Sample records for accurate quantitative predictions

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

    PubMed Central

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

    2017-01-01

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

  2. Toward Accurate and Quantitative Comparative Metagenomics

    PubMed Central

    Nayfach, Stephen; Pollard, Katherine S.

    2016-01-01

    Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized. PMID:27565341

  3. Quantitative self-assembly prediction yields targeted nanomedicines

    NASA Astrophysics Data System (ADS)

    Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

    2018-02-01

    Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

  4. Toward Accurate and Quantitative Comparative Metagenomics.

    PubMed

    Nayfach, Stephen; Pollard, Katherine S

    2016-08-25

    Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Quantitative prediction of drug side effects based on drug-related features.

    PubMed

    Niu, Yanqing; Zhang, Wen

    2017-09-01

    Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

  6. Quantitative prediction of phase transformations in silicon during nanoindentation

    NASA Astrophysics Data System (ADS)

    Zhang, Liangchi; Basak, Animesh

    2013-08-01

    This paper establishes the first quantitative relationship between the phases transformed in silicon and the shape characteristics of nanoindentation curves. Based on an integrated analysis using TEM and unit cell properties of phases, the volumes of the phases emerged in a nanoindentation are formulated as a function of pop-out size and depth of nanoindentation impression. This simple formula enables a fast, accurate and quantitative prediction of the phases in a nanoindentation cycle, which has been impossible before.

  7. Mental models accurately predict emotion transitions.

    PubMed

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  8. Mental models accurately predict emotion transitions

    PubMed Central

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  9. Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

    NASA Astrophysics Data System (ADS)

    Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

    2017-03-01

    Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

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

    PubMed Central

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

    2013-01-01

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

  11. Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

    PubMed

    Darrington, Richard T; Jiao, Jim

    2004-04-01

    Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

  12. Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior

    PubMed Central

    Marsh, Abigail A.; Kozak, Megan N.; Ambady, Nalini

    2009-01-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants’ ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale. PMID:17516803

  13. Accurate identification of fear facial expressions predicts prosocial behavior.

    PubMed

    Marsh, Abigail A; Kozak, Megan N; Ambady, Nalini

    2007-05-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

  14. Accurate Binding Free Energy Predictions in Fragment Optimization.

    PubMed

    Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

    2015-11-23

    Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

  15. Quantitative computed tomography for the prediction of pulmonary function after lung cancer surgery: a simple method using simulation software.

    PubMed

    Ueda, Kazuhiro; Tanaka, Toshiki; Li, Tao-Sheng; Tanaka, Nobuyuki; Hamano, Kimikazu

    2009-03-01

    The prediction of pulmonary functional reserve is mandatory in therapeutic decision-making for patients with resectable lung cancer, especially those with underlying lung disease. Volumetric analysis in combination with densitometric analysis of the affected lung lobe or segment with quantitative computed tomography (CT) helps to identify residual pulmonary function, although the utility of this modality needs investigation. The subjects of this prospective study were 30 patients with resectable lung cancer. A three-dimensional CT lung model was created with voxels representing normal lung attenuation (-600 to -910 Hounsfield units). Residual pulmonary function was predicted by drawing a boundary line between the lung to be preserved and that to be resected, directly on the lung model. The predicted values were correlated with the postoperative measured values. The predicted and measured values corresponded well (r=0.89, p<0.001). Although the predicted values corresponded with values predicted by simple calculation using a segment-counting method (r=0.98), there were two outliers whose pulmonary functional reserves were predicted more accurately by CT than by segment counting. The measured pulmonary functional reserves were significantly higher than the predicted values in patients with extensive emphysematous areas (<-910 Hounsfield units), but not in patients with chronic obstructive pulmonary disease. Quantitative CT yielded accurate prediction of functional reserve after lung cancer surgery and helped to identify patients whose functional reserves are likely to be underestimated. Hence, this modality should be utilized for patients with marginal pulmonary function.

  16. Accurate Construction of Photoactivated Localization Microscopy (PALM) Images for Quantitative Measurements

    PubMed Central

    Coltharp, Carla; Kessler, Rene P.; Xiao, Jie

    2012-01-01

    Localization-based superresolution microscopy techniques such as Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) have allowed investigations of cellular structures with unprecedented optical resolutions. One major obstacle to interpreting superresolution images, however, is the overcounting of molecule numbers caused by fluorophore photoblinking. Using both experimental and simulated images, we determined the effects of photoblinking on the accurate reconstruction of superresolution images and on quantitative measurements of structural dimension and molecule density made from those images. We found that structural dimension and relative density measurements can be made reliably from images that contain photoblinking-related overcounting, but accurate absolute density measurements, and consequently faithful representations of molecule counts and positions in cellular structures, require the application of a clustering algorithm to group localizations that originate from the same molecule. We analyzed how applying a simple algorithm with different clustering thresholds (tThresh and dThresh) affects the accuracy of reconstructed images, and developed an easy method to select optimal thresholds. We also identified an empirical criterion to evaluate whether an imaging condition is appropriate for accurate superresolution image reconstruction with the clustering algorithm. Both the threshold selection method and imaging condition criterion are easy to implement within existing PALM clustering algorithms and experimental conditions. The main advantage of our method is that it generates a superresolution image and molecule position list that faithfully represents molecule counts and positions within a cellular structure, rather than only summarizing structural properties into ensemble parameters. This feature makes it particularly useful for cellular structures of heterogeneous densities and irregular geometries, and

  17. Accurate Quantitative Sensing of Intracellular pH based on Self-ratiometric Upconversion Luminescent Nanoprobe.

    PubMed

    Li, Cuixia; Zuo, Jing; Zhang, Li; Chang, Yulei; Zhang, Youlin; Tu, Langping; Liu, Xiaomin; Xue, Bin; Li, Qiqing; Zhao, Huiying; Zhang, Hong; Kong, Xianggui

    2016-12-09

    Accurate quantitation of intracellular pH (pH i ) is of great importance in revealing the cellular activities and early warning of diseases. A series of fluorescence-based nano-bioprobes composed of different nanoparticles or/and dye pairs have already been developed for pH i sensing. Till now, biological auto-fluorescence background upon UV-Vis excitation and severe photo-bleaching of dyes are the two main factors impeding the accurate quantitative detection of pH i . Herein, we have developed a self-ratiometric luminescence nanoprobe based on förster resonant energy transfer (FRET) for probing pH i , in which pH-sensitive fluorescein isothiocyanate (FITC) and upconversion nanoparticles (UCNPs) were served as energy acceptor and donor, respectively. Under 980 nm excitation, upconversion emission bands at 475 nm and 645 nm of NaYF 4 :Yb 3+ , Tm 3+ UCNPs were used as pH i response and self-ratiometric reference signal, respectively. This direct quantitative sensing approach has circumvented the traditional software-based subsequent processing of images which may lead to relatively large uncertainty of the results. Due to efficient FRET and fluorescence background free, a highly-sensitive and accurate sensing has been achieved, featured by 3.56 per unit change in pH i value 3.0-7.0 with deviation less than 0.43. This approach shall facilitate the researches in pH i related areas and development of the intracellular drug delivery systems.

  18. Accurate Quantitative Sensing of Intracellular pH based on Self-ratiometric Upconversion Luminescent Nanoprobe

    NASA Astrophysics Data System (ADS)

    Li, Cuixia; Zuo, Jing; Zhang, Li; Chang, Yulei; Zhang, Youlin; Tu, Langping; Liu, Xiaomin; Xue, Bin; Li, Qiqing; Zhao, Huiying; Zhang, Hong; Kong, Xianggui

    2016-12-01

    Accurate quantitation of intracellular pH (pHi) is of great importance in revealing the cellular activities and early warning of diseases. A series of fluorescence-based nano-bioprobes composed of different nanoparticles or/and dye pairs have already been developed for pHi sensing. Till now, biological auto-fluorescence background upon UV-Vis excitation and severe photo-bleaching of dyes are the two main factors impeding the accurate quantitative detection of pHi. Herein, we have developed a self-ratiometric luminescence nanoprobe based on förster resonant energy transfer (FRET) for probing pHi, in which pH-sensitive fluorescein isothiocyanate (FITC) and upconversion nanoparticles (UCNPs) were served as energy acceptor and donor, respectively. Under 980 nm excitation, upconversion emission bands at 475 nm and 645 nm of NaYF4:Yb3+, Tm3+ UCNPs were used as pHi response and self-ratiometric reference signal, respectively. This direct quantitative sensing approach has circumvented the traditional software-based subsequent processing of images which may lead to relatively large uncertainty of the results. Due to efficient FRET and fluorescence background free, a highly-sensitive and accurate sensing has been achieved, featured by 3.56 per unit change in pHi value 3.0-7.0 with deviation less than 0.43. This approach shall facilitate the researches in pHi related areas and development of the intracellular drug delivery systems.

  19. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  20. ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

    PubMed

    Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

    2016-11-01

    ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P < 0.0001). 394 (61.2%) of physicians' estimates about the percentage probability of post-thrombolysis symptomatic intracranial haemorrhage were accurate compared with 583 (90.5%) of SEDAN score estimates (P < 0.0001). 160 (24.8%) of physicians' estimates about post-thrombolysis 3-month percentage probability of mRS 0-2 were accurate compared with 240 (37.3%) DRAGON score estimates (P < 0.0001). 260 (40.4%) of physicians' estimates about the percentage probability of post-thrombolysis mRS 5-6 were accurate compared with 518 (80.4%) DRAGON score estimates (P < 0.0001). ASTRAL, DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

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

    PubMed Central

    Noecker, Cecilia; Schaefer, Krista; Zaccheo, Kelly; Yang, Yiding; Day, Judy; Ganusov, Vitaly V.

    2015-01-01

    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 dose. These results

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

    PubMed Central

    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

  3. Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

    DOE PAGES

    Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.; ...

