumably lead to dose reduction to the patient outside the target region-of-interest (ROI), in dental x-ray imaging. Here an x-ray beam from each projection position covers only a relatively small ROI containing a target of diagnosis from the examined structure, leading to imaging benefits such as decreasing scatters and system cost as well as reducing imaging dose. We considered the compressed-sensing (CS) framework, rather than common filtered-backprojection (FBP)-based algorithms, for more accurate ICT reconstruction. We implemented a CS-based ICT algorithm and performed a systematic simulation to investigate the imaging characteristics. Simulation conditions of two ROI ratios of 0.28 and 0.14 between the target and the whole phantom sizes and four projection numbers of 360, 180, 90, and 45 were tested. We successfully reconstructed ICT images of substantially high image quality by using the CS framework even with few-view projection data, still preserving sharp edges in the images.

  1. 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

    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

  2. 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

  3. Early Diagnosis and Prediction of Anticancer Drug-induced Cardiotoxicity: From Cardiac Imaging to "Omics" Technologies.

    Science.gov (United States)

    Madonna, Rosalinda

    2017-07-01

    Heart failure due to antineoplastic therapy remains a major cause of morbidity and mortality in oncological patients. These patients often have no prior manifestation of disease. There is therefore a need for accurate identification of individuals at risk of such events before the appearance of clinical manifestations. The present article aims to provide an overview of cardiac imaging as well as new "-omics" technologies, especially with regard to genomics and proteomics as promising tools for the early detection and prediction of cardiotoxicity and individual responses to antineoplastic drugs. Copyright © 2017 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

  4. Prediction of lean and fat composition in swine carcasses from ham area measurements with image analysis.

    Science.gov (United States)

    Jia, Jiancheng; Schinckel, Allan P; Forrest, John C; Chen, Way; Wagner, Jeffrey R

    2010-06-01

    Video images of ham cross-sections were recorded from 71 pork carcasses (ranging in weight from 72 to 119kg). Three sets of prediction equations were developed to estimate pork carcass lean and fat composition from video image analysis (VIA) of ham cross-sectional area measurements, 10th rib back fat depth (TENFAT) and hot carcass weight (HCKg). Carcass data of dissected lean and fat in the four primal cuts (ham, loin, Boston button and picnic shoulder) were used as dependent variables in establishing regression equations. The first set of equations combined VIA ham measurements and total ham weight (HTKg). Regression models containing the single variable HTKg times ham percentage lean area (Vol. 1) or HTKg times ham percentage fat area (Vol. 2) accounted for 88% and 68% of the variation in total carcass lean weight (CLKg) and total carcass fat weight (CFKg) from the right side of each carcass, respectively. The second set of equations combined VIA ham measurements and TENFAT (cm). Multiple regression models involving TENFAT, Vol. 1, and Vol. 2 accounted for 91% and 90% of the variation in CLKg and CFKg. The third set of equations used VIA ham measurements, TENFAT and HCKg. Carcass lean weight was best predicted by HCKg, TENFAT, and ham lean area (HLA) (R(2)=.92). Carcass fat weight was best predicted by HCKg, TENFAT, and Vol. 2 (R(2)=.91). Overall correlations showed a high association between Vol. 1 and CLKg (r=.94, PVol. 2 and CFKg (r=.83, P<.0001). Ham lean area was related to CLKg (r=.74, P<.0001) and ham fat area to CFKg (r=.81, P<.0001). The results of this study indicated video image analysis of ham cross-section slices combined with backfat depth at the 10th rib can be used for accurate estimation of total carcass lean or fat composition.

  5. Can Kohn-Sham density functional theory predict accurate charge distributions for both single-reference and multi-reference molecules?

    Science.gov (United States)

    Verma, Pragya; Truhlar, Donald G

    2017-05-24

    Dipole moments are the first moment of electron density and are fundamental quantities that are often available from experiments. An exchange-correlation functional that leads to an accurate representation of the charge distribution of a molecule should accurately predict the dipole moments of the molecule. It is well known that Kohn-Sham density functional theory (DFT) is more accurate for the energetics of single-reference systems than for the energetics of multi-reference ones, but there has been less study of charge distributions. In this work, we benchmark 48 density functionals chosen with various combinations of ingredients, against accurate experimental data for dipole moments of 78 molecules, in particular 55 single-reference molecules and 23 multi-reference ones. We chose both organic and inorganic molecules, and within the category of inorganic molecules there are both main-group and transition-metal-containing molecules, with some of them being multi-reference. As one would expect, the multi-reference molecules are not as well described by single-reference DFT, and the functionals tested in this work do show larger mean unsigned errors (MUEs) for the 23 multi-reference molecules than the single-reference ones. Five of the 78 molecules have relatively large experimental error bars and were therefore not included in calculating the overall MUEs. For the 73 molecules not excluded, we find that three of the hybrid functionals, B97-1, PBE0, and TPSSh (each with less than or equal to 25% Hartree-Fock (HF) exchange), the range-separated hybrid functional, HSE06 (with HF exchange decreasing from 25% to 0 as interelectronic distance increases), and the hybrid functional, PW6B95 (with 28% HF exchange) are the best performing functionals with each yielding an MUE of 0.18 D. Perhaps the most significant finding of this study is that there exists great similarity among the success rate of various functionals in predicting dipole moments. In particular, of 39

  6. StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images.

    Science.gov (United States)

    De Backer, A; van den Bos, K H W; Van den Broek, W; Sijbers, J; Van Aert, S

    2016-12-01

    An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighbouring columns, enabling the analysis of a large field of view. For this algorithm, the accuracy and precision with which measurements for the atomic column positions and scattering cross-sections from annular dark field (ADF) STEM images can be estimated, has been investigated. The highest attainable precision is reached even for low dose images. Furthermore, the advantages of the model-based approach taking into account overlap between neighbouring columns are highlighted. This is done for the estimation of the distance between two neighbouring columns as a function of their distance and for the estimation of the scattering cross-section which is compared to the integrated intensity from a Voronoi cell. To provide end-users this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license.

  7. Accurate IMRT fluence verification for prostate cancer patients using 'in-vivo' measured EPID images and in-room acquired kilovoltage cone-beam CT scans

    NARCIS (Netherlands)

    A.S.A.M. Ali (Ali Sid Ahmed M.); M.L.P. Dirkx (Maarten); R.M. Cools (Ruud); B.J.M. Heijmen (Ben)

    2013-01-01

    textabstractBackground: To investigate for prostate cancer patients the comparison of 'in-vivo' measured portal dose images (PDIs) with predictions based on a kilovoltage cone-beam CT scan (CBCT), acquired during the same treatment fraction, as an alternative for pre-treatment verification. For eval

  8. A methodology to accurately quantify patellofemoral cartilage contact kinematics by combining 3D image shape registration and cine-PC MRI velocity data.

    Science.gov (United States)

    Borotikar, Bhushan S; Sipprell, William H; Wible, Emily E; Sheehan, Frances T

    2012-04-05

    Patellofemoral osteoarthritis and its potential precursor patellofemoral pain syndrome (PFPS) are common, costly, and debilitating diseases. PFPS has been shown to be associated with altered patellofemoral joint mechanics; however, an actual variation in joint contact stresses has not been established due to challenges in accurately quantifying in vivo contact kinematics (area and location). This study developed and validated a method for tracking dynamic, in vivo cartilage contact kinematics by combining three magnetic resonance imaging (MRI) techniques, cine-phase contrast (CPC), multi-plane cine (MPC), and 3D high-resolution static imaging. CPC and MPC data were acquired from 12 healthy volunteers while they actively extended/flexed their knee within the MRI scanner. Since no gold standard exists for the quantification of in vivo dynamic cartilage contact kinematics, the accuracy of tracking a single point (patellar origin relative to the femur) represented the accuracy of tracking the kinematics of an entire surface. The accuracy was determined by the average absolute error between the PF kinematics derived through registration of MPC images to a static model and those derived through integration of the CPC velocity data. The accuracy ranged from 0.47 mm to 0.77 mm for the patella and femur and from 0.68 mm to 0.86 mm for the patellofemoral joint. For purely quantifying joint kinematics, CPC remains an analytically simpler and more accurate (accuracy <0.33 mm) technique. However, for application requiring the tracking of an entire surface, such as quantifying cartilage contact kinematics, this combined imaging approach produces accurate results with minimal operator intervention.

  9. PET guidance in prostate cancer radiotherapy: Quantitative imaging to predict response and guide treatment.

    Science.gov (United States)

    Cattaneo, G M; Bettinardi, V; Mapelli, P; Picchio, M

    2016-03-01

    Positron emission tomography (PET) allows a monitoring and recording of the spatial and temporal distribution of molecular/cellular processes for diagnostic and therapeutic applications. The aim of this review is to describe the current applications and to explore the role of PET in prostate cancer management, mainly in the radiation therapy (RT) scenario. The state-of-the art of PET for prostate cancer will be presented together with the impact of new specific PET tracers and technological developments aiming at obtaining better imaging quality, increased tumor detectability and more accurate volume delineation. An increased number of studies have been focusing on PET quantification methods as predictive biomarkers capable of guiding individualized treatment and improving patient outcome; the sophisticated advanced intensity modulated and imaged guided radiation therapy techniques (IMRT/IGRT) are capable of boosting more radioresistant tumor (sub)volumes. The use of advanced feature analyses of PET images is an approach that holds great promise with regard to several oncological diseases, but needs further validation in managing prostate diseases.

  10. Accurate kinematic measurement at interfaces between dissimilar materials using conforming finite-element-based digital image correlation

    KAUST Repository

    Tao, Ran

    2016-02-11

    Digital image correlation (DIC) is now an extensively applied full-field measurement technique with subpixel accuracy. A systematic drawback of this technique, however, is the smoothening of the kinematic field (e.g., displacement and strains) across interfaces between dissimilar materials, where the deformation gradient is known to be large. This can become an issue when a high level of accuracy is needed, for example, in the interfacial region of composites or joints. In this work, we described the application of global conforming finite-element-based DIC technique to obtain precise kinematic fields at interfaces between dissimilar materials. Speckle images from both numerical and actual experiments processed by the described global DIC technique better captured sharp strain gradient at the interface than local subset-based DIC. © 2016 Elsevier Ltd. All rights reserved.

  11. Analysis of algebraic reconstruction technique for accurate imaging of gas temperature and concentration based on tunable diode laser absorption spectroscopy

    Science.gov (United States)

    Hui-Hui, Xia; Rui-Feng, Kan; Jian-Guo, Liu; Zhen-Yu, Xu; Ya-Bai, He

    2016-06-01

    An improved algebraic reconstruction technique (ART) combined with tunable diode laser absorption spectroscopy(TDLAS) is presented in this paper for determining two-dimensional (2D) distribution of H2O concentration and temperature in a simulated combustion flame. This work aims to simulate the reconstruction of spectroscopic measurements by a multi-view parallel-beam scanning geometry and analyze the effects of projection rays on reconstruction accuracy. It finally proves that reconstruction quality dramatically increases with the number of projection rays increasing until more than 180 for 20 × 20 grid, and after that point, the number of projection rays has little influence on reconstruction accuracy. It is clear that the temperature reconstruction results are more accurate than the water vapor concentration obtained by the traditional concentration calculation method. In the present study an innovative way to reduce the error of concentration reconstruction and improve the reconstruction quality greatly is also proposed, and the capability of this new method is evaluated by using appropriate assessment parameters. By using this new approach, not only the concentration reconstruction accuracy is greatly improved, but also a suitable parallel-beam arrangement is put forward for high reconstruction accuracy and simplicity of experimental validation. Finally, a bimodal structure of the combustion region is assumed to demonstrate the robustness and universality of the proposed method. Numerical investigation indicates that the proposed TDLAS tomographic algorithm is capable of detecting accurate temperature and concentration profiles. This feasible formula for reconstruction research is expected to resolve several key issues in practical combustion devices. Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61205151), the National Key Scientific Instrument and Equipment Development Project of China (Grant

  12. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

    Directory of Open Access Journals (Sweden)

    Snowdon Stuart

    2009-07-01

    Full Text Available Abstract Background Metabolomics experiments using Mass Spectrometry (MS technology measure the mass to charge ratio (m/z and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50% of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data.