    2017-03-06

    Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

  4. Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

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

    Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.

    Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

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

    PubMed Central

    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

  6. Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach.

    PubMed

    Efthymiou, Evdokia; Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa; Imbach, Lukas L

    2017-10-01

    The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (p<0.005) and correlated with independently identified visual EEG patterns such as generalized periodic discharges (p<0.02). Receiver operating characteristic (ROC) analysis confirmed the predictive value of lower state space velocity for poor clinical outcome after cardiac arrest (AUC 80.8, 70% sensitivity, 15% false positive rate). Model-based quantitative EEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic

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

    PubMed

    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. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

    NASA Astrophysics Data System (ADS)

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

    2018-02-01

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

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

    PubMed

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

    2018-04-01

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

  10. Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy.

    PubMed

    Doblas, Ana; Sánchez-Ortiga, Emilio; Martínez-Corral, Manuel; Saavedra, Genaro; Garcia-Sucerquia, Jorge

    2014-04-01

    The advantages of using a telecentric imaging system in digital holographic microscopy (DHM) to study biological specimens are highlighted. To this end, the performances of nontelecentric DHM and telecentric DHM are evaluated from the quantitative phase imaging (QPI) point of view. The evaluated stability of the microscope allows single-shot QPI in DHM by using telecentric imaging systems. Quantitative phase maps of a section of the head of the drosophila melanogaster fly and of red blood cells are obtained via single-shot DHM with no numerical postprocessing. With these maps we show that the use of telecentric DHM provides larger field of view for a given magnification and permits more accurate QPI measurements with less number of computational operations.

  11. Quantitative prediction of solute strengthening in aluminium alloys.

    PubMed

    Leyson, Gerard Paul M; Curtin, William A; Hector, Louis G; Woodward, Christopher F

    2010-09-01

    Despite significant advances in computational materials science, a quantitative, parameter-free prediction of the mechanical properties of alloys has been difficult to achieve from first principles. Here, we present a new analytic theory that, with input from first-principles calculations, is able to predict the strengthening of aluminium by substitutional solute atoms. Solute-dislocation interaction energies in and around the dislocation core are first calculated using density functional theory and a flexible-boundary-condition method. An analytic model for the strength, or stress to move a dislocation, owing to the random field of solutes, is then presented. The theory, which has no adjustable parameters and is extendable to other metallic alloys, predicts both the energy barriers to dislocation motion and the zero-temperature flow stress, allowing for predictions of finite-temperature flow stresses. Quantitative comparisons with experimental flow stresses at temperature T=78 K are made for Al-X alloys (X=Mg, Si, Cu, Cr) and good agreement is obtained.

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

    PubMed

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-05-01

    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. 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. 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 attributed to the design of the features, which are

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

    PubMed Central

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-01-01

    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 attributed to the design of

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

    PubMed Central

    Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

    2015-01-01

    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. PMID:26424887

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

    PubMed

    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. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

  16. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

    PubMed Central

    Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

    2014-01-01

    Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

  17. Universality and predictability in molecular quantitative genetics.

    PubMed

    Nourmohammad, Armita; Held, Torsten; Lässig, Michael

    2013-12-01

    Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology. Copyright © 2013. Published by Elsevier Ltd.

  18. A novel logic-based approach for quantitative toxicology prediction.

    PubMed

    Amini, Ata; Muggleton, Stephen H; Lodhi, Huma; Sternberg, Michael J E

    2007-01-01

    There is a pressing need for accurate in silico methods to predict the toxicity of molecules that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicology is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as molecular superposition, faced by some other SAR methods. The ILP approach reasons with chemical substructures within a relational framework and yields chemically understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qualitative ILP-based SAR to quantitative modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 molecules with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R2CV) from a chemical descriptor method (CHEM) and SVILP are 0.52 and 0.66, respectively. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, respectively, of toxic molecules. In a set of 165 unseen molecules, the R2 values from the commercial software TOPKAT and SVILP are 0.26 and 0.57, respectively. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.

  19. Combining quantitative trait loci analysis with physiological models to predict genotype-specific transpiration rates.

    PubMed

    Reuning, Gretchen A; Bauerle, William L; Mullen, Jack L; McKay, John K

    2015-04-01

    Transpiration is controlled by evaporative demand and stomatal conductance (gs ), and there can be substantial genetic variation in gs . A key parameter in empirical models of transpiration is minimum stomatal conductance (g0 ), a trait that can be measured and has a large effect on gs and transpiration. In Arabidopsis thaliana, g0 exhibits both environmental and genetic variation, and quantitative trait loci (QTL) have been mapped. We used this information to create a genetically parameterized empirical model to predict transpiration of genotypes. For the parental lines, this worked well. However, in a recombinant inbred population, the predictions proved less accurate. When based only upon their genotype at a single g0 QTL, genotypes were less distinct than our model predicted. Follow-up experiments indicated that both genotype by environment interaction and a polygenic inheritance complicate the application of genetic effects into physiological models. The use of ecophysiological or 'crop' models for predicting transpiration of novel genetic lines will benefit from incorporating further knowledge of the genetic control and degree of independence of core traits/parameters underlying gs variation. © 2014 John Wiley & Sons Ltd.

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

    PubMed

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

    2015-09-01

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

  1. Accurate virus quantitation using a Scanning Transmission Electron Microscopy (STEM) detector in a scanning electron microscope.

    PubMed

    Blancett, Candace D; Fetterer, David P; Koistinen, Keith A; Morazzani, Elaine M; Monninger, Mitchell K; Piper, Ashley E; Kuehl, Kathleen A; Kearney, Brian J; Norris, Sarah L; Rossi, Cynthia A; Glass, Pamela J; Sun, Mei G

    2017-10-01

    A method for accurate quantitation of virus particles has long been sought, but a perfect method still eludes the scientific community. Electron Microscopy (EM) quantitation is a valuable technique because it provides direct morphology information and counts of all viral particles, whether or not they are infectious. In the past, EM negative stain quantitation methods have been cited as inaccurate, non-reproducible, and with detection limits that were too high to be useful. To improve accuracy and reproducibility, we have developed a method termed Scanning Transmission Electron Microscopy - Virus Quantitation (STEM-VQ), which simplifies sample preparation and uses a high throughput STEM detector in a Scanning Electron Microscope (SEM) coupled with commercially available software. In this paper, we demonstrate STEM-VQ with an alphavirus stock preparation to present the method's accuracy and reproducibility, including a comparison of STEM-VQ to viral plaque assay and the ViroCyt Virus Counter. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  2. Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

    PubMed Central

    Burger, Lukas; van Nimwegen, Erik

    2008-01-01

    Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

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

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

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

    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, andmore » 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.« less

  4. Quantitating Organoleptic Volatile Phenols in Smoke-Exposed Vitis vinifera Berries.

    PubMed

    Noestheden, Matthew; Thiessen, Katelyn; Dennis, Eric G; Tiet, Ben; Zandberg, Wesley F

    2017-09-27

    Accurate methods for quantitating volatile phenols (i.e., guaiacol, syringol, 4-ethylphenol, etc.) in smoke-exposed Vitis vinifera berries prior to fermentation are needed to predict the likelihood of perceptible smoke taint following vinification. Reported here is a complete, cross-validated analytical workflow to accurately quantitate free and glycosidically bound volatile phenols in smoke-exposed berries using liquid-liquid extraction, acid-mediated hydrolysis, and gas chromatography-tandem mass spectrometry. The reported workflow addresses critical gaps in existing methods for volatile phenols that impact quantitative accuracy, most notably the effect of injection port temperature and the variability in acid-mediated hydrolytic procedures currently used. Addressing these deficiencies will help the wine industry make accurate, informed decisions when producing wines from smoke-exposed berries.

  5. Accurate prediction of energy expenditure using a shoe-based activity monitor.

    PubMed

    Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

    2011-07-01

    The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

  6. Accurate quantitation standards of glutathione via traceable sulfur measurement by inductively coupled plasma optical emission spectrometry and ion chromatography

    PubMed Central

    Rastogi, L.; Dash, K.; Arunachalam, J.

    2013-01-01

    The quantitative analysis of glutathione (GSH) is important in different fields like medicine, biology, and biotechnology. Accurate quantitative measurements of this analyte have been hampered by the lack of well characterized reference standards. The proposed procedure is intended to provide an accurate and definitive method for the quantitation of GSH for reference measurements. Measurement of the stoichiometrically existing sulfur content in purified GSH offers an approach for its quantitation and calibration through an appropriate characterized reference material (CRM) for sulfur would provide a methodology for the certification of GSH quantity, that is traceable to SI (International system of units). The inductively coupled plasma optical emission spectrometry (ICP-OES) approach negates the need for any sample digestion. The sulfur content of the purified GSH is quantitatively converted into sulfate ions by microwave-assisted UV digestion in the presence of hydrogen peroxide prior to ion chromatography (IC) measurements. The measurement of sulfur by ICP-OES and IC (as sulfate) using the “high performance” methodology could be useful for characterizing primary calibration standards and certified reference materials with low uncertainties. The relative expanded uncertainties (% U) expressed at 95% confidence interval for ICP-OES analyses varied from 0.1% to 0.3%, while in the case of IC, they were between 0.2% and 1.2%. The described methods are more suitable for characterizing primary calibration standards and certifying reference materials of GSH, than for routine measurements. PMID:29403814

  7. Accurate Virus Quantitation Using a Scanning Transmission Electron Microscopy (STEM) Detector in a Scanning Electron Microscope

    DTIC Science & Technology

    2017-06-29

    Accurate Virus Quantitation Using a Scanning Transmission Electron Microscopy (STEM) Detector in a Scanning Electron Microscope Candace D Blancett1...L Norris2, Cynthia A Rossi4 , Pamela J Glass3, Mei G Sun1,* 1 Pathology Division, United States Army Medical Research Institute of Infectious...Diseases (USAMRIID), 1425 Porter Street, Fort Detrick, Maryland, 21702 2Biostatistics Division, United States Army Medical Research Institute of

  8. Are EMS call volume predictions based on demand pattern analysis accurate?

    PubMed

    Brown, Lawrence H; Lerner, E Brooke; Larmon, Baxter; LeGassick, Todd; Taigman, Michael

    2007-01-01

    Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over

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

    PubMed

    Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

    2012-01-01

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  11. A correlative imaging based methodology for accurate quantitative assessment of bone formation in additive manufactured implants.

    PubMed

    Geng, Hua; Todd, Naomi M; Devlin-Mullin, Aine; Poologasundarampillai, Gowsihan; Kim, Taek Bo; Madi, Kamel; Cartmell, Sarah; Mitchell, Christopher A; Jones, Julian R; Lee, Peter D

    2016-06-01

    A correlative imaging methodology was developed to accurately quantify bone formation in the complex lattice structure of additive manufactured implants. Micro computed tomography (μCT) and histomorphometry were combined, integrating the best features from both, while demonstrating the limitations of each imaging modality. This semi-automatic methodology registered each modality using a coarse graining technique to speed the registration of 2D histology sections to high resolution 3D μCT datasets. Once registered, histomorphometric qualitative and quantitative bone descriptors were directly correlated to 3D quantitative bone descriptors, such as bone ingrowth and bone contact. The correlative imaging allowed the significant volumetric shrinkage of histology sections to be quantified for the first time (~15 %). This technique demonstrated the importance of location of the histological section, demonstrating that up to a 30 % offset can be introduced. The results were used to quantitatively demonstrate the effectiveness of 3D printed titanium lattice implants.

  12. Highly Accurate Quantitative Analysis Of Enantiomeric Mixtures from Spatially Frequency Encoded 1H NMR Spectra.

    PubMed

    Plainchont, Bertrand; Pitoux, Daisy; Cyrille, Mathieu; Giraud, Nicolas

    2018-02-06

    We propose an original concept to measure accurately enantiomeric excesses on proton NMR spectra, which combines high-resolution techniques based on a spatial encoding of the sample, with the use of optically active weakly orienting solvents. We show that it is possible to simulate accurately dipolar edited spectra of enantiomers dissolved in a chiral liquid crystalline phase, and to use these simulations to calibrate integrations that can be measured on experimental data, in order to perform a quantitative chiral analysis. This approach is demonstrated on a chemical intermediate for which optical purity is an essential criterion. We find that there is a very good correlation between the experimental and calculated integration ratios extracted from G-SERF spectra, which paves the way to a general method of determination of enantiomeric excesses based on the observation of 1 H nuclei.

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

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

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

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

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

    DOE PAGES

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

    2016-12-28

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

  15. Measuring the value of accurate link prediction for network seeding.

    PubMed

    Wei, Yijin; Spencer, Gwen

    2017-01-01

    The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

  16. Quantitative thickness prediction of tectonically deformed coal using Extreme Learning Machine and Principal Component Analysis: a case study

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Li, Yan; Chen, Tongjun; Yan, Qiuyan; Ma, Li

    2017-04-01

    The thickness of tectonically deformed coal (TDC) has positive correlation associations with gas outbursts. In order to predict the TDC thickness of coal beds, we propose a new quantitative predicting method using an extreme learning machine (ELM) algorithm, a principal component analysis (PCA) algorithm, and seismic attributes. At first, we build an ELM prediction model using the PCA attributes of a synthetic seismic section. The results suggest that the ELM model can produce a reliable and accurate prediction of the TDC thickness for synthetic data, preferring Sigmoid activation function and 20 hidden nodes. Then, we analyze the applicability of the ELM model on the thickness prediction of the TDC with real application data. Through the cross validation of near-well traces, the results suggest that the ELM model can produce a reliable and accurate prediction of the TDC. After that, we use 250 near-well traces from 10 wells to build an ELM predicting model and use the model to forecast the TDC thickness of the No. 15 coal in the study area using the PCA attributes as the inputs. Comparing the predicted results, it is noted that the trained ELM model with two selected PCA attributes yields better predication results than those from the other combinations of the attributes. Finally, the trained ELM model with real seismic data have a different number of hidden nodes (10) than the trained ELM model with synthetic seismic data. In summary, it is feasible to use an ELM model to predict the TDC thickness using the calculated PCA attributes as the inputs. However, the input attributes, the activation function and the number of hidden nodes in the ELM model should be selected and tested carefully based on individual application.

  17. Accurate prediction of collapse temperature using optical coherence tomography-based freeze-drying microscopy.

    PubMed

    Greco, Kristyn; Mujat, Mircea; Galbally-Kinney, Kristin L; Hammer, Daniel X; Ferguson, R Daniel; Iftimia, Nicusor; Mulhall, Phillip; Sharma, Puneet; Kessler, William J; Pikal, Michael J

    2013-06-01

    The objective of this study was to assess the feasibility of developing and applying a laboratory tool that can provide three-dimensional product structural information during freeze-drying and which can accurately characterize the collapse temperature (Tc ) of pharmaceutical formulations designed for freeze-drying. A single-vial freeze dryer coupled with optical coherence tomography freeze-drying microscopy (OCT-FDM) was developed to investigate the structure and Tc of formulations in pharmaceutically relevant products containers (i.e., freeze-drying in vials). OCT-FDM was used to measure the Tc and eutectic melt of three formulations in freeze-drying vials. The Tc as measured by OCT-FDM was found to be predictive of freeze-drying with a batch of vials in a conventional laboratory freeze dryer. The freeze-drying cycles developed using OCT-FDM data, as compared with traditional light transmission freeze-drying microscopy (LT-FDM), resulted in a significant reduction in primary drying time, which could result in a substantial reduction of manufacturing costs while maintaining product quality. OCT-FDM provides quantitative data to justify freeze-drying at temperatures higher than the Tc measured by LT-FDM and provides a reliable upper limit to setting a product temperature in primary drying. Copyright © 2013 Wiley Periodicals, Inc.

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

    PubMed

    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.

  19. Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

    PubMed

    Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

    2017-05-01

    physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

  20. Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

    PubMed Central

    Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

    2017-01-01

    Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

  1. Novel Application of Quantitative Single-Photon Emission Computed Tomography/Computed Tomography to Predict Early Response to Methimazole in Graves' Disease

    PubMed Central

    Kim, Hyun Joo; Bang, Ji-In; Kim, Ji-Young; Moon, Jae Hoon; So, Young

    2017-01-01

    Objective Since Graves' disease (GD) is resistant to antithyroid drugs (ATDs), an accurate quantitative thyroid function measurement is required for the prediction of early responses to ATD. Quantitative parameters derived from the novel technology, single-photon emission computed tomography/computed tomography (SPECT/CT), were investigated for the prediction of achievement of euthyroidism after methimazole (MMI) treatment in GD. Materials and Methods A total of 36 GD patients (10 males, 26 females; mean age, 45.3 ± 13.8 years) were enrolled for this study, from April 2015 to January 2016. They underwent quantitative thyroid SPECT/CT 20 minutes post-injection of 99mTc-pertechnetate (5 mCi). Association between the time to biochemical euthyroidism after MMI treatment and %uptake, standardized uptake value (SUV), functional thyroid mass (SUVmean × thyroid volume) from the SPECT/CT, and clinical/biochemical variables, were investigated. Results GD patients had a significantly greater %uptake (6.9 ± 6.4%) than historical control euthyroid patients (n = 20, 0.8 ± 0.5%, p < 0.001) from the same quantitative SPECT/CT protocol. Euthyroidism was achieved in 14 patients at 156 ± 62 days post-MMI treatment, but 22 patients had still not achieved euthyroidism by the last follow-up time-point (208 ± 80 days). In the univariate Cox regression analysis, the initial MMI dose (p = 0.014), %uptake (p = 0.015), and functional thyroid mass (p = 0.016) were significant predictors of euthyroidism in response to MMI treatment. However, only %uptake remained significant in a multivariate Cox regression analysis (p = 0.034). A %uptake cutoff of 5.0% dichotomized the faster responding versus the slower responding GD patients (p = 0.006). Conclusion A novel parameter of thyroid %uptake from quantitative SPECT/CT is a predictive indicator of an early response to MMI in GD patients. PMID:28458607

  2. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  3. An accurate model for predicting high frequency noise of nanoscale NMOS SOI transistors

    NASA Astrophysics Data System (ADS)

    Shen, Yanfei; Cui, Jie; Mohammadi, Saeed

    2017-05-01

    A nonlinear and scalable model suitable for predicting high frequency noise of N-type Metal Oxide Semiconductor (NMOS) transistors is presented. The model is developed for a commercial 45 nm CMOS SOI technology and its accuracy is validated through comparison with measured performance of a microwave low noise amplifier. The model employs the virtual source nonlinear core and adds parasitic elements to accurately simulate the RF behavior of multi-finger NMOS transistors up to 40 GHz. For the first time, the traditional long-channel thermal noise model is supplemented with an injection noise model to accurately represent the noise behavior of these short-channel transistors up to 26 GHz. The developed model is simple and easy to extract, yet very accurate.

  4. Semi-quantitative prediction of a multiple API solid dosage form with a combination of vibrational spectroscopy methods.