  13. Accurate prediction of HIV-1 drug response from the reverse transcriptase and protease amino acid sequences using sparse models created by convex optimization.

    Science.gov (United States)

    Rabinowitz, Matthew; Myers, Lance; Banjevic, Milena; Chan, Albert; Sweetkind-Singer, Joshua; Haberer, Jessica; McCann, Kelly; Wolkowicz, Roland

    2006-03-01

    Genotype-phenotype modeling problems are often overcomplete, or ill-posed, since the number of potential predictors-genes, proteins, mutations and their interactions-is large relative to the number of measured outcomes. Such datasets can still be used to train sparse parameter models that generalize accurately, by exerting a principle similar to Occam's Razor: When many possible theories can explain the observations, the most simple is most likely to be correct. We apply this philosophy to modeling the drug response of Type-1 Human Immunodeficiency Virus (HIV-1). Owing to the decreasing expense of genetic sequencing relative to in vitro phenotype testing, a statistical model that reliably predicts viral drug response from genetic data is an important tool in the selection of antiretroviral therapy (ART). The optimization techniques described will have application to many genotype-phenotype modeling problems for the purpose of enhancing clinical decisions. We describe two regression techniques for predicting viral phenotype in response to ART from genetic sequence data. Both techniques employ convex optimization for the continuous subset selection of a sparse set of model parameters. The first technique, the least absolute shrinkage and selection operator, uses the l(1) norm loss function to create a sparse linear model; the second, the support vector machine with radial basis kernel functions, uses the epsilon-insensitive loss function to create a sparse non-linear model. The techniques are applied to predict the response of the HIV-1 virus to 10 reverse transcriptase inhibitor and 7 protease inhibitor drugs. The genetic data are derived from the HIV coding sequences for the reverse transcriptase and protease enzymes. When tested by cross-validation with actual laboratory measurements, these models predict drug response phenotype more accurately than models previously discussed in the literature, and other canonical techniques described here. Key features of the

  14. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

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    Bendl, Jaroslav; Musil, Miloš; Štourač, Jan; Zendulka, Jaroslav; Damborský, Jiří; Brezovský, Jan

    2016-05-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To

  15. Noninvasive Multimodal Imaging to Predict Recovery of Locomotion after Extended Limb Ischemia.

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    Jason S Radowsky

    Full Text Available Acute limb ischemia is a common cause of morbidity and mortality following trauma both in civilian centers and in combat related injuries. Rapid determination of tissue viability and surgical restoration of blood flow are desirable, but not always possible. We sought to characterize the response to increasing periods of hind limb ischemia in a porcine model such that we could define a period of critical ischemia (the point after which irreversible neuromuscular injury occurs, evaluate non-invasive methods for characterizing that ischemia, and establish a model by which we could predict whether or not the animal's locomotion would return to baselines levels post-operatively. Ischemia was induced by either application of a pneumatic tourniquet or vessel occlusion (performed by clamping the proximal iliac artery and vein at the level of the inguinal ligament. The limb was monitored for the duration of the procedure with both 3-charge coupled device (3CCD and infrared (IR imaging for tissue oxygenation and perfusion, respectively. The experimental arms of this model are effective at inducing histologically evident muscle injury with some evidence of expected secondary organ damage, particularly in animals with longer ischemia times. Noninvasive imaging data shows excellent correlation with post-operative functional outcomes, validating its use as a non-invasive means of viability assessment, and directly monitors post-occlusive reactive hyperemia. A classification model, based on partial-least squares discriminant analysis (PLSDA of imaging variables only, successfully classified animals as "returned to normal locomotion" or "did not return to normal locomotion" with 87.5% sensitivity and 66.7% specificity after cross-validation. PLSDA models generated from non-imaging data were not as accurate (AUC of 0.53 compared the PLSDA model generated from only imaging data (AUC of 0.76. With some modification, this limb ischemia model could also serve as a

  16. Subtraction MRI versus diffusion weighted imaging: Which is more accurate in assessment of hepatocellular carcinoma after Trans Arterial Chemoembolization (TACE?

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    Noha Abd ElShafy ElSaid

    2016-12-01

    Full Text Available This study aims to compare the role of both subtraction MRI and Diffusion weighted images in assessment of HCC after trans-arterial chemo embolization (TACE. Patients and methods: 32 patients having 54 HCC lesions underwent TACE. Ages ranged between 59 and 73 years (mean age 59.6; there were 26 males and 6 females. All examinations were performed using Philips 1.5 T MRI (Achieva. Pre contrast: T1, T2W and In phase and out phase gradient echo sequence, dynamic: eTHRIVE technique performed. Subtraction of an unenhanced T1-weighted was from the identical post enhancement sequences in early angiographic and late arterial phases. Diffusion imaging: it was performed using single-shot spin-echo echo-planar imaging with b = 0, 300, 600 mm/s2. DWI was used to create ADC maps. Two readers blinded to each other assessed subtraction of dynamic study and DWI technique to evaluate post treatment response. Results: Reader 1 subtraction dynamic MRI sensitivity = 97%, specificity = 100% PPV = 100% and NPV = 95% compared to 70.59%, 75%, 82.76% and 60% respectively in DWI. Reader 2 subtraction dynamic MRI sensitivity = 97%, specificity = 100% PPV = 100% and NPV = 95% compared to 76.5%, 90%, 92.8% and 69% respectively in DWI. Conclusion: DW-MRI had lower accuracy measures compared to subtraction MRI with increased false negative. DWI may act as a supplementary sequence to compensate for the dynamic MRI in patients who couldn't hold their breath adequately.

  17. Discovery of a general method of solving the Schrödinger and dirac equations that opens a way to accurately predictive quantum chemistry.

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    Nakatsuji, Hiroshi

    2012-09-18

    Just as Newtonian law governs classical physics, the Schrödinger equation (SE) and the relativistic Dirac equation (DE) rule the world of chemistry. So, if we can solve these equations accurately, we can use computation to predict chemistry precisely. However, for approximately 80 years after the discovery of these equations, chemists believed that they could not solve SE and DE for atoms and molecules that included many electrons. This Account reviews ideas developed over the past decade to further the goal of predictive quantum chemistry. Between 2000 and 2005, I discovered a general method of solving the SE and DE accurately. As a first inspiration, I formulated the structure of the exact wave function of the SE in a compact mathematical form. The explicit inclusion of the exact wave function's structure within the variational space allows for the calculation of the exact wave function as a solution of the variational method. Although this process sounds almost impossible, it is indeed possible, and I have published several formulations and applied them to solve the full configuration interaction (CI) with a very small number of variables. However, when I examined analytical solutions for atoms and molecules, the Hamiltonian integrals in their secular equations diverged. This singularity problem occurred in all atoms and molecules because it originates from the singularity of the Coulomb potential in their Hamiltonians. To overcome this problem, I first introduced the inverse SE and then the scaled SE. The latter simpler idea led to immediate and surprisingly accurate solution for the SEs of the hydrogen atom, helium atom, and hydrogen molecule. The free complement (FC) method, also called the free iterative CI (free ICI) method, was efficient for solving the SEs. In the FC method, the basis functions that span the exact wave function are produced by the Hamiltonian of the system and the zeroth-order wave function. These basis functions are called complement

  18. An improved approach for accurate and efficient measurement of common carotid artery intima-media thickness in ultrasound images.

    Science.gov (United States)

    Li, Qiang; Zhang, Wei; Guan, Xin; Bai, Yu; Jia, Jing

    2014-01-01

    The intima-media thickness (IMT) of common carotid artery (CCA) can serve as an important indicator for the assessment of cardiovascular diseases (CVDs). In this paper an improved approach for automatic IMT measurement with low complexity and high accuracy is presented. 100 ultrasound images from 100 patients were tested with the proposed approach. The ground truth (GT) of the IMT was manually measured for six times and averaged, while the automatic segmented (AS) IMT was computed by the algorithm proposed in this paper. The mean difference±standard deviation between AS and GT IMT is 0.0231±0.0348 mm, and the correlation coefficient between them is 0.9629. The computational time is 0.3223 s per image with MATLAB under Windows XP on an Intel Core 2 Duo CPU E7500 @2.93 GHz. The proposed algorithm has the potential to achieve real-time measurement under Visual Studio.

  19. An Improved Approach for Accurate and Efficient Measurement of Common Carotid Artery Intima-Media Thickness in Ultrasound Images

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2014-01-01

    Full Text Available The intima-media thickness (IMT of common carotid artery (CCA can serve as an important indicator for the assessment of cardiovascular diseases (CVDs. In this paper an improved approach for automatic IMT measurement with low complexity and high accuracy is presented. 100 ultrasound images from 100 patients were tested with the proposed approach. The ground truth (GT of the IMT was manually measured for six times and averaged, while the automatic segmented (AS IMT was computed by the algorithm proposed in this paper. The mean difference ± standard deviation between AS and GT IMT is 0.0231 ± 0.0348 mm, and the correlation coefficient between them is 0.9629. The computational time is 0.3223 s per image with MATLAB under Windows XP on an Intel Core 2 Duo CPU E7500 @2.93 GHz. The proposed algorithm has the potential to achieve real-time measurement under Visual Studio.

  20. The Need for Accurate Risk Prediction Models for Road Mapping, Shared Decision Making and Care Planning for the Elderly with Advanced Chronic Kidney Disease.

    Science.gov (United States)

    Stryckers, Marijke; Nagler, Evi V; Van Biesen, Wim

    2016-11-01

    As people age, chronic kidney disease becomes more common, but it rarely leads to end-stage kidney disease. When it does, the choice between dialysis and conservative care can be daunting, as much depends on life expectancy and personal expectations of medical care. Shared decision making implies adequately informing patients about their options, and facilitating deliberation of the available information, such that decisions are tailored to the individual's values and preferences. Accurate estimations of one's risk of progression to end-stage kidney disease and death with or without dialysis are essential for shared decision making to be effective. Formal risk prediction models can help, provided they are externally validated, well-calibrated and discriminative; include unambiguous and measureable variables; and come with readily applicable equations or scores. Reliable, externally validated risk prediction models for progression of chronic kidney disease to end-stage kidney disease or mortality in frail elderly with or without chronic kidney disease are scant. Within this paper, we discuss a number of promising models, highlighting both the strengths and limitations physicians should understand for using them judiciously, and emphasize the need for external validation over new development for further advancing the field.

  1. Forecasting irrigation demand by assimilating satellite images and numerical weather predictions

    Science.gov (United States)

    Pelosi, Anna; Medina, Hanoi; Villani, Paolo; Falanga Bolognesi, Salvatore; D'Urso, Guido; Battista Chirico, Giovanni

    2016-04-01

    Forecasting irrigation water demand, with small predictive uncertainty in the short-medium term, is fundamental for an efficient planning of water resource allocation among multiple users and for decreasing water and energy consumptions. In this study we present an innovative system for forecasting irrigation water demand, applicable at different spatial scales: from the farm level to the irrigation district level. The forecast system is centred on a crop growth model assimilating data from satellite images and numerical weather forecasts, according to a stochastic ensemble-based approach. Different sources of uncertainty affecting model predictions are represented by an ensemble of model trajectories, each generated by a possible realization of the model components (model parameters, input weather data and model state variables). The crop growth model is based on a set of simplified analytical relations, with the aim to assess biomass, leaf area index (LAI) growth and evapotranspiration rate with a daily time step. Within the crop growth model, LAI dynamics is let be governed by temperature and leaf dry matter supply, according to the development stage of the crop. The model assimilates LAI data retrieved from VIS-NIR high-resolution multispectral satellite images. Numerical weather model outputs are those from the European limited area ensemble prediction system (COSMO-LEPS), which provides forecasts up to five days with a spatial resolution of seven kilometres. Weather forecasts are sequentially bias corrected based on data from ground weather stations. The forecasting system is evaluated in experimental areas of southern Italy during three irrigation seasons. The performance analysis shows very accurate irrigation water demand forecasts, which make the proposed system a valuable support for water planning and saving at farm level as well as for water management at larger spatial scales.

  2. Bioluminescence in vivo imaging of autoimmune encephalomyelitis predicts disease

    Directory of Open Access Journals (Sweden)

    Steinman Lawrence

    2008-02-01

    Full Text Available Abstract Background Experimental autoimmune encephalomyelitis is a widely used animal model to understand not only multiple sclerosis but also basic principles of immunity. The disease is scored typically by observing signs of paralysis, which do not always correspond with pathological changes. Methods Experimental autoimmune encephalomyelitis was induced in transgenic mice expressing an injury responsive luciferase reporter in astrocytes (GFAP-luc. Bioluminescence in the brain and spinal cord was measured non-invasively in living mice. Mice were sacrificed at different time points to evaluate clinical and pathological changes. The correlation between bioluminescence and clinical and pathological EAE was statistically analyzed by Pearson correlation analysis. Results Bioluminescence from the brain and spinal cord correlates strongly with severity of clinical disease and a number of pathological changes in the brain in EAE. Bioluminescence at early time points also predicts severity of disease. Conclusion These results highlight the potential use of bioluminescence imaging to monitor neuroinflammation for rapid drug screening and immunological studies in EAE and suggest that similar approaches could be applied to other animal models of autoimmune and inflammatory disorders.

  3. MR diffusion-weighted imaging-based subcutaneous tumour volumetry in a xenografted nude mouse model using 3D Slicer: an accurate and repeatable method

    Science.gov (United States)

    Ma, Zelan; Chen, Xin; Huang, Yanqi; He, Lan; Liang, Cuishan; Liang, Changhong; Liu, Zaiyi

    2015-01-01

    Accurate and repeatable measurement of the gross tumour volume(GTV) of subcutaneous xenografts is crucial in the evaluation of anti-tumour therapy. Formula and image-based manual segmentation methods are commonly used for GTV measurement but are hindered by low accuracy and reproducibility. 3D Slicer is open-source software that provides semiautomatic segmentation for GTV measurements. In our study, subcutaneous GTVs from nude mouse xenografts were measured by semiautomatic segmentation with 3D Slicer based on morphological magnetic resonance imaging(mMRI) or diffusion-weighted imaging(DWI)(b = 0,20,800 s/mm2) . These GTVs were then compared with those obtained via the formula and image-based manual segmentation methods with ITK software using the true tumour volume as the standard reference. The effects of tumour size and shape on GTVs measurements were also investigated. Our results showed that, when compared with the true tumour volume, segmentation for DWI(P = 0.060–0.671) resulted in better accuracy than that mMRI(P < 0.001) and the formula method(P < 0.001). Furthermore, semiautomatic segmentation for DWI(intraclass correlation coefficient, ICC = 0.9999) resulted in higher reliability than manual segmentation(ICC = 0.9996–0.9998). Tumour size and shape had no effects on GTV measurement across all methods. Therefore, DWI-based semiautomatic segmentation, which is accurate and reproducible and also provides biological information, is the optimal GTV measurement method in the assessment of anti-tumour treatments. PMID:26489359

  4. Prediction of cerebral hyperperfusion syndrome after carotid artery stenting by CT perfusion imaging with acetazolamide challenge

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    Yoshie, Tomohide; Ueda, Toshihiro; Takada, Tatsuro; Nogoshi, Shinji; Fukano, Takayuki [St. Marianna University Toyoko Hospital, Department of Strokology, Stroke Center, Kawasaki (Japan); Hasegawa, Yasuhiro [St. Marianna University School of Medicine, Department of Internal Medicine, Division of Neurology, Kawasaki (Japan)

    2016-03-15

    Cerebral hyperperfusion syndrome (HPS) is an uncommon but serious complication of carotid artery stenting (CAS). The purpose of this study was to investigate the efficacy of CT perfusion imaging (CTP) with acetazolamide challenge to identify patients at risk for HPS after CAS. We retrospectively analyzed 113 patients who underwent CTP with rest and acetazolamide challenge before CAS. CTP maps were assessed for absolute and relative cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and change of each parameter before and after acetazolamide challenge. Patients were divided into two groups according to the HPS after the CAS. Receiver-operating characteristic (ROC) curve analysis was performed to determine the most accurate CTP parameter for the prediction of HPS. Nine of 113 patients had HPS. There were significant differences for absolute and relative values of resting CBF (p = 0.001 and p = 0.026), resting MTT (p < 0.001 and p = 0.004), post-acetazolamide CBF (p < 0.001 and p = 0.001), post-acetazolamide MTT (p < 0.001 and p = 0.002), and %changes of CBF (p = 0.009) between the HPS and non-HPS groups. ROC curve analysis showed that the CTP parameters with the maximal area under the receiver-operating characteristic curve (AUC) for HPS was the absolute value of post-acetazolamide MTT (AUC 0.909) and the absolute value of resting MTT (AUC 0.896). Pretreatment CTP with acetazolamide challenge could identify patients at risk for HPS after CAS. Although the CTP parameter that most accurately identified patients at risk for HPS was the absolute value of post-acetazolamide MTT, resting MTT was sufficiently accurate. (orig.)

  5. Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic features in tumor habitats

    Science.gov (United States)

    Chaudhury, Baishali; Zhou, Mu; Farhidzadeh, Hamidreza; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Weinfurtner, Robert J.; Drukteinis, Jennifer S.

    2016-03-01

    The use of Ki67% expression, a cell proliferation marker, as a predictive and prognostic factor has been widely studied in the literature. Yet its usefulness is limited due to inconsistent cut off scores for Ki67% expression, subjective differences in its assessment in various studies, and spatial variation in expression, which makes it difficult to reproduce as a reliable independent prognostic factor. Previous studies have shown that there are significant spatial variations in Ki67% expression, which may limit its clinical prognostic utility after core biopsy. These variations are most evident when examining the periphery of the tumor vs. the core. To date, prediction of Ki67% expression from quantitative image analysis of DCE-MRI is very limited. This work presents a novel computer aided diagnosis framework to use textural kinetics to (i) predict the ratio of periphery Ki67% expression to core Ki67% expression, and (ii) predict Ki67% expression from individual tumor habitats. The pilot cohort consists of T1 weighted fat saturated DCE-MR images from 17 patients. Support vector regression with a radial basis function was used for predicting the Ki67% expression and ratios. The initial results show that texture features from individual tumor habitats are more predictive of the Ki67% expression ratio and spatial Ki67% expression than features from the whole tumor. The Ki67% expression ratio could be predicted with a root mean square error (RMSE) of 1.67%. Quantitative image analysis of DCE-MRI using textural kinetic habitats, has the potential to be used as a non-invasive method for predicting Ki67 percentage and ratio, thus more accurately reporting high KI-67 expression for patient prognosis.

  6. Human cell structure-driven model construction for predicting protein subcellular location from biological images.

    Science.gov (United States)

    Shao, Wei; Liu, Mingxia; Zhang, Daoqiang

    2016-01-01

    The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting. We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method. The dataset and code can be downloaded from https://github.com/shaoweinuaa/. dqzhang@nuaa.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  7. A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters☆

    Science.gov (United States)

    Liu, Hong; Wang, Jie; Xu, Xiangyang; Song, Enmin; Wang, Qian; Jin, Renchao; Hung, Chih-Cheng; Fei, Baowei

    2015-01-01

    A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod. PMID:25087857

  8. Conceptual design and algorithm evaluation for a very accurate imaging star tracker for deep- space optical communications

    Science.gov (United States)

    Maurer, Donald E.; Boone, Bradley G.

    2002-12-01

    The National Aeronautics and Space Administration is planning high data rate optical communications for future deep space missions. The Johns Hopkins University Applied Physics Laboratory (JHU/APL) is responding by developing concepts for implementing optical communications terminals that are more compact and lightweight than heretofore. An essential requirement for these long-range optical links is a high-precision pointing and tracking system. Focal plane array (FPA)-based star trackers that enable open-loop pointing and tracking are necessary. Spacecraft attitude instabilities, emphemeris errors, tracking sensor noise, clock errors, and mechanical misalignments are among the error sources that must be minimized and compensated for. To achieve this JHU/APL has developed an imaging star tracker concept using redundant multi-aperture FPA's symmetrically disposed about the laser downlink. Centroid estimation and pattern matching techniques account for aberration and motion errors. Robustness, sensitivity to detection thresholds, field-of-view sizing, number of stars per frame, missed detections, false alarms, and position biases, as well as stellar catalog size and star selection, will be described. Finally the conceptual design of a frame-to-frame integration method and sensor fusion algorithm (such as a Kalman filter) will be considered. The goal is to achieve a system pointing and tracking error significantly less than 1 μrad.

  9. An efficient numerical tool for dose deposition prediction applied to synchrotron medical imaging and radiation therapy.

    Science.gov (United States)

    Mittone, Alberto; Baldacci, Fabien; Bravin, Alberto; Brun, Emmanuel; Delaire, François; Ferrero, Claudio; Gasilov, Sergei; Freud, Nicolas; Létang, Jean Michel; Sarrut, David; Smekens, François; Coan, Paola

    2013-09-01

    Medical imaging and radiation therapy are widely used synchrotron-based techniques which have one thing in common: a significant dose delivery to typically biological samples. Among the ways to provide the experimenters with image guidance techniques indicating optimization strategies, Monte Carlo simulation has become the gold standard for accurately predicting radiation dose levels under specific irradiation conditions. A highly important hampering factor of this method is, however, its slow statistical convergence. A track length estimator (TLE) module has been coded and implemented for the first time in the open-source Monte Carlo code GATE/Geant4. Results obtained with the module and the procedures used to validate them are presented. A database of energy-absorption coefficients was also generated, which is used by the TLE calculations and is now also included in GATE/Geant4. The validation was carried out by comparing the TLE-simulated doses with experimental data in a synchrotron radiation computed tomography experiment. The TLE technique shows good agreement versus both experimental measurements and the results of a classical Monte Carlo simulation. Compared with the latter, it is possible to reach a pre-defined statistical uncertainty in about two to three orders of magnitude less time for complex geometries without loss of accuracy.

  10. Calibrating transition-metal energy levels and oxygen bands in first-principles calculations: Accurate prediction of redox potentials and charge transfer in lithium transition-metal oxides

    Science.gov (United States)

    Seo, Dong-Hwa; Urban, Alexander; Ceder, Gerbrand

    2015-09-01

    Transition-metal (TM) oxides play an increasingly important role in technology today, including applications such as catalysis, solar energy harvesting, and energy storage. In many of these applications, the details of their electronic structure near the Fermi level are critically important for their properties. We propose a first-principles-based computational methodology for the accurate prediction of oxygen charge transfer in TM oxides and lithium TM (Li-TM) oxides. To obtain accurate electronic structures, the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional is adopted, and the amount of exact Hartree-Fock exchange (mixing parameter) is adjusted to reproduce reference band gaps. We show that the HSE06 functional with optimal mixing parameter yields not only improved electronic densities of states, but also better energetics (Li-intercalation voltages) for LiCo O2 and LiNi O2 as compared to the generalized gradient approximation (GGA), Hubbard U corrected GGA (GGA +U ), and standard HSE06. We find that the optimal mixing parameters for TM oxides are system specific and correlate with the covalency (ionicity) of the TM species. The strong covalent (ionic) nature of TM-O bonding leads to lower (higher) optimal mixing parameters. We find that optimized HSE06 functionals predict stronger hybridization of the Co 3 d and O 2 p orbitals as compared to GGA, resulting in a greater contribution from oxygen states to charge compensation upon delithiation in LiCo O2 . We also find that the band gaps of Li-TM oxides increase linearly with the mixing parameter, enabling the straightforward determination of optimal mixing parameters based on GGA (α =0.0 ) and HSE06 (α =0.25 ) calculations. Our results also show that G0W0@GGA +U band gaps of TM oxides (M O ,M =Mn ,Co ,Ni ) and LiCo O2 agree well with experimental references, suggesting that G0W0 calculations can be used as a reference for the calibration of the mixing parameter in cases when no experimental band gap has been

  11. Establishing magnetic resonance imaging as an accurate and reliable tool to diagnose and monitor esophageal cancer in a rat model.