    PubMed

    Hertrampf, A; Sousa, R M; Menezes, J C; Herdling, T

    2016-05-30

    Quality control (QC) in the pharmaceutical industry is a key activity in ensuring medicines have the required quality, safety and efficacy for their intended use. QC departments at pharmaceutical companies are responsible for all release testing of final products but also all incoming raw materials. Near-infrared spectroscopy (NIRS) and Raman spectroscopy are important techniques for fast and accurate identification and qualification of pharmaceutical samples. Tablets containing two different active pharmaceutical ingredients (API) [bisoprolol, hydrochlorothiazide] in different commercially available dosages were analysed using Raman- and NIR Spectroscopy. The goal was to define multivariate models based on each vibrational spectroscopy to discriminate between different dosages (identity) and predict their dosage (semi-quantitative). Furthermore the combination of spectroscopic techniques was investigated. Therefore, two different multiblock techniques based on PLS have been applied: multiblock PLS (MB-PLS) and sequential-orthogonalised PLS (SO-PLS). NIRS showed better results compared to Raman spectroscopy for both identification and quantitation. The multiblock techniques investigated showed that each spectroscopy contains information not present or captured with the other spectroscopic technique, thus demonstrating that there is a potential benefit in their combined use for both identification and quantitation purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

    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.

  6. Competitive Abilities in Experimental Microcosms Are Accurately Predicted by a Demographic Index for R*

    PubMed Central

    Murrell, Ebony G.; Juliano, Steven A.

    2012-01-01

    Resource competition theory predicts that R*, the equilibrium resource amount yielding zero growth of a consumer population, should predict species' competitive abilities for that resource. This concept has been supported for unicellular organisms, but has not been well-tested for metazoans, probably due to the difficulty of raising experimental populations to equilibrium and measuring population growth rates for species with long or complex life cycles. We developed an index (Rindex) of R* based on demography of one insect cohort, growing from egg to adult in a non-equilibrium setting, and tested whether Rindex yielded accurate predictions of competitive abilities using mosquitoes as a model system. We estimated finite rate of increase (λ′) from demographic data for cohorts of three mosquito species raised with different detritus amounts, and estimated each species' Rindex using nonlinear regressions of λ′ vs. initial detritus amount. All three species' Rindex differed significantly, and accurately predicted competitive hierarchy of the species determined in simultaneous pairwise competition experiments. Our Rindex could provide estimates and rigorous statistical comparisons of competitive ability for organisms for which typical chemostat methods and equilibrium population conditions are impractical. PMID:22970128

  7. Generation of accurate peptide retention data for targeted and data independent quantitative LC-MS analysis: Chromatographic lessons in proteomics.

    PubMed

    Krokhin, Oleg V; Spicer, Vic

    2016-12-01

    The emergence of data-independent quantitative LC-MS/MS analysis protocols further highlights the importance of high-quality reproducible chromatographic procedures. Knowing, controlling and being able to predict the effect of multiple factors that alter peptide RP-HPLC separation selectivity is critical for successful data collection for the construction of ion libraries. Proteomic researchers have often regarded RP-HPLC as a "black box", while vast amount of research on peptide separation is readily available. In addition to obvious parameters, such as the type of ion-pairing modifier, stationary phase and column temperature, we describe the "mysterious" effects of gradient slope, column size and flow rate on peptide separation selectivity. Retention time variations due to these parameters are governed by the linear solvent strength (LSS) theory on a peptide level by the value of its slope S in the basic LSS equation-a parameter that can be accurately predicted. Thus, the application of shallower gradients, higher flow rates, or smaller columns will each increases the relative retention of peptides with higher S-values (long species with multiple positively charged groups). Simultaneous changes to these parameters that each drive shifts in separation selectivity in the same direction should be avoided. The unification of terminology represents another pressing issue in this field of applied proteomics that should be addressed to facilitate further progress. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. SIFTER search: a web server for accurate phylogeny-based protein function prediction

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

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  9. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    DOE PAGES

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

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

    PubMed

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

    2013-12-23

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

  11. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

    PubMed

    Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

    2015-11-01

    A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

  12. Restriction Site Tiling Analysis: accurate discovery and quantitative genotyping of genome-wide polymorphisms using nucleotide arrays

    PubMed Central

    2010-01-01

    High-throughput genotype data can be used to identify genes important for local adaptation in wild populations, phenotypes in lab stocks, or disease-related traits in human medicine. Here we advance microarray-based genotyping for population genomics with Restriction Site Tiling Analysis. The approach simultaneously discovers polymorphisms and provides quantitative genotype data at 10,000s of loci. It is highly accurate and free from ascertainment bias. We apply the approach to uncover genomic differentiation in the purple sea urchin. PMID:20403197

  13. Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

    PubMed

    Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

    2018-02-01

    The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

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

    PubMed

    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. A unique charge-coupled device/xenon arc lamp based imaging system for the accurate detection and quantitation of multicolour fluorescence.

    PubMed

    Spibey, C A; Jackson, P; Herick, K

    2001-03-01

    In recent years the use of fluorescent dyes in biological applications has dramatically increased. The continual improvement in the capabilities of these fluorescent dyes demands increasingly sensitive detection systems that provide accurate quantitation over a wide linear dynamic range. In the field of proteomics, the detection, quantitation and identification of very low abundance proteins are of extreme importance in understanding cellular processes. Therefore, the instrumentation used to acquire an image of such samples, for spot picking and identification by mass spectrometry, must be sensitive enough to be able, not only, to maximise the sensitivity and dynamic range of the staining dyes but, as importantly, adapt to the ever changing portfolio of fluorescent dyes as they become available. Just as the available fluorescent probes are improving and evolving so are the users application requirements. Therefore, the instrumentation chosen must be flexible to address and adapt to those changing needs. As a result, a highly competitive market for the supply and production of such dyes and the instrumentation for their detection and quantitation have emerged. The instrumentation currently available is based on either laser/photomultiplier tube (PMT) scanning or lamp/charge-coupled device (CCD) based mechanisms. This review briefly discusses the advantages and disadvantages of both System types for fluorescence imaging, gives a technical overview of CCD technology and describes in detail a unique xenon/are lamp CCD based instrument, from PerkinElmer Life Sciences. The Wallac-1442 ARTHUR is unique in its ability to scan both large areas at high resolution and give accurate selectable excitation over the whole of the UV/visible range. It operates by filtering both the excitation and emission wavelengths, providing optimal and accurate measurement and quantitation of virtually any available dye and allows excellent spectral resolution between different fluorophores

  16. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting

    PubMed Central

    Khan, Tarik A.; Friedensohn, Simon; de Vries, Arthur R. Gorter; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T.

    2016-01-01

    High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion—the intraclonal diversity index—which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology. PMID:26998518

  17. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting.

    PubMed

    Khan, Tarik A; Friedensohn, Simon; Gorter de Vries, Arthur R; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T

    2016-03-01

    High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion-the intraclonal diversity index-which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology.

  18. Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation

    NASA Astrophysics Data System (ADS)

    Zargari, Abolfazl; Du, Yue; Thai, Theresa C.; Gunderson, Camille C.; Moore, Kathleen; Mannel, Robert S.; Liu, Hong; Zheng, Bin; Qiu, Yuchen

    2018-02-01

    The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.

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

    NASA Astrophysics Data System (ADS)

    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.

  20. Towards First Principles-Based Prediction of Highly Accurate Electrochemical Pourbaix Diagrams

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

    Zeng, Zhenhua; Chan, Maria K. Y.; Zhao, Zhi-Jian

    2015-08-13

    Electrochemical potential/pH (Pourbaix) diagrams underpin many 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 diagrams, inherent errors in the description of transition metal (hydroxy)oxides, together with neglect of van der Waals interactions, have limited the reliability of such predictions for even the simplest pure metal bulk compounds, and corresponding predictions for more complex alloy or surface structures are even more challenging. In the present work, through synergistic use of a Hubbard U correction,more » a state-of-the-art dispersion correction, and a water-based bulk reference state for the calculations, these errors are systematically corrected. The approach describes the weak binding that occurs between hydroxyl-containing functional groups in certain compounds in Pourbaix diagrams, corrects for self-interaction errors in transition metal compounds, and reduces residual errors on oxygen atoms by preserving a consistent oxidation state between the reference state, water, and the relevant bulk phases. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxides, oxyhydroxides, binary, and ternary oxides, where the corresponding thermodynamics of redox and (de)hydration are described with standard errors of 0.04 eV per (reaction) formula unit. The approach further preserves accurate descriptions of the overall thermodynamics of electrochemically-relevant bulk reactions, such as water formation, which is an essential condition for facilitating accurate analysis of reaction energies for electrochemical processes on surfaces. The overall generality and transferability of the scheme suggests that it may find useful application in the construction of a broad array of electrochemical phase diagrams, including

  1. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

    PubMed

    Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

    2016-06-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  2. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals

    PubMed Central

    Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

    2016-01-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting 1) the overall intensity of their future negative emotion, and 2) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. PMID:27100309

  3. Extending Theory-Based Quantitative Predictions to New Health Behaviors.

    PubMed

    Brick, Leslie Ann D; Velicer, Wayne F; Redding, Colleen A; Rossi, Joseph S; Prochaska, James O

    2016-04-01

    Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing. A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory. This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel. Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions. Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

  4. Shedding light on the variability of optical skin properties: finding a path towards more accurate prediction of light propagation in human cutaneous compartments

    PubMed Central

    Mignon, C.; Tobin, D. J.; Zeitouny, M.; Uzunbajakava, N. E.