    Directory of Open Access Journals (Sweden)

    Juliann E Kosovec

    Full Text Available OBJECTIVE: To assess the reliability of magnetic resonance imaging (MRI for detection of esophageal cancer in the Levrat model of end-to-side esophagojejunostomy. BACKGROUND: The Levrat model has proven utility in terms of its ability to replicate Barrett's carcinogenesis by inducing gastroduodenoesophageal reflux (GDER. Due to lack of data on the utility of non-invasive methods for detection of esophageal cancer, treatment efficacy studies have been limited, as adenocarcinoma histology has only been validated post-mortem. It would therefore be of great value if the validity and reliability of MRI could be established in this setting. METHODS: Chronic GDER reflux was induced in 19 male Sprague-Dawley rats using the modified Levrat model. At 40 weeks post-surgery, all animals underwent endoscopy, MRI scanning, and post-mortem histological analysis of the esophagus and anastomosis. With post-mortem histology serving as the gold standard, assessment of presence of esophageal cancer was made by five esophageal specialists and five radiologists on endoscopy and MRI, respectively. RESULTS: The accuracy of MRI and endoscopic analysis to correctly identify cancer vs. no cancer was 85.3% and 50.5%, respectively. ROC curves demonstrated that MRI rating had an AUC of 0.966 (p<0.001 and endoscopy rating had an AUC of 0.534 (p = 0.804. The sensitivity and specificity of MRI for identifying cancer vs. no-cancer was 89.1% and 80% respectively, as compared to 45.5% and 57.5% for endoscopy. False positive rates of MRI and endoscopy were 20% and 42.5%, respectively. CONCLUSIONS: MRI is a more reliable diagnostic method than endoscopy in the Levrat model. The non-invasiveness of the tool and its potential to volumetrically quantify the size and number of tumors likely makes it even more useful in evaluating novel agents and their efficacy in treatment studies of esophageal cancer.

  12. Prognosis versus actual outcome. IV. The effectiveness of clinical parameters and IL-1 genotype in accurately predicting prognoses and tooth survival.

    Science.gov (United States)

    McGuire, M K; Nunn, M E

    1999-01-01

    Recently, a genetic marker (IL-1 genotype) that identifies individuals at higher risk for developing severe periodontal disease was discovered. A subgroup of the population reported on earlier was evaluated to determine if knowledge of the patient's IL-1 genotype would improve accuracy in assignment of prognoses and prediction of tooth loss. This subgroup consisted of 42 patients (1,044 teeth) in maintenance care for 14 years; 16 tested IL-1 genotype-positive (IL-1GP). Nine were smokers, and 30 had a history of smoking, with an average of 29.44 pack years. A multiple Cox regression model and Kaplan-Meier survival plots were fit to the subset of patients to evaluate tooth loss. Both IL-1GP and heavy smoking were significantly related to tooth loss. A positive IL-1 genotype increased the risk of tooth loss by 2.7 times, and heavy smoking by 2.9 times. The combined effect of IL-1GP and heavy smoking increased the risk of tooth loss by 7.7 times. The value of clinical parameters traditionally used to assign prognosis was found to be dependent on IL-genotype and smoking status. In the model that included IL-1 genotype and heavy smoking, none of the clinical parameters added significantly to the model for tooth loss while mobility, probing depth, crown-to-root ratio, and percent bone loss added significantly to the model, which included IL-1 genotype in non-smokers. IL-1GP patients and patients who smoked heavily demonstrated a much worse tooth survival rate when compared to IL-1 genotype-negative patients and non-smokers, respectively. Knowledge of the patient's IL-1 genotype and smoking status will improve the clinician's ability to accurately assign prognosis and predict tooth survival. Clinical implications are as follows. Investigators were unable to judge which patients would be IL-GP or negative based on their clinical presentation or family history of tooth loss due to periodontal disease. Since periodontal diseases are multifactorial, knowledge of the patient

  13. MR imaging of hypoglycemic encephalopathy: lesion distribution and prognosis prediction by diffusion-weighted imaging

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Jeong-Hyun; Kim, Young-Joo; Yoo, Won-Jong; Ihn, Yon-Kwon; Kim, Jee-Young; Kim, Bum-Soo [The Catholic University of Korea, Department of Radiology, College of Medicine, Uijongbu, Kyunggi-do (Korea); Song, Ha-Hun [Cheju Halla General Hospital, Department of Radiology, Jeju (Korea)

    2009-10-15

    The aim of this study was to evaluate the patterns of hypoglycemic encephalopathy on diffusion-weighted imaging (DWI) and the relationship between the imaging patterns and clinical outcomes. This retrospective study included 17 consecutive patients that had hypoglycemic encephalopathy with DWI abnormalities. The topographic distributions of the DWI abnormalities of the cortex, deep gray matter, and white matter structures were assessed. In addition, possible correlation between the patterns of brain injury on DWI and clinical outcomes was investigated. There were three patterns of DWI abnormalities: involvement of both gray and white matter (n=8), selective involvement of gray matter (n=4), and selective involvement of white matter (n=5). There was no significant difference in the initial blood glucose levels among patients for each of the imaging patterns. Most patients (16/17) had bilateral symmetrical abnormalities. Among patients with bilateral symmetrical gray and/or white matter injuries, one had moderate to severe disability and 14 remained in a persistent vegetative state. The two patients with a focal unilateral white matter abnormality and a localized splenial abnormality recovered without neurological deficits. The results of this study showed that white matter was more sensitive to hypoglycemia than previously thought and there was no specific association between the patterns of injury and clinical outcomes whether the cerebral cortex, deep gray matter, and/or white matter were affected. Diffuse and extensive injury observed on the DWI predicts a poor neurologic outcome in patients with hypoglycemic injuries. (orig.)

  14. Shadow imaging of geosynchronous satellites: simulation, image reconstruction, and shadow prediction

    Science.gov (United States)

    Douglas, Dennis M.; Hunt, Bobby R.; Sheppard, David G.

    2016-09-01

    Shadow imaging is a technique to obtain highly resolved silhouettes of resident space objects (RSOs) which would otherwise be unattainable using conventional terrestrial based imaging approaches. This is done by post processing the measured irradiance pattern (shadow) cast onto the Earth as the RSO occults a star. The research presented here focuses on shadow imaging of geosynchronous (GEO) satellites with near stationary orbits approximately 36,000 km from the Earth. Shadows pertaining to a set of diverse observing scenarios are simulated and used as inputs to a Fresnel based phase retrieval algorithm. Spatial resolution limits are evaluated and correlated to signal to noise (SNR) metrics. Resolvable feature sizes of less than 1 m are shown to be readily achievable using foreseeable observing scenarios. Initial output from a shadow prediction tool indicates that there are, on average, over 1000 shadows on the Earth on any given time from a single GEO satellite for stars brighter than mv=10. Shadow ground track uncertainties are correlated to stellar astrometric errors. Global and localized shadow track maps are presented demonstrating a high feasibility for future shadow collections.

  15. IMPre: an accurate and efficient software for prediction of T- and B-cell receptor germline genes and alleles from rearranged repertoire data

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2016-11-01

    Full Text Available Large-scale study of the properties of T-cell receptor (TCR and B-cell receptor (BCR repertoires through next-generation sequencing is providing excellent insights into the understanding of adaptive immune responses. Variable(DiversityJoining V(DJ germline genes and alleles must be characterized in detail to facilitate repertoire analyses. However, most species do not have well-characterized TCR/BCR germline genes because of their high homology. Also, more germline alleles are required for humans and other species, which limits the capacity for studying immune repertoires. Herein, we developed Immune Germline Prediction (IMPre, a tool for predicting germline V/J genes and alleles using deep-sequencing data derived from TCR/BCR repertoires. We developed a new algorithm, Seed_Clust, for clustering, produced a multiway tree for assembly and optimized the sequence according to the characteristics of rearrangement. We trained IMPre on human samples of T-cell receptor beta (TRB and immunoglobulin heavy chain (IGH, and then tested it on additional human samples. Accuracy of 97.7%, 100%, 92.9% and 100% was obtained for TRBV, TRBJ, IGHV and IGHJ, respectively. Analyses of subsampling performance for these samples showed IMPre to be robust using different data quantities. Subsequently, IMPre was tested on samples from rhesus monkeys and human long sequences: the highly accurate results demonstrated IMPre to be stable with animal and multiple data types. With rapid accumulation of high-throughput sequence data for TCR and BCR repertoires, IMPre can be applied broadly for obtaining novel genes and a large number of novel alleles. IMPre is available at https://github.com/zhangwei2015/IMPre.

  16. Integrating trans-abdominal ultrasonography with fecal steroid metabolite monitoring to accurately diagnose pregnancy and predict the timing of parturition in the red panda (Ailurus fulgens styani).

    Science.gov (United States)

    Curry, Erin; Browning, Lissa J; Reinhart, Paul; Roth, Terri L

    2017-02-23

    Red pandas (Ailurus fulgens styani) exhibit a variable gestation length and may experience a pseudopregnancy indistinguishable from true pregnancy; therefore, it is not possible to deduce an individual's true pregnancy status and parturition date based on breeding dates or fecal progesterone excretion patterns alone. The goal of this study was to evaluate the use of transabdominal ultrasonography for pregnancy diagnosis in red pandas. Two to three females were monitored over 4 consecutive years, generating a total of seven profiles (four pregnancies, two pseudopregnancies, and one lost pregnancy). Fecal samples were collected and assayed for progesterone (P4) and estrogen conjugate (EC) to characterize patterns associated with breeding activity and parturition events. Animals were trained for voluntary transabdominal ultrasound and examinations were performed weekly. Breeding behaviors and fecal EC data suggest that the estrus cycle of this species is 11-12 days in length. Fecal steroid metabolite analyses also revealed that neither P4 nor EC concentrations were suitable indicators of pregnancy in this species; however, a secondary increase in P4 occurred 69-71 days prior to parturition in all pregnant females, presumably coinciding with embryo implantation. Using ultrasonography, embryos were detected as early as 62 days post-breeding/50 days pre-partum and serial measurements of uterine lumen diameter were documented throughout four pregnancies. Advances in reproductive diagnostics, such as the implementation of ultrasonography, may facilitate improved husbandry of pregnant females and allow for the accurate prediction of parturition. © 2017 Wiley Periodicals, Inc.

  17. Highly accurate chemical formula prediction tool utilizing high-resolution mass spectra, MS/MS fragmentation, heuristic rules, and isotope pattern matching.

    Science.gov (United States)

    Pluskal, Tomáš; Uehara, Taisuke; Yanagida, Mitsuhiro

    2012-05-15

    Mass spectrometry is commonly applied to qualitatively and quantitatively profile small molecules, such as peptides, metabolites, or lipids. Modern mass spectrometers provide accurate measurements of mass-to-charge ratios of ions, with errors as low as 1 ppm. Even such high mass accuracy, however, is not sufficient to determine the unique chemical formula of each ion, and additional algorithms are necessary. Here we present a universal software tool for predicting chemical formulas from high-resolution mass spectrometry data, developed within the MZmine 2 framework. The tool is based on the use of a combination of heuristic techniques, including MS/MS fragmentation analysis and isotope pattern matching. The performance of the tool was evaluated using a real metabolomic data set obtained with the Orbitrap MS detector. The true formula was correctly determined as the highest-ranking candidate for 79% of the tested compounds. The novel isotope pattern-scoring algorithm outperformed a previously published method in 64% of the tested Orbitrap spectra. The software described in this manuscript is freely available and its source code can be accessed within the MZmine 2 source code repository.

  18. Artifacts resulting from imaging in scattering media: a theoretical prediction.

    Science.gov (United States)

    Rohrbach, Alexander

    2009-10-01

    Scattering of illumination light from a laser is a severe problem especially when imaging in thick media. Although this effect occurs in nearly every imaging process, it can be well perceived and analyzed in configurations where the optical axes for illumination and detection are perpendicular to each other. In this paper I present a theoretical perspective of how to extend the point-spread function arithmetic from ideal imaging to realistic imaging including ghost images. These ghost images are generated by scattered light and are low-correlated with the ideal image. Numerical simulations of the propagation of four different types of illumination beams through a cluster of spheres illustrate the effects of inhomogeneous object illumination. Clear differences between a conventional plane-wave illumination, a static light-sheet, and a laterally scanned Gaussian beam, but also relative to a scanned Bessel beam, can be observed.

  19. Towards accurate dose accumulation for step- and -shoot IMRT. Impact of weighting schemes and temporal image resolution on the estimation of dosimetric motion effects

    Energy Technology Data Exchange (ETDEWEB)

    Werner, Rene; Ehrhardt, Jan; Schmidt-Richberg, Alexander; Handels, Heinz [Luebeck Univ. (Germany). Inst. of Medical Informatics; Albers, Dirk; Petersen, Cordula; Cremers, Florian [University Medical Center Hamburg-Eppendorf, Hamburg (Germany). Dept. of Radiotherapy and Radio-Oncology; Frenzel, Thorsten [University Medical Center Hamburg-Eppendorf, Hamburg (Germany). Health Care Center

    2012-07-01

    Purpose: Breathing-induced motion effects on dose distributions in radiotherapy can be analyzed using 4D CT image sequences and registration-based dose accumulation techniques. Often simplifying assumptions are made during accumulation. In this paper, we study the dosimetric impact of two aspects which may be especially critical for IMRT treatment: the weighting scheme for the dose contributions of IMRT segments at different breathing phases and the temporal resolution of 4D CT images applied for dose accumulation. Methods: Based on a continuous problem formulation a patient- and plan-specific scheme for weighting segment dose contributions at different breathing phases is derived for use in step- and -shoot IMRT dose accumulation. Using 4D CT data sets and treatment plans for 5 lung tumor patients, dosimetric motion effects as estimated by the derived scheme are compared to effects resulting from a common equal weighting approach. Effects of reducing the temporal image resolution are evaluated for the same patients and both weighting schemes. Results: The equal weighting approach underestimates dosimetric motion effects when considering single treatment fractions. Especially interplay effects (relative misplacement of segments due to respiratory tumor motion) for IMRT segments with only a few monitor units are insufficiently represented (local point differences > 25% of the prescribed dose for larger tumor motion). The effects, however, tend to be averaged out over the entire treatment course. Regarding temporal image resolution, estimated motion effects in terms of measures of the CTV dose coverage are barely affected (in comparison to the full resolution) when using only half of the original resolution and equal weighting. In contrast, occurence and impact of interplay effects are poorly captured for some cases (large tumor motion, undersized PTV margin) for a resolution of 10/14 phases and the more accurate patient- and plan-specific dose accumulation scheme

  20. What is the most accurate whole-body imaging modality for assessment of local and distant recurrent disease in colorectal cancer? A meta-analysis. Imaging for recurrent colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Maas, Monique; Lambregts, Doenja M.J. [Maastricht University Medical Centre, Department of Radiology, Maastricht (Netherlands); Maastricht University Medical Centre, Department of Surgery, Maastricht (Netherlands); Rutten, Iris J.G.; Cappendijk, Vincent C.; Beets-Tan, Regina G.H. [Maastricht University Medical Centre, Department of Radiology, Maastricht (Netherlands); Nelemans, Patty J. [Maastricht University, Department of Epidemiology, Maastricht (Netherlands); Beets, Geerard L. [Maastricht University Medical Centre, Department of Surgery, Maastricht (Netherlands)

    2011-08-15

    The objective of this study was to compare the diagnostic performance of positron emission tomography (PET), PET/CT, CT and MRI as whole-body imaging modalities for the detection of local and/or distant recurrent disease in colorectal cancer (CRC) patients who have a (high) suspicion of recurrent disease, based on clinical findings or rise in carcinoembryonic antigen (CEA). A meta-analysis was undertaken. PubMed and Embase were searched for studies on the accuracy of whole-body imaging for patients with suspected local and/or distant recurrence of their CRC. Additionally, studies had to have included at least 20 patients with CRC and 2 x 2 contingency tables had to be provided or derivable. Articles evaluating only local recurrence or liver metastasis were excluded. Summary receiver-operating characteristic (ROC) curves were constructed from the data on sensitivity and specificity of individual studies and pooled estimates of diagnostic odds ratios (DORs) and areas under the ROC curve (AUCs) were calculated. To test for heterogeneity the Cochran Q test was used. Fourteen observational studies were included which evaluated PET, PET/CT, CT and/or MRI. Study results were available in 12 studies for PET, in 5 studies for CT, in 5 studies for PET/CT and in 1 study for MRI. AUCs for PET, PET/CT and CT were 0.94 (0.90-0.97), 0.94 (0.87-0.98) and 0.83 (0.72-0.90), respectively. In patient based analyses PET/CT had a higher diagnostic performance than PET with an AUC of 0.95 (0.89-0.97) for PET/CT vs 0.92 (0.86-0.96) for PET. Both whole-body PET and PET/CT are very accurate for the detection of local and/or distant recurrent disease in CRC patients with a (high) suspicion of recurrent disease. CT has the lowest diagnostic performance. This difference is probably mainly due to the lower accuracy of CT for detection of extrahepatic metastases (including local recurrence). For clinical practice PET/CT might be the modality of choice when evaluating patients with a (high

  1. Characterization and Predictive Value of Near Infrared 2-Deoxyglucose Optical Imaging in Severe Acute Pancreatitis.

    Directory of Open Access Journals (Sweden)

    Cristiane de Oliveira

    Full Text Available Studying the uptake of 2-deoxy glucose (2-DG analogs such as 2-Deoxy-2-[18F] fluoroglucose (FDG is a common approach to identify and monitor malignancies and more recently chronic inflammation. While pancreatitis is a common cause for false positive results in human studies on pancreatic cancer using FDG, the relevance of these findings to acute pancreatitis (AP is unknown. FDG has a short half-life. Thus, with an aim to accurately characterize the metabolic demand of the pancreas during AP in real-time, we studied the uptake of the non-radioactive, near infrared fluorescence labelled 2-deoxyglucose analog, IRDye® 800CW 2-DG probe (NIR 2-DG; Li-Cor during mild and severe biliary AP.Wistar rats (300 g; 8-12/group were administered NIR 2-DG (10 nM; I.V.. Mild and severe biliary AP were respectively induced by biliopancreatic duct ligation (DL alone or along with infusing glyceryl trilinoleate (GTL; 50 μL/100 g within 10 minutes of giving NIR 2-DG. Controls (CON only received NIR 2-DG. Imaging was done every 5-10 minutes over 3 hrs. Average Radiant Efficiency [p/s/cm²/sr]/[μW/cm²] was measured over the pancreas using the IVIS 200 in-vivo imaging system (PerkinElmer using the Living Image® software and verified in ex vivo pancreata. Blood amylase, lipase and pancreatic edema, necrosis were measured over the course of AP.NIR 2-DG uptake over the first hour was not influenced by AP induction. However, while the signal declined in controls and rats with mild AP, there was significantly higher retention of NIR 2-DG in the pancreas after 1 hour in those with GTL pancreatitis. The increase was > 3 fold over controls in the GTL group and was verified to be in the pancreas ex vivo. In vitro, pancreatic acini exposed to GTL had a similar increase in NIR 2-DG uptake which was followed by progressively worse acinar necrosis. Greater retention of NIR 2-DG in vivo was associated with worse pancreatic necrosis, reduced ATP concentrations and mortality

  2. Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging

    DEFF Research Database (Denmark)

    Olesen, Merete Halkjær; Nikneshan, Pejman; Shrestha, Santosh;

    2015-01-01

    The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the s...

  3. MO-AB-BRA-10: Cancer Therapy Outcome Prediction Based On Dempster-Shafer Theory and PET Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Lian, C [Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7253 Heudiasyc, 60205 Compiegne (France); University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France); Li, H; Chen, H; Robinson, C. [Washington University School of Medicine, Saint Louis, MO (United States); Denoeux, T [Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7253 Heudiasyc, 60205 Compiegne (France); Vera, P [Centre Henri-Becquerel, 76038 Rouen (France); University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France); Ruan, S [University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen (France)

    2015-06-15

    Purpose: In cancer therapy, utilizing FDG-18 PET image-based features for accurate outcome prediction is challenging because of 1) limited discriminative information within a small number of PET image sets, and 2) fluctuant feature characteristics caused by the inferior spatial resolution and system noise of PET imaging. In this study, we proposed a new Dempster-Shafer theory (DST) based approach, evidential low-dimensional transformation with feature selection (ELT-FS), to accurately predict cancer therapy outcome with both PET imaging features and clinical characteristics. Methods: First, a specific loss function with sparse penalty was developed to learn an adaptive low-rank distance metric for representing the dissimilarity between different patients’ feature vectors. By minimizing this loss function, a linear low-dimensional transformation of input features was achieved. Also, imprecise features were excluded simultaneously by applying a l2,1-norm regularization of the learnt dissimilarity metric in the loss function. Finally, the learnt dissimilarity metric was applied in an evidential K-nearest-neighbor (EK- NN) classifier to predict treatment outcome. Results: Twenty-five patients with stage II–III non-small-cell lung cancer and thirty-six patients with esophageal squamous cell carcinomas treated with chemo-radiotherapy were collected. For the two groups of patients, 52 and 29 features, respectively, were utilized. The leave-one-out cross-validation (LOOCV) protocol was used for evaluation. Compared to three existing linear transformation methods (PCA, LDA, NCA), the proposed ELT-FS leads to higher prediction accuracy for the training and testing sets both for lung-cancer patients (100+/−0.0, 88.0+/−33.17) and for esophageal-cancer patients (97.46+/−1.64, 83.33+/−37.8). The ELT-FS also provides superior class separation in both test data sets. Conclusion: A novel DST- based approach has been proposed to predict cancer treatment outcome using PET

  4. The lucky image-motion prediction for simple scene observation based soft-sensor technology

    Science.gov (United States)

    Li, Yan; Su, Yun; Hu, Bin

    2015-08-01

    High resolution is important to earth remote sensors, while the vibration of the platforms of the remote sensors is a major factor restricting high resolution imaging. The image-motion prediction and real-time compensation are key technologies to solve this problem. For the reason that the traditional autocorrelation image algorithm cannot meet the demand for the simple scene image stabilization, this paper proposes to utilize soft-sensor technology in image-motion prediction, and focus on the research of algorithm optimization in imaging image-motion prediction. Simulations results indicate that the improving lucky image-motion stabilization algorithm combining the Back Propagation Network (BP NN) and support vector machine (SVM) is the most suitable for the simple scene image stabilization. The relative error of the image-motion prediction based the soft-sensor technology is below 5%, the training computing speed of the mathematical predication model is as fast as the real-time image stabilization in aerial photography.