    2018-01-01

    Finding a path towards a more accurate prediction of light propagation in human skin remains an aspiration of biomedical scientists working on cutaneous applications both for diagnostic and therapeutic reasons. The objective of this study was to investigate variability of the optical properties of human skin compartments reported in literature, to explore the underlying rational of this variability and to propose a dataset of values, to better represent an in vivo case and recommend a solution towards a more accurate prediction of light propagation through cutaneous compartments. To achieve this, we undertook a novel, logical yet simple approach. We first reviewed scientific articles published between 1981 and 2013 that reported on skin optical properties, to reveal the spread in the reported quantitative values. We found variations of up to 100-fold. Then we extracted the most trust-worthy datasets guided by a rule that the spectral properties should reflect the specific biochemical composition of each of the skin layers. This resulted in the narrowing of the spread in the calculated photon densities to 6-fold. We conclude with a recommendation to use the identified most robust datasets when estimating light propagation in human skin using Monte Carlo simulations. Alternatively, otherwise follow our proposed strategy to screen any new datasets to determine their biological relevance. PMID:29552418

  5. High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Toth, G.; Gombosi, T. I.

    2014-12-01

    BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

  6. Accurate high-throughput structure mapping and prediction with transition metal ion FRET

    PubMed Central

    Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

    2013-01-01

    Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

  7. Accurate quantitation of D+ fetomaternal hemorrhage by flow cytometry using a novel reagent to eliminate granulocytes from analysis.

    PubMed

    Kumpel, Belinda; Hazell, Matthew; Guest, Alan; Dixey, Jonathan; Mushens, Rosey; Bishop, Debbie; Wreford-Bush, Tim; Lee, Edmond

    2014-05-01

    Quantitation of fetomaternal hemorrhage (FMH) is performed to determine the dose of prophylactic anti-D (RhIG) required to prevent D immunization of D- women. Flow cytometry (FC) is the most accurate method. However, maternal white blood cells (WBCs) can give high background by binding anti-D nonspecifically, compromising accuracy. Maternal blood samples (69) were sent for FC quantitation of FMH after positive Kleihauer-Betke test (KBT) analysis and RhIG administration. Reagents used were BRAD-3-fluorescein isothiocyanate (FITC; anti-D), AEVZ5.3-FITC (anti-varicella zoster [anti-VZ], negative control), anti-fetal hemoglobin (HbF)-FITC, blended two-color reagents, BRAD-3-FITC/anti-CD45-phycoerythrin (PE; anti-D/L), and BRAD-3-FITC/anti-CD66b-PE (anti-D/G). PE-positive WBCs were eliminated from analysis by gating. Full blood counts were performed on maternal samples and female donors. Elevated numbers of neutrophils were present in 80% of patients. Red blood cell (RBC) indices varied widely in maternal blood. D+ FMH values obtained with anti-D/L, anti-D/G, and anti-HbF-FITC were very similar (r = 0.99, p < 0.001). Correlation between KBT and anti-HbF-FITC FMH results was low (r = 0.716). Inaccurate FMH quantitation using the current method (anti-D minus anti-VZ) occurred with 71% samples having less than 15 mL of D+ FMH (RBCs) and insufficient RhIG calculated for 9%. Using two-color reagents and anti-HbF-FITC, approximately 30% patients had elevated F cells, 26% had no fetal cells, 6% had D- FMH, 26% had 4 to 15 mL of D+ FMH, and 12% patients had more than 15 mL of D+ FMH (RBCs) requiring more than 300 μg of RhIG. Without accurate quantitation of D+ FMH by FC, some women would receive inappropriate or inadequate anti-D prophylaxis. The latter may be at risk of immunization leading to hemolytic disease of the newborn. © 2013 American Association of Blood Banks.

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

    PubMed

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-06-01

    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 binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1

  9. Quantitative prediction of oral cancer risk in patients with oral leukoplakia.

    PubMed

    Liu, Yao; Li, Yicheng; Fu, Yue; Liu, Tong; Liu, Xiaoyong; Zhang, Xinyan; Fu, Jie; Guan, Xiaobing; Chen, Tong; Chen, Xiaoxin; Sun, Zheng

    2017-07-11

    Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma. We have developed an oral cancer risk index using DNA index value to quantitatively assess cancer risk in patients with oral leukoplakia, but with limited success. In order to improve the performance of the risk index, we collected exfoliative cytology, histopathology, and clinical follow-up data from two independent cohorts of normal, leukoplakia and cancer subjects (training set and validation set). Peaks were defined on the basis of first derivatives with positives, and modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Random forest was found to be the best model with high sensitivity (100%) and specificity (99.2%). Using the Peaks-Random Forest model, we constructed an index (OCRI2) as a quantitative measurement of cancer risk. Among 11 leukoplakia patients with an OCRI2 over 0.5, 4 (36.4%) developed cancer during follow-up (23 ± 20 months), whereas 3 (5.3%) of 57 leukoplakia patients with an OCRI2 less than 0.5 developed cancer (32 ± 31 months). OCRI2 is better than other methods in predicting oral squamous cell carcinoma during follow-up. In conclusion, we have developed an exfoliative cytology-based method for quantitative prediction of cancer risk in patients with oral leukoplakia.

  10. Accurate and quantitative polarization-sensitive OCT by unbiased birefringence estimator with noise-stochastic correction

    NASA Astrophysics Data System (ADS)

    Kasaragod, Deepa; Sugiyama, Satoshi; Ikuno, Yasushi; Alonso-Caneiro, David; Yamanari, Masahiro; Fukuda, Shinichi; Oshika, Tetsuro; Hong, Young-Joo; Li, En; Makita, Shuichi; Miura, Masahiro; Yasuno, Yoshiaki

    2016-03-01

    Polarization sensitive optical coherence tomography (PS-OCT) is a functional extension of OCT that contrasts the polarization properties of tissues. It has been applied to ophthalmology, cardiology, etc. Proper quantitative imaging is required for a widespread clinical utility. However, the conventional method of averaging to improve the signal to noise ratio (SNR) and the contrast of the phase retardation (or birefringence) images introduce a noise bias offset from the true value. This bias reduces the effectiveness of birefringence contrast for a quantitative study. Although coherent averaging of Jones matrix tomography has been widely utilized and has improved the image quality, the fundamental limitation of nonlinear dependency of phase retardation and birefringence to the SNR was not overcome. So the birefringence obtained by PS-OCT was still not accurate for a quantitative imaging. The nonlinear effect of SNR to phase retardation and birefringence measurement was previously formulated in detail for a Jones matrix OCT (JM-OCT) [1]. Based on this, we had developed a maximum a-posteriori (MAP) estimator and quantitative birefringence imaging was demonstrated [2]. However, this first version of estimator had a theoretical shortcoming. It did not take into account the stochastic nature of SNR of OCT signal. In this paper, we present an improved version of the MAP estimator which takes into account the stochastic property of SNR. This estimator uses a probability distribution function (PDF) of true local retardation, which is proportional to birefringence, under a specific set of measurements of the birefringence and SNR. The PDF was pre-computed by a Monte-Carlo (MC) simulation based on the mathematical model of JM-OCT before the measurement. A comparison between this new MAP estimator, our previous MAP estimator [2], and the standard mean estimator is presented. The comparisons are performed both by numerical simulation and in vivo measurements of anterior and

  11. Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.

    PubMed

    Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

    2007-01-01

    Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method

  12. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

    PubMed

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2016-10-01

    The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future. © 2016 John Wiley & Sons Ltd.

  13. How accurate is our clinical prediction of "minimal prostate cancer"?

    PubMed

    Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

    2013-07-01

    Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

  14. Investigating the Validity of Two Widely Used Quantitative Text Tools

    ERIC Educational Resources Information Center

    Cunningham, James W.; Hiebert, Elfrieda H.; Mesmer, Heidi Anne

    2018-01-01

    In recent years, readability formulas have gained new prominence as a basis for selecting texts for learning and assessment. Variables that quantitative tools count (e.g., word frequency, sentence length) provide valid measures of text complexity insofar as they accurately predict representative and high-quality criteria. The longstanding…

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

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

    2015-06-04

    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 demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

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

    PubMed Central

    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

  17. PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

  18. Impact of implementation choices on quantitative predictions of cell-based computational models

    NASA Astrophysics Data System (ADS)

    Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

    2017-09-01

    'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

  19. Predicting Children's Reading and Mathematics Achievement from Early Quantitative Knowledge and Domain-General Cognitive Abilities

    PubMed Central

    Chu, Felicia W.; vanMarle, Kristy; Geary, David C.

    2016-01-01

    One hundred children (44 boys) participated in a 3-year longitudinal study of the development of basic quantitative competencies and the relation between these competencies and later mathematics and reading achievement. The children's preliteracy knowledge, intelligence, executive functions, and parental educational background were also assessed. The quantitative tasks assessed a broad range of symbolic and nonsymbolic knowledge and were administered four times across 2 years of preschool. Mathematics achievement was assessed at the end of each of 2 years of preschool, and mathematics and word reading achievement were assessed at the end of kindergarten. Our goals were to determine how domain-general abilities contribute to growth in children's quantitative knowledge and to determine how domain-general and domain-specific abilities contribute to children's preschool mathematics achievement and kindergarten mathematics and reading achievement. We first identified four core quantitative competencies (e.g., knowledge of the cardinal value of number words) that predict later mathematics achievement. The domain-general abilities were then used to predict growth in these competencies across 2 years of preschool, and the combination of domain-general abilities, preliteracy skills, and core quantitative competencies were used to predict mathematics achievement across preschool and mathematics and word reading achievement at the end of kindergarten. Both intelligence and executive functions predicted growth in the four quantitative competencies, especially across the first year of preschool. A combination of domain-general and domain-specific competencies predicted preschoolers' mathematics achievement, with a trend for domain-specific skills to be more strongly related to achievement at the beginning of preschool than at the end of preschool. Preschool preliteracy skills, sensitivity to the relative quantities of collections of objects, and cardinal knowledge predicted

  20. Predicting Children's Reading and Mathematics Achievement from Early Quantitative Knowledge and Domain-General Cognitive Abilities.