  5. Accurate Prediction of Hyperfine Coupling Constants in Muoniated and Hydrogenated Ethyl Radicals: Ab Initio Path Integral Simulation Study with Density Functional Theory Method.

    Science.gov (United States)

    Yamada, Kenta; Kawashima, Yukio; Tachikawa, Masanori

    2014-05-13

    We performed ab initio path integral molecular dynamics (PIMD) simulations with a density functional theory (DFT) method to accurately predict hyperfine coupling constants (HFCCs) in the ethyl radical (CβH3-CαH2) and its Mu-substituted (muoniated) compound (CβH2Mu-CαH2). The substitution of a Mu atom, an ultralight isotope of the H atom, with larger nuclear quantum effect is expected to strongly affect the nature of the ethyl radical. The static conventional DFT calculations of CβH3-CαH2 find that the elongation of one Cβ-H bond causes a change in the shape of potential energy curve along the rotational angle via the imbalance of attractive and repulsive interactions between the methyl and methylene groups. Investigation of the methyl-group behavior including the nuclear quantum and thermal effects shows that an unbalanced CβH2Mu group with the elongated Cβ-Mu bond rotates around the Cβ-Cα bond in a muoniated ethyl radical, quite differently from the CβH3 group with the three equivalent Cβ-H bonds in the ethyl radical. These rotations couple with other molecular motions such as the methylene-group rocking motion (inversion), leading to difficulties in reproducing the corresponding barrier heights. Our PIMD simulations successfully predict the barrier heights to be close to the experimental values and provide a significant improvement in muon and proton HFCCs given by the static conventional DFT method. Further investigation reveals that the Cβ-Mu/H stretching motion, methyl-group rotation, methylene-group rocking motion, and HFCC values deeply intertwine with each other. Because these motions are different between the radicals, a proper description of the structural fluctuations reflecting the nuclear quantum and thermal effects is vital to evaluate HFCC values in theory to be comparable to the experimental ones. Accordingly, a fundamental difference in HFCC between the radicals arises from their intrinsic molecular motions at a finite temperature, in

  6. Forward-looking infrared imaging predicts ultimate burn depth in a porcine vertical injury progression model.

    Science.gov (United States)

    Miccio, Joseph; Parikh, Shruti; Marinaro, Xavier; Prasad, Atulya; McClain, Steven; Singer, Adam J; Clark, Richard A F

    2016-03-01

    Current methods of assessing burn depth are limited and are primarily based on visual assessments by burn surgeons. This technique has been shown to have only 60% accuracy and a more accurate, simple, noninvasive method is needed to determine burn wound depth. Forward-looking infrared (FLIR) thermography is both noninvasive and user-friendly with the potential to rapidly assess burn depth. The purpose of this paper is to determine if early changes in burn temperature (first 3 days) can be a predictor of burn depth as assessed by vertical scarring 28 days after injury. While under general anesthesia, 20 burns were created on the backs of two female Yorkshire swine using a 2.5cm×2.5cm×7.5cm, 150g aluminum bar, for a total of 40 burns. FLIR imaging was performed at both early (1, 2 and 3 days) and late (7, 10, 14, 17, 21, 24 and 28 days) time points. Burns were imaged from a height of 12 inches from the skin surface. FLIR ExaminIR(©) software was used to examine the infrared thermographs. One hundred temperature points from burn edge to edge across the center of the burn were collected for each burn at all time points and were exported as a comma-separated values (CSV) file. The CSV file was processed and analyzed using a MATLAB program. The temperature profiles through the center of the burns generated parabola-like curves. The lowest temperature (temperature minimum) and a line midway between the temperature minimum and ambient skin temperature at the burn edges was defined and the area of the curve calculated (the "temperature half-area"). Half-area values 2 days after burn had higher correlations with scar depth than did the minimum temperatures. However, burns that became warmer from 1 day to 2 days after injury had a lower scar depth then burns that became cooler and this trend was best predicted by temperature minima. When data were analyzed as a diagnostic test for sensitivity and specificity using >3mm scarring, i.e. a full-thickness burn, as a clinically

  7. Hierarchical prediction and context adaptive coding for lossless color image compression.

    Science.gov (United States)

    Kim, Seyun; Cho, Nam Ik

    2014-01-01

    This paper presents a new lossless color image compression algorithm, based on the hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression of an RGB image, it is first decorrelated by a reversible color transform and then Y component is encoded by a conventional lossless grayscale image compression method. For encoding the chrominance images, we develop a hierarchical scheme that enables the use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster scan prediction methods use upper and left pixels. An appropriate context model for the prediction error is also defined and the arithmetic coding is applied to the error signal corresponding to each context. For several sets of images, it is shown that the proposed method further reduces the bit rates compared with JPEG2000 and JPEG-XR.

  8. An improved technique for the prediction of optimal image resolution ...

    African Journals Online (AJOL)

    user

    2010-10-04

    Oct 4, 2010 ... Key words: Optimal resolution, savannah ecosystems, image noise index, land cover index, .... Most techniques, including those employed by Mugisha .... Resampling imagery using cubic convolution was used because it.

  9. Prediction of liver cirrhosis, using diagnostic imaging tools

    Institute of Scientific and Technical Information of China (English)

    2015-01-01

    Early diagnosis of liver cirrhosis is important. Ultrasoundguidedliver biopsy is the gold standard for diagnosis ofliver cirrhosis. However, its invasiveness and samplingbias limit the applicability of the method. Basic imagingfor the diagnosis of liver cirrhosis has developed overthe last few decades, enabling early detection ofmorphological changes of the liver by ultrasonography(US), computed tomography, and magnetic resonanceimaging (MRI). They are also accurate diagnosticmethods for advanced liver cirrhosis, for which earlydiagnosis is difficult. There are a number of ways tocompensate for this difficulty, including texture analysisto more closely identify the homogeneity of hepaticparenchyma, elastography to measure the stiffness andelasticity of the liver, and perfusion studies to determinethe blood flow volume, transit time, and velocity.Amongst these methods, elastography using US andMRI was found to be slightly easier, faster, and able toprovide an accurate diagnosis. Early diagnosis of livercirrhosis using MRI or US elastography is therefore arealistic alternative, but further research is still needed.

  10. What You See Is What You Get: Lead Location Within Deep Brain Structures Is Accurately Depicted by Stereotactic Magnetic Resonance Imaging.

    Science.gov (United States)

    Hyam, Jonathan A; Akram, Harith; Foltynie, Thomas; Limousin, Patricia; Hariz, Marwan; Zrinzo, Ludvic

    2015-09-01

    Magnetic resonance imaging (MRI)-verified deep brain stimulation relies on the correct interpretation of stereotactic imaging documenting lead location in relation to visible anatomic target. However, it has been suggested that local signal distortion from the lead itself renders its depiction on MRI unreliable. To compare lead location on stereotactic MRI with subsequent location of its brain track after removal. Patients underwent deep brain stimulation with the use of MRI-guided and MRI-verified Leksell frame approach. Infection or suboptimal efficacy required lead removal and subsequent reimplantation by using the same technique. Postimplantation stereotactic MR images were analyzed. Lateral (x) and anteroposterior (y) distances from midcommissural point to center of the lead hypointensity were recorded at the anterior commissure-posterior commissure plane (pallidal electrode) or z = -4 (subthalamic electrode). Stereotactic MRI before the second procedure, x and y distances from the center of the visible lead track hypointensity to midcommissural point were independently recorded. Vectorial distance from center of the lead hypointensity to the center of its track was calculated. Sixteen electrode tracks were studied in 10 patients. Mean differences between lead artifact location and lead track location were: x coordinate 0.4 mm ± 0.2; y coordinate 0.6 mm ± 0.3. Mean vectorial distance was 0.7 mm ± 0.2. Stereotactic distance between lead location and subsequent brain track location on MRI was small. The mean discrepancy was approximately half the deep brain stimulation lead width. This suggests that lead hypointensity seen on postimplantation MRI is indeed an accurate representation of its real location within deep brain structures.

  11. Comprehensive model for predicting perceptual image quality of smart mobile devices.

    Science.gov (United States)

    Gong, Rui; Xu, Haisong; Luo, M R; Li, Haifeng

    2015-01-01

    An image quality model for smart mobile devices was proposed based on visual assessments of several image quality attributes. A series of psychophysical experiments were carried out on two kinds of smart mobile devices, i.e., smart phones and tablet computers, in which naturalness, colorfulness, brightness, contrast, sharpness, clearness, and overall image quality were visually evaluated under three lighting environments via categorical judgment method for various application types of test images. On the basis of Pearson correlation coefficients and factor analysis, the overall image quality could first be predicted by its two constituent attributes with multiple linear regression functions for different types of images, respectively, and then the mathematical expressions were built to link the constituent image quality attributes with the physical parameters of smart mobile devices and image appearance factors. The procedure and algorithms were applicable to various smart mobile devices, different lighting conditions, and multiple types of images, and performance was verified by the visual data.

  12. 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.)

  13. VEGFR2-Targeted Three-Dimensional Ultrasound Imaging Can Predict Responses to Antiangiogenic Therapy in Preclinical Models of Colon Cancer.

    Science.gov (United States)

    Zhou, Jianhua; Wang, Huaijun; Zhang, Huiping; Lutz, Amelie M; Tian, Lu; Hristov, Dimitre; Willmann, Jürgen K

    2016-07-15

    Three-dimensional (3D) imaging capabilities to assess responses to anticancer therapies are needed to minimize sampling errors common to two-dimensional approaches as a result of spatial heterogeneity in tumors. Recently, the feasibility and reproducibility of 3D ultrasound molecular imaging (3D USMI) using contrast agents, which target molecular markers, have greatly improved, due to the development of clinical 3D matrix array transducers. Here we report preclinical proof-of-concept studies showing that 3D USMI of VEGFR2/KDR expression accurately gauges longitudinal treatment responses to antiangiogenesis therapy in responding versus nonresponding mouse models of colon cancer. Tumors in these models exhibited differential patterns of VEGFR2-targeted 3D USMI signals during the course of antiangiogenic treatment with bevacizumab. In responding tumors, the VEGFR2 signal decreased as soon as 24 hours after therapy was started, whereas in nonresponding tumors there was no change in signal at any time point. The early decrease in VEGFR2 signal was highly predictive of treatment outcome at the end of therapy. Our results offer preclinical proof that 3D USMI can predict responses to antiangiogenic therapy, warranting further investigation of its clinical translatability to predicting treatment outcomes in patients. Cancer Res; 76(14); 4081-9. ©2016 AACR.

  14. Accurate estimation of global and regional cardiac function by retrospectively gated multidetector row computed tomography: comparison with cine magnetic resonance imaging.

    Science.gov (United States)

    Belge, Bénédicte; Coche, Emmanuel; Pasquet, Agnès; Vanoverschelde, Jean-Louis J; Gerber, Bernhard L

    2006-07-01

    Retrospective reconstruction of ECG-gated images at different parts of the cardiac cycle allows the assessment of cardiac function by multi-detector row CT (MDCT) at the time of non-invasive coronary imaging. We compared the accuracy of such measurements by MDCT to cine magnetic resonance (MR). Forty patients underwent the assessment of global and regional cardiac function by 16-slice MDCT and cine MR. Left ventricular (LV) end-diastolic and end-systolic volumes estimated by MDCT (134+/-51 and 67+/-56 ml) were similar to those by MR (137+/-57 and 70+/-60 ml, respectively; both P=NS) and strongly correlated (r=0.92 and r=0.95, respectively; both P<0.001). Consequently, LV ejection fractions by MDCT and MR were also similar (55+/-21 vs. 56+/-21%; P=NS) and highly correlated (r=0.95; P<0.001). Regional end-diastolic and end-systolic wall thicknesses by MDCT were highly correlated (r=0.84 and r=0.92, respectively; both P<0.001), but significantly lower than by MR (8.3+/-1.8 vs. 8.8+/-1.9 mm and 12.7+/-3.4 vs. 13.3+/-3.5 mm, respectively; both P<0.001). Values of regional wall thickening by MDCT and MR were similar (54+/-30 vs. 51+/-31%; P=NS) and also correlated well (r=0.91; P<0.001). Retrospectively gated MDCT can accurately estimate LV volumes, EF and regional LV wall thickening compared to cine MR.

  15. Accurate estimation of global and regional cardiac function by retrospectively gated multidetector row computed tomography. Comparison with cine magnetic resonance imaging

    Energy Technology Data Exchange (ETDEWEB)

    Belge, Benedicte; Pasquet, Agnes; Vanoverschelde, Jean-Louis J. [Universite Catholique de Louvain, Division of Cardiology, Brussels (Belgium); Coche, Emmanuel [Universite Catholique de Louvain, Division of Radiology, Brussels (Belgium); Gerber, Bernhard L. [Universite Catholique de Louvain, Division of Cardiology, Brussels (Belgium); Cliniques Universitaires St. Luc UCL, Department of Cardiology, Woluwe St. Lambert (Belgium)

    2006-07-15

    Retrospective reconstruction of ECG-gated images at different parts of the cardiac cycle allows the assessment of cardiac function by multi-detector row CT (MDCT) at the time of non-invasive coronary imaging. We compared the accuracy of such measurements by MDCT to cine magnetic resonance (MR). Forty patients underwent the assessment of global and regional cardiac function by 16-slice MDCT and cine MR. Left ventricular (LV) end-diastolic and end-systolic volumes estimated by MDCT (134{+-}51 and 67{+-}56 ml) were similar to those by MR (137{+-}57 and 70{+-}60 ml, respectively; both P=NS) and strongly correlated (r=0.92 and r=0.95, respectively; both P<0.001). Consequently, LV ejection fractions by MDCT and MR were also similar (55{+-}21 vs. 56{+-}21%; P=NS) and highly correlated (r=0.95; P<0.001). Regional end-diastolic and end-systolic wall thicknesses by MDCT were highly correlated (r=0.84 and r=0.92, respectively; both P<0.001), but significantly lower than by MR (8.3{+-}1.8 vs. 8.8{+-}1.9 mm and 12.7{+-}3.4 vs. 13.3{+-}3.5 mm, respectively; both P<0.001). Values of regional wall thickening by MDCT and MR were similar (54{+-}30 vs. 51{+-}31%; P=NS) and also correlated well (r=0.91; P<0.001). Retrospectively gated MDCT can accurately estimate LV volumes, EF and regional LV wall thickening compared to cine MR. (orig.)

  16. Magnetic resonance imaging is comparable to computed tomography for determination of glenoid version but does not accurately distinguish between Walch B2 and C classifications.

    Science.gov (United States)

    Lowe, Jeremiah T; Testa, Edward J; Li, Xinning; Miller, Suzanne; DeAngelis, Joseph P; Jawa, Andrew

    2017-04-01

    Computed tomography (CT) scan is the standard for the preoperative assessment of glenoid version and morphology before total shoulder arthroplasty. However, the capacity of magnetic resonance imaging (MRI) to visualize bone morphology has improved with advancing technology. The purpose of this study was to compare the accuracy of MRI to CT for assessment of glenoid version and Walch classification. Three fellowship-trained shoulder surgeons assessed glenoid version and Walch classification of 30 patients with primary shoulder osteoarthritis who received both CT and MRI scans before total shoulder arthroplasty. Version measurements, Walch classification, and observer agreement were compared. Mean glenoid version was -15.5° and -18.6° by CT and MRI, respectively (P = .17). Interobserver reliability coefficients were good for both imaging modalities (CT, 0.73; MRI, 0.62). Intraobserver coefficients were good to excellent for CT (range, 0.76-0.87) and good for MRI (range, 0.75-0.79). For Walch classification, interobserver reliability for both modalities was merely fair, whereas intraobserver reliability was moderate to good. Although identification of type A1, A2, and B1 was nearly identical between CT and MRI, there was observer disagreement on type B2 (P = .001) and C glenoids (P = .03). Specifically, MRI underidentified type B2 and overidentified type C compared with CT. MRI is largely comparable to CT scan for evaluation of the glenoid, with similar measurements of version and identification of less extreme Walch glenoids. However, MRI is less accurate at distinguishing between type B2 and C glenoids. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

  17. Hyperspectral imaging based on diffused laser light for prediction of astaxanthin coating concentration

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Nielsen, Otto Højager Attermann; Frosch, Stina

    2014-01-01

    We present a study on predicting the concentration level of synthetic astaxanthin in fish feed pellet coating using multi- and hyperspectral image analysis. This was done in parallel using two different vision systems. A new instrument for hyperspectral imaging, the SuperK setup, using a super...

  18. Bit-plane-channelized hotelling observer for predicting task performance using lossy-compressed images

    Science.gov (United States)

    Schmanske, Brian M.; Loew, Murray H.

    2003-05-01

    A technique for assessing the impact of lossy wavelet-based image compression on signal detection tasks is presented. A medical image"s value is based on its ability to support clinical decisions such as detecting and diagnosing abnormalities. Image quality of compressed images is, however, often stated in terms of mathematical metrics such as mean square error. The presented technique provides a more suitable measure of image degradation by building on the channelized Hotelling observer model, which has been shown to predict human performance of signal detection tasks in noise-limited images. The technique first decomposes an image into its constituent wavelet subband coefficient bit-planes. Channel responses for the individual subband bit-planes are computed, combined,and processed with a Hotelling observer model to provide a measure of signal detectability versus compression ratio. This allows a user to determine how much compression can be tolerated before signal detectability drops below a certain threshold.

  19. Prediction of yield by digital image analysis of vine

    Directory of Open Access Journals (Sweden)

    Bešlić Zoran S.

    2014-01-01

    Full Text Available The grape yield per vine of cv. Cabernet Sauvignon (Vitis vinifera L. was evaluated on the basis of digital image processing of vine part. Digital camera was mounted on tripod and used for taking photos of 1 x 1 m portions of canopy. The Adobe Photoshop software was used to analyse image for the colour counting of the blue pixels of grape in the quadrant region. The actual yield was obtained from the photographed vines by hand harvesting of sampled portions. Linear regression was used for calculation of the correlation between blue pixels and grape weight. The relatively strong relationship between blue pixels and grape weight (R2=0.91 was obtained. Based on these results, we can recommend this simple technique for yield forecasting. [Projekat Ministarstva nauke Republike Srbije, br. TP31063

  20. Diffusion-weighted magnetic resonance imaging to predict response of hepatocellular carcinoma to chemoembolization

    Institute of Scientific and Technical Information of China (English)

    Johnathan; C; Chung; Neel; K; Naik; Robert; J; Lewandowski; Mary; F; Mulcahy; Laura; M; Kulik; Kent; T; Sato; Robert; K; Ryu; Riad; Salem; Andrew; C; Larson; Reed; A; Omary

    2010-01-01

    AIM: To investigate whether intra-procedural diffusion- weighted magnetic resonance imaging can predict response of hepatocellular carcinoma (HCC) during trans- catheter arterial chemoembolization (TACE). METHODS: Sixteen patients (15 male), aged 59 ±11 years (range: 42-81 years) underwent a total of 21 separate treatments for unresectable HCC in a hybrid magnetic resonance/interventional radiology suite. Ana- tomical imaging and diffusion-weighted imaging (b = 0, 500 s/mm2) were performed on a 1.5-T unit. ...

  1. Photodissociation of the BrO radical using velocity map ion imaging: excited state dynamics and accurate D0(0)(BrO) evaluation.

    Science.gov (United States)

    Kim, Hahkjoon; Dooley, Kristin S; Johnson, Elizabeth R; North, Simon W

    2006-04-07

    We have studied the photodissociation dynamics of expansion-cooled BrO radical both above (278-281.5 nm) and below (355 nm) the A (2)Pi(3/2) state threshold using velocity map ion imaging. A recently developed late-mixing flash pyrolytic reactor source was utilized to generate an intense BrO radical molecular beam. The relative electronic product branching ratios at 355 nm and from 278 to 281.5 nm were determined. We have investigated the excited state dynamics based on both the product branching and the photofragment angular distributions. We find that above the O((1)D(2)) threshold the contribution of the direct excitation to states other than the A (2)Pi(3/2) state and the role of curve crossing is considerably larger in BrO compared to that observed for ClO, in agreement with recent theoretical studies. The measurement of low velocity photofragments resulting from photodissociation just above the O((1)D(2)) threshold provides an accurate and direct determination of the A (2)Pi(3/2) state dissociation threshold of 35418+/-35 cm(-1), leading to a ground state bond energy of D(0)(0)(BrO)=55.9+/-0.1 kcal/mol.

  2. Predicting Performance of a Face Recognition System Based on Image Quality

    NARCIS (Netherlands)

    Dutta, A.

    2015-01-01

    In this dissertation, we focus on several aspects of models that aim to predict performance of a face recognition system. Performance prediction models are commonly based on the following two types of performance predictor features: a) image quality features; and b) features derived solely from

  3. Three attempts of earthquake prediction with satellite cloud images

    Directory of Open Access Journals (Sweden)

    G. Guangmeng

    2013-01-01

    Full Text Available Thermal anomalies detected from satellite data are widely reported. Nearly all the anomalies are reported after the quake. Here we report three earthquake predictions in Italy and Iran according to satellite cloud anomalies. These cloud anomalies usually show a linear pattern, stay there for hours and do not move with winds. According to these anomalies, we can give a rough estimation about impending earthquake activities. All the estimated dates and magnitudes are in good agreement with the earthquake facts, and the only unsatisfactory point is that the distance error is 100–300 km. Because the cloud anomaly is long, we can not reduce the distance error further. A possible way is to combine geophysical data and satellite data together to estimate the epicenter and this will increase the prediction accuracy.

  4. Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

    DEFF Research Database (Denmark)

    Bøvith, Thomas; Nielsen, Allan Aasbjerg; Hansen, Lars Kai

    2006-01-01

    A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect...... information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale...

  5. Prediction suppression in monkey inferotemporal cortex depends on the conditional probability between images.

    Science.gov (United States)

    Ramachandran, Suchitra; Meyer, Travis; Olson, Carl R

    2016-01-01

    When monkeys view two images in fixed sequence repeatedly over days and weeks, neurons in area TE of the inferotemporal cortex come to exhibit prediction suppression. The trailing image elicits only a weak response when presented following the leading image that preceded it during training. Induction of prediction suppression might depend either on the contiguity of the images, as determined by their co-occurrence and captured in the measure of joint probability P(A,B), or on their contingency, as determined by their correlation and as captured in the measures of conditional probability P(A|B) and P(B|A). To distinguish between these possibilities, we measured prediction suppression after imposing training regimens that held P(A,B) constant but varied P(A|B) and P(B|A). We found that reducing either P(A|B) or P(B|A) during training attenuated prediction suppression as measured during subsequent testing. We conclude that prediction suppression depends on contingency, as embodied in the predictive relations between the images, and not just on contiguity, as embodied in their co-occurrence.

  6. Formation of NO from N2/O2 mixtures in a flow reactor: Toward an accurate prediction of thermal NO

    DEFF Research Database (Denmark)

    Abian, Maria; Alzueta, Maria U.; Glarborg, Peter

    2015-01-01

    We have conducted flow reactor experiments for NO formation from N2/O2 mixtures at high temperatures and atmospheric pressure, controlling accurately temperature and reaction time. Under these conditions, atomic oxygen equilibrates rapidly with O2. The experimental results were interpreted by a d...