    PubMed

    Chu, Felicia W; vanMarle, Kristy; Geary, David C

    2016-01-01

    One hundred children (44 boys) participated in a 3-year longitudinal study of the development of basic quantitative competencies and the relation between these competencies and later mathematics and reading achievement. The children's preliteracy knowledge, intelligence, executive functions, and parental educational background were also assessed. The quantitative tasks assessed a broad range of symbolic and nonsymbolic knowledge and were administered four times across 2 years of preschool. Mathematics achievement was assessed at the end of each of 2 years of preschool, and mathematics and word reading achievement were assessed at the end of kindergarten. Our goals were to determine how domain-general abilities contribute to growth in children's quantitative knowledge and to determine how domain-general and domain-specific abilities contribute to children's preschool mathematics achievement and kindergarten mathematics and reading achievement. We first identified four core quantitative competencies (e.g., knowledge of the cardinal value of number words) that predict later mathematics achievement. The domain-general abilities were then used to predict growth in these competencies across 2 years of preschool, and the combination of domain-general abilities, preliteracy skills, and core quantitative competencies were used to predict mathematics achievement across preschool and mathematics and word reading achievement at the end of kindergarten. Both intelligence and executive functions predicted growth in the four quantitative competencies, especially across the first year of preschool. A combination of domain-general and domain-specific competencies predicted preschoolers' mathematics achievement, with a trend for domain-specific skills to be more strongly related to achievement at the beginning of preschool than at the end of preschool. Preschool preliteracy skills, sensitivity to the relative quantities of collections of objects, and cardinal knowledge predicted

  1. Can quantitative sensory testing predict responses to analgesic treatment?

    PubMed

    Grosen, K; Fischer, I W D; Olesen, A E; Drewes, A M

    2013-10-01

    The role of quantitative sensory testing (QST) in prediction of analgesic effect in humans is scarcely investigated. This updated review assesses the effectiveness in predicting analgesic effects in healthy volunteers, surgical patients and patients with chronic pain. A systematic review of English written, peer-reviewed articles was conducted using PubMed and Embase (1980-2013). Additional studies were identified by chain searching. Search terms included 'quantitative sensory testing', 'sensory testing' and 'analgesics'. Studies on the relationship between QST and response to analgesic treatment in human adults were included. Appraisal of the methodological quality of the included studies was based on evaluative criteria for prognostic studies. Fourteen studies (including 720 individuals) met the inclusion criteria. Significant correlations were observed between responses to analgesics and several QST parameters including (1) heat pain threshold in experimental human pain, (2) electrical and heat pain thresholds, pressure pain tolerance and suprathreshold heat pain in surgical patients, and (3) electrical and heat pain threshold and conditioned pain modulation in patients with chronic pain. Heterogeneity among studies was observed especially with regard to application of QST and type and use of analgesics. Although promising, the current evidence is not sufficiently robust to recommend the use of any specific QST parameter in predicting analgesic response. Future studies should focus on a range of different experimental pain modalities rather than a single static pain stimulation paradigm. © 2013 European Federation of International Association for the Study of Pain Chapters.

  2. How accurate are resting energy expenditure prediction equations in obese trauma and burn patients?

    PubMed

    Stucky, Chee-Chee H; Moncure, Michael; Hise, Mary; Gossage, Clint M; Northrop, David

    2008-01-01

    While the prevalence of obesity continues to increase in our society, outdated resting energy expenditure (REE) prediction equations may overpredict energy requirements in obese patients. Accurate feeding is essential since overfeeding has been demonstrated to adversely affect outcomes. The first objective was to compare REE calculated by prediction equations to the measured REE in obese trauma and burn patients. Our hypothesis was that an equation using fat-free mass would give a more accurate prediction. The second objective was to consider the effect of a commonly used injury factor on the predicted REE. A retrospective chart review was performed on 28 patients. REE was measured using indirect calorimetry and compared with the Harris-Benedict and Cunningham equations, and an equation using type II diabetes as a factor. Statistical analyses used were paired t test, +/-95% confidence interval, and the Bland-Altman method. Measured average REE in trauma and burn patients was 21.37 +/- 5.26 and 21.81 +/- 3.35 kcal/kg/d, respectively. Harris-Benedict underpredicted REE in trauma and burn patients to the least extent, while the Cunningham equation underpredicted REE in both populations to the greatest extent. Using an injury factor of 1.2, Cunningham continued to underestimate REE in both populations, while the Harris-Benedict and Diabetic equations overpredicted REE in both populations. The measured average REE is significantly less than current guidelines. This finding suggests that a hypocaloric regimen is worth considering for ICU patients. Also, if an injury factor of 1.2 is incorporated in certain equations, patients may be given too many calories.

  3. Quantitative analysis of naphthenic acids in water by liquid chromatography-accurate mass time-of-flight mass spectrometry.

    PubMed

    Hindle, Ralph; Noestheden, Matthew; Peru, Kerry; Headley, John

    2013-04-19

    This study details the development of a routine method for quantitative analysis of oil sands naphthenic acids, which are a complex class of compounds found naturally and as contaminants in oil sands process waters from Alberta's Athabasca region. Expanding beyond classical naphthenic acids (CnH2n-zO2), those compounds conforming to the formula CnH2n-zOx (where 2≥x≤4) were examined in commercial naphthenic acid and environmental water samples. HPLC facilitated a five-fold reduction in ion suppression when compared to the more commonly used flow injection analysis. A comparison of 39 model naphthenic acids revealed significant variability in response factors, demonstrating the necessity of using naphthenic acid mixtures for quantitation, rather than model compounds. It was also demonstrated that naphthenic acidic heterogeneity (commercial and environmental) necessitates establishing a single NA mix as the standard against which all quantitation is performed. The authors present the first ISO17025 accredited method for the analysis of naphthenic acids in water using HPLC high resolution accurate mass time-of-flight mass spectrometry. The method detection limit was 1mg/L total oxy-naphthenic acids (Sigma technical mix). Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Accurate ECG diagnosis of atrial tachyarrhythmias using quantitative analysis: a prospective diagnostic and cost-effectiveness study.

    PubMed

    Krummen, David E; Patel, Mitul; Nguyen, Hong; Ho, Gordon; Kazi, Dhruv S; Clopton, Paul; Holland, Marian C; Greenberg, Scott L; Feld, Gregory K; Faddis, Mitchell N; Narayan, Sanjiv M

    2010-11-01

    Quantitative ECG Analysis. Optimal atrial tachyarrhythmia management is facilitated by accurate electrocardiogram interpretation, yet typical atrial flutter (AFl) may present without sawtooth F-waves or RR regularity, and atrial fibrillation (AF) may be difficult to separate from atypical AFl or rapid focal atrial tachycardia (AT). We analyzed whether improved diagnostic accuracy using a validated analysis tool significantly impacts costs and patient care. We performed a prospective, blinded, multicenter study using a novel quantitative computerized algorithm to identify atrial tachyarrhythmia mechanism from the surface ECG in patients referred for electrophysiology study (EPS). In 122 consecutive patients (age 60 ± 12 years) referred for EPS, 91 sustained atrial tachyarrhythmias were studied. ECGs were also interpreted by 9 physicians from 3 specialties for comparison and to allow healthcare system modeling. Diagnostic accuracy was compared to the diagnosis at EPS. A Markov model was used to estimate the impact of improved arrhythmia diagnosis. We found 13% of typical AFl ECGs had neither sawtooth flutter waves nor RR regularity, and were misdiagnosed by the majority of clinicians (0/6 correctly diagnosed by consensus visual interpretation) but correctly by quantitative analysis in 83% (5/6, P = 0.03). AF diagnosis was also improved through use of the algorithm (92%) versus visual interpretation (primary care: 76%, P < 0.01). Economically, we found that these improvements in diagnostic accuracy resulted in an average cost-savings of $1,303 and 0.007 quality-adjusted-life-years per patient. Typical AFl and AF are frequently misdiagnosed using visual criteria. Quantitative analysis improves diagnostic accuracy and results in improved healthcare costs and patient outcomes. © 2010 Wiley Periodicals, Inc.

  5. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

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

    Visel, Axel; Blow, Matthew J.; Li, Zirong

    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. Wemore » 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.« less

  6. Analytical method for the accurate determination of tricothecenes in grains using LC-MS/MS: a comparison between MRM transition and MS3 quantitation.

    PubMed

    Lim, Chee Wei; Tai, Siew Hoon; Lee, Lin Min; Chan, Sheot Harn

    2012-07-01

    The current food crisis demands unambiguous determination of mycotoxin contamination in staple foods to achieve safer food for consumption. This paper describes the first accurate LC-MS/MS method developed to analyze tricothecenes in grains by applying multiple reaction monitoring (MRM) transition and MS(3) quantitation strategies in tandem. The tricothecenes are nivalenol, deoxynivalenol, deoxynivalenol-3-glucoside, fusarenon X, 3-acetyl-deoxynivalenol, 15-acetyldeoxynivalenol, diacetoxyscirpenol, and HT-2 and T-2 toxins. Acetic acid and ammonium acetate were used to convert the analytes into their respective acetate adducts and ammonium adducts under negative and positive MS polarity conditions, respectively. The mycotoxins were separated by reversed-phase LC in a 13.5-min run, ionized using electrospray ionization, and detected by tandem mass spectrometry. Analyte-specific mass-to-charge (m/z) ratios were used to perform quantitation under MRM transition and MS(3) (linear ion trap) modes. Three experiments were made for each quantitation mode and matrix in batches over 6 days for recovery studies. The matrix effect was investigated at concentration levels of 20, 40, 80, 120, 160, and 200 μg kg(-1) (n = 3) in 5 g corn flour and rice flour. Extraction with acetonitrile provided a good overall recovery range of 90-108% (n = 3) at three levels of spiking concentration of 40, 80, and 120 μg kg(-1). A quantitation limit of 2-6 μg kg(-1) was achieved by applying an MRM transition quantitation strategy. Under MS(3) mode, a quantitation limit of 4-10 μg kg(-1) was achieved. Relative standard deviations of 2-10% and 2-11% were reported for MRM transition and MS(3) quantitation, respectively. The successful utilization of MS(3) enabled accurate analyte fragmentation pattern matching and its quantitation, leading to the development of analytical methods in fields that demand both analyte specificity and fragmentation fingerprint-matching capabilities that are

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

    PubMed

    Kim, Scott Y H

    2014-04-01

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

  8. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

    PubMed

    Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

    2018-01-01

    The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

  9. Testing process predictions of models of risky choice: a quantitative model comparison approach

    PubMed Central

    Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

    2013-01-01

    This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

  10. Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

    NASA Astrophysics Data System (ADS)

    Theologou, I.; Patelaki, M.; Karantzalos, K.