  7. Complete Focal Plane Compression Based on CMOS Image Sensor Using Predictive Coding

    Institute of Scientific and Technical Information of China (English)

    Yao Suying; Yu Xiao; Gao Jing; Xu Jiangtao

    2015-01-01

    In this paper, a CMOS image sensor(CIS) is proposed, which can accomplish both decorrelation and en-tropy coding of image compression directly on the focal plane. The design is based on predictive coding for image decorrelation. The predictions are performed in analog domain by 2×2 pixel units. Both the prediction residuals and original pixel values are quantized and encoded in parallel. Since the residuals have a peak distribution around zero, the output codewords can be replaced by the valid part of the residuals’ binary mode. The compressed bit stream is accessible directly at the output of CIS without extra disposition. Simulation results show that the proposed approach achieves a compression rate of 2. 2 and PSNR of 51 on different test images.

  8. Application of extended Kalman filtering on aircraft pose prediction of image sequences

    Institute of Scientific and Technical Information of China (English)

    YANG Li-mei; GUO Li-hong

    2007-01-01

    In allusion to the character of monocular image sequences,a method based on extended Kalman filtering to predict the aircraft pose of image sequences is proposed. With α-β-γstable state filtering technique,a mathematics model is built to realize the prediction of aircraft pose of image sequences. In the model,not only the influence of noise during the image process is considered,but also the shortcoming of low precision in the constant velocity model is overcomed. The derivation of acceleration is considered as white noise. The predictive curve plotted with Matlab proves that the maximum of error of using this method is about 3. So its precision is higher and error standard deviation is lower than those of the constant velocity model.

  9. Prediction Method for Image Coding Quality Based on Differential Information Entropy

    Directory of Open Access Journals (Sweden)

    Xin Tian

    2014-02-01

    Full Text Available For the requirement of quality-based image coding, an approach to predict the quality of image coding based on differential information entropy is proposed. First of all, some typical prediction approaches are introduced, and then the differential information entropy is reviewed. Taking JPEG2000 as an example, the relationship between differential information entropy and the objective assessment indicator PSNR at a fixed compression ratio is established via data fitting, and the constraint for fitting is to minimize the average error. Next, the relationship among differential information entropy, compression ratio and PSNR at various compression ratios is constructed and this relationship is used as an indicator to predict the image coding quality. Finally, the proposed approach is compared with some traditional approaches. From the experiments, it can be seen that the differential information entropy has a better linear relationship with image coding quality than that with the image activity. Therefore, the conclusion can be reached that the proposed approach is capable of predicting image coding quality at low compression ratios with small errors, and can be widely applied in a variety of real-time space image coding systems for its simplicity.

  10. PET imaging predicts future body weight and cocaine preference

    Energy Technology Data Exchange (ETDEWEB)

    Michaelides M.; Wang G.; Michaelides M.; Thanos P.K. Kim R.; Cho J.; Ananth M.; Wang G.-J.; Volkow N.D.

    2011-08-28

    Deficits in dopamine D2/D3 receptor (D2R/D3R) binding availability using PET imaging have been reported in obese humans and rodents. Similar deficits have been reported in cocaine-addicts and cocaine-exposed primates. We found that D2R/D3R binding availability negatively correlated with measures of body weight at the time of scan (ventral striatum), at 1 (ventral striatum) and 2 months (dorsal and ventral striatum) post scan in rats. Cocaine preference was negatively correlated with D2R/D3R binding availability 2 months (ventral striatum) post scan. Our findings suggest that inherent deficits in striatal D2R/D3R signaling are related to obesity and drug addiction susceptibility and that ventral and dorsal striatum serve dissociable roles in maintaining weight gain and cocaine preference. Measuring D2R/D3R binding availability provides a way for assessing susceptibility to weight gain and cocaine abuse in rodents and given the translational nature of PET imaging, potentially primates and humans.

  11. Editorial Commentary: Single-Image Slice Magnetic Resonance Imaging Assessments Do Not Predict 3-Dimensional Muscle Volume.

    Science.gov (United States)

    Brand, Jefferson C

    2016-01-01

    No single-image magnetic resonance imaging (MRI) assessment-Goutallier classification, Fuchs classification, or cross-sectional area-is predictive of whole-muscle volume or fatty atrophy of the supraspinatus or infraspinatus. Rather, 3-dimensional MRI measurement of whole-muscle volume and fat-free muscle volume is required and is associated with shoulder strength, which is clinically relevant. Three-dimensional MRI may represent a new gold standard for assessment of the rotator cuff musculature using imaging and may help to predict the feasibility of repair of a rotator cuff tear as well as the postoperative outcome. Unfortunately, 3-dimensional MRI assessment of muscle volume is labor intensive and is not widely available for clinical use.

  12. Cross-sectional imaging for diagnosis and clinical outcome prediction of acute basilar artery thrombosis

    Energy Technology Data Exchange (ETDEWEB)

    Mortimer, A.M., E-mail: alex_mortimer@hotmail.co [Severn School of Radiology, Bristol (United Kingdom); Department of Radiology, Great Western Hospital, Swindon (United Kingdom); Saunders, T.; Cook, J.-L. [Department of Radiology, Great Western Hospital, Swindon (United Kingdom)

    2011-06-15

    Basilar artery occlusion is a potentially fatal condition and imaging findings can be subtle. Prompt diagnosis is vital, as recognition may lead to therapeutic recanalization that may improve functional outcome and survival. Furthermore, cross-sectional imaging signs may help predict eventual outcome and, therefore, guide which patients should be subjected to aggressive treatment. Computed tomography (CT) signs include a hyperdense basilar artery that has a high specificity, accuracy, positive and negative predictive value. Evidence regarding the prognostic significance of the hyperdense basilar artery sign is conflicting. Early magnetic resonance imaging (MRI) features include loss of flow void, seen as increased signal intensity within the basilar artery on T2-weigted images and identification of acute thrombus, seen as intermediate signal on T1-weighted images. MRI sequences are more sensitive for early detection of acute ischaemia or infarction, ideally with diffusion-weighted imaging (DWI). Both CT and MR angiography are sensitive for detection of acute thrombus, seen as a filling defect or occlusion. These are the non-invasive imaging techniques of choice to confirm diagnosis, with perhaps the speed and accessibility of CT angiography resulting in this technique being valuable in the acute setting. Several new scoring systems based on arterial segmentation rather than global volume assessment using CT angiography source images and DWI have shown early promise in the prediction of eventual clinical outcome in order to isolate those patients who may benefit from therapeutic recanalization.

  13. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.

    Science.gov (United States)

    Lundegaard, Claus; Lamberth, Kasper; Harndahl, Mikkel; Buus, Søren; Lund, Ole; Nielsen, Morten

    2008-07-01

    NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8-11 for all 122 alleles. artificial neural network predictions are given as actual IC(50) values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75-80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.

  14. Accurate shadow detection in liver CT images%肝CT图像中阴影部分的精确检测

    Institute of Scientific and Technical Information of China (English)

    张晓峰; 李景辉; 马燕

    2009-01-01

    The shadows in liver are likely to be cancer tissue, so the liver cancers can be detected by detecting the shadows first. The shadows and normal liver tissue are hard to divide for the densities of them are close to each other. An algorithm is proposed to calculate the signed distance function (SDF) in Chan- Vese model. This algorithm makes the initial curve close to the division edge at the beginning, largely decreases the repeating times and improves the speed of target detection of Chan-Vese model. Finally, this algorithm is applied to the accurate detection of shadows in liver CT images and divides the shadows from normal liver tissue successfully.%肝部阴影是肝癌的重要疑似对象,因此检测肝癌可以先将肝部阴影检测出来.但肝部阴影与正常肝组织密度接近,很难分割.为此,提出一种CV分割方法的初始距离函数的设置方法,该方法使初始曲线一开始就接近图像的分割边缘,极大地减少了CV方法的迭代次数,加快了CV方法目标提取的速度.最后将该方法应用于肝部阴影的精确检测,成功地分割了图像中的肝部组织与其中的阴影部分.

  15. Predict brain MR image registration via sparse learning of appearance and transformation.

    Science.gov (United States)

    Wang, Qian; Kim, Minjeong; Shi, Yonghong; Wu, Guorong; Shen, Dinggang

    2015-02-01

    We propose a new approach to register the subject image with the template by leveraging a set of intermediate images that are pre-aligned to the template. We argue that, if points in the subject and the intermediate images share similar local appearances, they may have common correspondence in the template. In this way, we learn the sparse representation of a certain subject point to reveal several similar candidate points in the intermediate images. Each selected intermediate candidate can bridge the correspondence from the subject point to the template space, thus predicting the transformation associated with the subject point at the confidence level that relates to the learned sparse coefficient. Following this strategy, we first predict transformations at selected key points, and retain multiple predictions on each key point, instead of allowing only a single correspondence. Then, by utilizing all key points and their predictions with varying confidences, we adaptively reconstruct the dense transformation field that warps the subject to the template. We further embed the prediction-reconstruction protocol above into a multi-resolution hierarchy. In the final, we refine our estimated transformation field via existing registration method in effective manners. We apply our method to registering brain MR images, and conclude that the proposed framework is competent to improve registration performances substantially. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.

    Science.gov (United States)

    Nicolasjilwan, Manal; Hu, Ying; Yan, Chunhua; Meerzaman, Daoud; Holder, Chad A; Gutman, David; Jain, Rajan; Colen, Rivka; Rubin, Daniel L; Zinn, Pascal O; Hwang, Scott N; Raghavan, Prashant; Hammoud, Dima A; Scarpace, Lisa M; Mikkelsen, Tom; Chen, James; Gevaert, Olivier; Buetow, Kenneth; Freymann, John; Kirby, Justin; Flanders, Adam E; Wintermark, Max

    2015-07-01

    The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679±0.068, Akaike's information criterion 566.7, P<0.001). A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  17. Addition of MR Imaging Features and Genetic Biomarkers Strengthen Glioblastoma Survival Prediction in TCGA Patients

    Science.gov (United States)

    Nicolasjilwan, Manal; Hu, Ying; Yan, Chunhua; Meerzaman, Daoud; Holder, Chad A.; Gutman, David; Jain, Rajan; Colen, Rivka; Rubin, Daniel L.; Zinn, Pascal O.; Hwang, Scott N.; Raghavan, Prashant; Hammoud, Dima A; Scarpace, Lisa M; Mikkelsen, Tom; Chen, James; Gevaert, Olivier; Buetow, Kenneth; Freymann, John; Kirby, Justin; Flanders, Adam E.; Wintermark, Max

    2017-01-01

    PURPOSE The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS The study was conducted leveraging the Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients’ clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS The features that were significantly associated with survival were: 1) clinical factors: chemotherapy; 2) imaging: proportion of tumor contrast enhancement on MRI, and 3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679 ± 0.068, Akaike’s information criterion 566.7, p < 0.001). CONCLUSION A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM. PMID:24997477

  18. The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction

    DEFF Research Database (Denmark)

    Sabuncu, Mert R.; Van Leemput, Koen

    2011-01-01

    to utilize a small number of spatially clustered sets of voxels that are particularly suited for clinical interpretation. RVoxM automatically tunes all its free parameters during the training phase, and offers the additional advantage of producing probabilistic prediction outcomes. Experiments on age......This paper presents the Relevance VoxelMachine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed...... prediction from structural brain MRI indicate that RVoxM yields biologically meaningful models that provide excellent predictive accuracy....

  19. Is scoring system of computed tomography based metric parameters can accurately predicts shock wave lithotripsy stone-free rates and aid in the development of treatment strategies?

    Directory of Open Access Journals (Sweden)

    Yasser ALI Badran

    2016-01-01

    Conclusion: Stone size, stone density (HU, and SSD is simple to calculate and can be reported by radiologists to applying combined score help to augment predictive power of SWL, reduce cost, and improving of treatment strategies.

  20. TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

    Energy Technology Data Exchange (ETDEWEB)

    Yip, S; Coroller, T; Niu, N; Mamon, H; Aerts, H; Berbeco, R [Brigham and Women’s Hospital, Boston, MA (United States)

    2015-06-15

    Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.0