    2015-04-01

    Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

  11. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor.

    PubMed

    Vallejo, Roger L; Silva, Rafael M O; Evenhuis, Jason P; Gao, Guangtu; Liu, Sixin; Parsons, James E; Martin, Kyle E; Wiens, Gregory D; Lourenco, Daniela A L; Leeds, Timothy D; Palti, Yniv

    2018-06-05

    Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium-density single nucleotide polymorphism (SNP) array. Here, the impact of lower-density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)-flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree-based prediction (0.50-0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL-flanking SNP (0.65-0.72) was similar to the panel with 35K SNP (0.65-0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r 2  ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long-range LD likely contributed to the accurate genomic predictions with the low-density SNP panels. Population structure analysis supported the hypothesis that long-range LD in this population may be caused by admixture. Results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs. © 2018 The Authors. This article is a U.S. Government work and is in the public domain in the USA. Journal of Animal Breeding and Genetics published by Blackwell Verlag GmbH.

  12. Kinetic approach to degradation mechanisms in polymer solar cells and their accurate lifetime predictions

    NASA Astrophysics Data System (ADS)

    Arshad, Muhammad Azeem; Maaroufi, AbdelKrim

    2018-07-01

    A beginning has been made in the present study regarding the accurate lifetime predictions of polymer solar cells. Certain reservations about the conventionally employed temperature accelerated lifetime measurements test for its unworthiness of predicting reliable lifetimes of polymer solar cells are brought into light. Critical issues concerning the accelerated lifetime testing include, assuming reaction mechanism instead of determining it, and relying solely on the temperature acceleration of a single property of material. An advanced approach comprising a set of theoretical models to estimate the accurate lifetimes of polymer solar cells is therefore suggested in order to suitably alternate the accelerated lifetime testing. This approach takes into account systematic kinetic modeling of various possible polymer degradation mechanisms under natural weathering conditions. The proposed kinetic approach is substantiated by its applications on experimental aging data-sets of polymer solar materials/solar cells including, P3HT polymer film, bulk heterojunction (MDMO-PPV:PCBM) and dye-sensitized solar cells. Based on the suggested approach, an efficacious lifetime determination formula for polymer solar cells is derived and tested on dye-sensitized solar cells. Some important merits of the proposed method are also pointed out and its prospective applications are discussed.

  13. The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer.

    PubMed

    van Rossum, Peter S N; Fried, David V; Zhang, Lifei; Hofstetter, Wayne L; van Vulpen, Marco; Meijer, Gert J; Court, Laurence E; Lin, Steven H

    2016-05-01

    A reliable prediction of a pathologic complete response (pathCR) to chemoradiotherapy before surgery for esophageal cancer would enable investigators to study the feasibility and outcome of an organ-preserving strategy after chemoradiotherapy. So far no clinical parameters or diagnostic studies are able to accurately predict which patients will achieve a pathCR. The aim of this study was to determine whether subjective and quantitative assessment of baseline and postchemoradiation (18)F-FDG PET can improve the accuracy of predicting pathCR to preoperative chemoradiotherapy in esophageal cancer beyond clinical predictors. This retrospective study was approved by the institutional review board, and the need for written informed consent was waived. Clinical parameters along with subjective and quantitative parameters from baseline and postchemoradiation (18)F-FDG PET were derived from 217 esophageal adenocarcinoma patients who underwent chemoradiotherapy followed by surgery. The associations between these parameters and pathCR were studied in univariable and multivariable logistic regression analysis. Four prediction models were constructed and internally validated using bootstrapping to study the incremental predictive values of subjective assessment of (18)F-FDG PET, conventional quantitative metabolic features, and comprehensive (18)F-FDG PET texture/geometry features, respectively. The clinical benefit of (18)F-FDG PET was determined using decision-curve analysis. A pathCR was found in 59 (27%) patients. A clinical prediction model (corrected c-index, 0.67) was improved by adding (18)F-FDG PET-based subjective assessment of response (corrected c-index, 0.72). This latter model was slightly improved by the addition of 1 conventional quantitative metabolic feature only (i.e., postchemoradiation total lesion glycolysis; corrected c-index, 0.73), and even more by subsequently adding 4 comprehensive (18)F-FDG PET texture/geometry features (corrected c-index, 0

  14. Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

    PubMed

    Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

    2016-08-01

    Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    PubMed

    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. © 2013 Wiley Periodicals, Inc.

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

    PubMed

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

    2007-06-18

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

  17. Urinary Squamous Epithelial Cells Do Not Accurately Predict Urine Culture Contamination, but May Predict Urinalysis Performance in Predicting Bacteriuria.

    PubMed

    Mohr, Nicholas M; Harland, Karisa K; Crabb, Victoria; Mutnick, Rachel; Baumgartner, David; Spinosi, Stephanie; Haarstad, Michael; Ahmed, Azeemuddin; Schweizer, Marin; Faine, Brett

    2016-03-01

    The presence of squamous epithelial cells (SECs) has been advocated to identify urinary contamination despite a paucity of evidence supporting this practice. We sought to determine the value of using quantitative SECs as a predictor of urinalysis contamination. Retrospective cross-sectional study of adults (≥18 years old) presenting to a tertiary academic medical center who had urinalysis with microscopy and urine culture performed. Patients with missing or implausible demographic data were excluded (2.5% of total sample). The primary analysis aimed to determine an SEC threshold that predicted urine culture contamination using receiver operating characteristics (ROC) curve analysis. The a priori secondary analysis explored how demographic variables (age, sex, body mass index) may modify the SEC test performance and whether SECs impacted traditional urinalysis indicators of bacteriuria. A total of 19,328 records were included. ROC curve analysis demonstrated that SEC count was a poor predictor of urine culture contamination (area under the ROC curve = 0.680, 95% confidence interval [CI] = 0.671 to 0.689). In secondary analysis, the positive likelihood ratio (LR+) of predicting bacteriuria via urinalysis among noncontaminated specimens was 4.98 (95% CI = 4.59 to 5.40) in the absence of SECs, but the LR+ fell to 2.35 (95% CI = 2.17 to 2.54) for samples with more than 8 SECs/low-powered field (lpf). In an independent validation cohort, urinalysis samples with fewer than 8 SECs/lpf predicted bacteriuria better (sensitivity = 75%, specificity = 84%) than samples with more than 8 SECs/lpf (sensitivity = 86%, specificity = 70%; diagnostic odds ratio = 17.5 [14.9 to 20.7] vs. 8.7 [7.3 to 10.5]). Squamous epithelial cells are a poor predictor of urine culture contamination, but may predict poor predictive performance of traditional urinalysis measures. © 2016 by the Society for Academic Emergency Medicine.

  18. Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings.

    PubMed

    Adde, Lars; Helbostad, Jorunn; Jensenius, Alexander R; Langaas, Mette; Støen, Ragnhild

    2013-08-01

    This study evaluates the role of postterm age at assessment and the use of one or two video recordings for the detection of fidgety movements (FMs) and prediction of cerebral palsy (CP) using computer vision software. Recordings between 9 and 17 weeks postterm age from 52 preterm and term infants (24 boys, 28 girls; 26 born preterm) were used. Recordings were analyzed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analysis. Sensitivities, specificities, and area under curve were estimated for the first and second recording, or a mean of both. FMs were classified based on the Prechtl approach of general movement assessment. CP status was reported at 2 years. Nine children developed CP of whom all recordings had absent FMs. The mean variability of the centroid of motion (CSD) from two recordings was more accurate than using only one recording, and identified all children who were diagnosed with CP at 2 years. Age at assessment did not influence the detection of FMs or prediction of CP. The accuracy of computer vision techniques in identifying FMs and predicting CP based on two recordings should be confirmed in future studies.

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

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

    Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

    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.more » 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.« less

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

    NASA Astrophysics Data System (ADS)

    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

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

    PubMed

    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

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

    PubMed Central

    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

  3. Obtaining Accurate Probabilities Using Classifier Calibration

    ERIC Educational Resources Information Center

    Pakdaman Naeini, Mahdi

    2016-01-01

    Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

  4. Comparing quantitative values of two generations of laser-assisted indocyanine green dye angiography systems: can we predict necrosis?

    PubMed

    Phillips, Brett T; Fourman, Mitchell S; Rivara, Andrew; Dagum, Alexander B; Huston, Tara L; Ganz, Jason C; Bui, Duc T; Khan, Sami U

    2014-01-01

    Several devices exist today to assist the intraoperative determination of skin flap perfusion. Laser-Assisted Indocyanine Green Dye Angiography (LAICGA) has been shown to accurately predict mastectomy skin flap necrosis using quantitative perfusion values. The laser properties of the latest LAICGA device (SPY Elite) differ significantly from its predecessor system (SPY 2001), preventing direct translation of previous published data. The purpose of this study was to establish a mathematical relationship of perfusion values between these 2 devices. Breast reconstruction patients were prospectively enrolled into a clinical trial where skin flap evaluation and excision was based on quantitative SPY Q values previously established in the literature. Initial study patients underwent mastectomy skin flap evaluation using both SPY systems simultaneously. Absolute perfusion unit (APU) values at identical locations on the breast were then compared graphically. 210 data points were identified on the same patients (n = 4) using both SPY systems. A linear relationship (y = 2.9883x + 12.726) was identified with a high level or correlation (R(2) = 0.744). Previously published values using SPY 2001 (APU 3.7) provided a value of 23.8 APU on the SPY Elite. In addition, postoperative necrosis in these patients correlated to regions of skin identified with the SPY Elite with APU less than 23.8. Intraoperative comparison of LAICGA systems has provided direct correlation of perfusion values predictive of necrosis that were previously established in the literature. An APU value of 3.7 from the SPY 2001 correlates to a SPY Elite APU value of 23.8.

  5. Comparing Quantitative Values of Two Generations of Laser-Assisted Indocyanine Green Dye Angiography Systems: Can We Predict Necrosis?

    PubMed Central

    Fourman, Mitchell S.; Rivara, Andrew; Dagum, Alexander B.; Huston, Tara L.; Ganz, Jason C.; Bui, Duc T.; Khan, Sami U.

    2014-01-01

    Objective: Several devices exist today to assist the intraoperative determination of skin flap perfusion. Laser-Assisted Indocyanine Green Dye Angiography (LAICGA) has been shown to accurately predict mastectomy skin flap necrosis using quantitative perfusion values. The laser properties of the latest LAICGA device (SPY Elite) differ significantly from its predecessor system (SPY 2001), preventing direct translation of previous published data. The purpose of this study was to establish a mathematical relationship of perfusion values between these 2 devices. Methods: Breast reconstruction patients were prospectively enrolled into a clinical trial where skin flap evaluation and excision was based on quantitative SPY Q values previously established in the literature. Initial study patients underwent mastectomy skin flap evaluation using both SPY systems simultaneously. Absolute perfusion unit (APU) values at identical locations on the breast were then compared graphically. Results: 210 data points were identified on the same patients (n = 4) using both SPY systems. A linear relationship (y = 2.9883x + 12.726) was identified with a high level or correlation (R2 = 0.744). Previously published values using SPY 2001 (APU 3.7) provided a value of 23.8 APU on the SPY Elite. In addition, postoperative necrosis in these patients correlated to regions of skin identified with the SPY Elite with APU less than 23.8. Conclusion: Intraoperative comparison of LAICGA systems has provided direct correlation of perfusion values predictive of necrosis that were previously established in the literature. An APU value of 3.7 from the SPY 2001 correlates to a SPY Elite APU value of 23.8. PMID:25525483

  6. Universal structural parameter to quantitatively predict metallic glass properties

    DOE PAGES

    Ding, Jun; Cheng, Yong-Qiang; Sheng, Howard; ...

    2016-12-12

    Quantitatively correlating the amorphous structure in metallic glasses (MGs) with their physical properties has been a long-sought goal. Here we introduce flexibility volume' as a universal indicator, to bridge the structural state the MG is in with its properties, on both atomic and macroscopic levels. The flexibility volume combines static atomic volume with dynamics information via atomic vibrations that probe local configurational space and interaction between neighbouring atoms. We demonstrate that flexibility volume is a physically appropriate parameter that can quantitatively predict the shear modulus, which is at the heart of many key properties of MGs. Moreover, the new parametermore » correlates strongly with atomic packing topology, and also with the activation energy for thermally activated relaxation and the propensity for stress-driven shear transformations. These correlations are expected to be robust across a very wide range of MG compositions, processing conditions and length scales.« less

  7. Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb

    DOE PAGES

    Monti, Remo; Barozzi, Iros; Osterwalder, Marco; ...

    2017-08-21

    Epigenomic mapping of enhancer-associated chromatin modifications facilitates the genome-wide discovery of tissue-specific enhancers in vivo. However, reliance on single chromatin marks leads to high rates of false-positive predictions. More sophisticated, integrative methods have been described, but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability. Here we present the Limb-Enhancer Genie (LEG), a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, available through a user-friendly online interface. We predict limb enhancers using a combination of > 50 published limb-specific datasets and clusters of evolutionarily conserved transcription factor binding sites, taking advantage ofmore » the patterns observed at previously in vivo validated elements. By combining different statistical models, our approach outperforms current state-of-the-art methods and provides interpretable measures of feature importance. Our results indicate that including a previously unappreciated score that quantifies tissue-specific nuclease accessibility significantly improves prediction performance. We demonstrate the utility of our approach through in vivo validation of newly predicted elements. Moreover, we describe general features that can guide the type of datasets to include when predicting tissue-specific enhancers genome-wide, while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations.« less

  8. Preferential access to genetic information from endogenous hominin ancient DNA and accurate quantitative SNP-typing via SPEX

    PubMed Central

    Brotherton, Paul; Sanchez, Juan J.; Cooper, Alan; Endicott, Phillip

    2010-01-01

    The analysis of targeted genetic loci from ancient, forensic and clinical samples is usually built upon polymerase chain reaction (PCR)-generated sequence data. However, many studies have shown that PCR amplification from poor-quality DNA templates can create sequence artefacts at significant levels. With hominin (human and other hominid) samples, the pervasive presence of highly PCR-amplifiable human DNA contaminants in the vast majority of samples can lead to the creation of recombinant hybrids and other non-authentic artefacts. The resulting PCR-generated sequences can then be difficult, if not impossible, to authenticate. In contrast, single primer extension (SPEX)-based approaches can genotype single nucleotide polymorphisms from ancient fragments of DNA as accurately as modern DNA. A single SPEX-type assay can amplify just one of the duplex DNA strands at target loci and generate a multi-fold depth-of-coverage, with non-authentic recombinant hybrids reduced to undetectable levels. Crucially, SPEX-type approaches can preferentially access genetic information from damaged and degraded endogenous ancient DNA templates over modern human DNA contaminants. The development of SPEX-type assays offers the potential for highly accurate, quantitative genotyping from ancient hominin samples. PMID:19864251

  9. Quantitative Sensory Testing Predicts Pregabalin Efficacy in Painful Chronic Pancreatitis

    PubMed Central

    Olesen, Søren S.; Graversen, Carina; Bouwense, Stefan A. W.; van Goor, Harry; Wilder-Smith, Oliver H. G.; Drewes, Asbjørn M.

    2013-01-01

    Background A major problem in pain medicine is the lack of knowledge about which treatment suits a specific patient. We tested the ability of quantitative sensory testing to predict the analgesic effect of pregabalin and placebo in patients with chronic pancreatitis. Methods Sixty-four patients with painful chronic pancreatitis received pregabalin (150–300 mg BID) or matching placebo for three consecutive weeks. Analgesic effect was documented in a pain diary based on a visual analogue scale. Responders were defined as patients with a reduction in clinical pain score of 30% or more after three weeks of study treatment compared to baseline recordings. Prior to study medication, pain thresholds to electric skin and pressure stimulation were measured in dermatomes T10 (pancreatic area) and C5 (control area). To eliminate inter-subject differences in absolute pain thresholds an index of sensitivity between stimulation areas was determined (ratio of pain detection thresholds in pancreatic versus control area, ePDT ratio). Pain modulation was recorded by a conditioned pain modulation paradigm. A support vector machine was used to screen sensory parameters for their predictive power of pregabalin efficacy. Results The pregabalin responders group was hypersensitive to electric tetanic stimulation of the pancreatic area (ePDT ratio 1.2 (0.9–1.3)) compared to non-responders group (ePDT ratio: 1.6 (1.5–2.0)) (P = 0.001). The electrical pain detection ratio was predictive for pregabalin effect with a classification accuracy of 83.9% (P = 0.007). The corresponding sensitivity was 87.5% and specificity was 80.0%. No other parameters were predictive of pregabalin or placebo efficacy. Conclusions The present study provides first evidence that quantitative sensory testing predicts the analgesic effect of pregabalin in patients with painful chronic pancreatitis. The method can be used to tailor pain medication based on patient’s individual sensory profile and thus

  10. Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

    PubMed Central

    Garcia Lopez, Sebastian; Kim, Philip M.

    2014-01-01

    Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

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

    PubMed

    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. Copyright © 2011 Wiley-Liss, Inc.

  12. Accurate experimental and theoretical comparisons between superconductor-insulator-superconductor mixers showing weak and strong quantum effects

    NASA Technical Reports Server (NTRS)

    Mcgrath, W. R.; Richards, P. L.; Face, D. W.; Prober, D. E.; Lloyd, F. L.

    1988-01-01

    A systematic study of the gain and noise in superconductor-insulator-superconductor mixers employing Ta based, Nb based, and Pb-alloy based tunnel junctions was made. These junctions displayed both weak and strong quantum effects at a signal frequency of 33 GHz. The effects of energy gap sharpness and subgap current were investigated and are quantitatively related to mixer performance. Detailed comparisons are made of the mixing results with the predictions of a three-port model approximation to the Tucker theory. Mixer performance was measured with a novel test apparatus which is accurate enough to allow for the first quantitative tests of theoretical noise predictions. It is found that the three-port model of the Tucker theory underestimates the mixer noise temperature by a factor of about 2 for all of the mixers. In addition, predicted values of available mixer gain are in reasonable agreement with experiment when quantum effects are weak. However, as quantum effects become strong, the predicted available gain diverges to infinity, which is in sharp contrast to the experimental results. Predictions of coupled gain do not always show such divergences.

  13. Predicting Team Performance through Human Behavioral Sensing and Quantitative Workflow Instrumentation

    DTIC Science & Technology

    2016-07-27

    make risk-informed decisions during serious games . Statistical models of intra- game performance were developed to determine whether behaviors in...specific facets of the gameplay workflow were predictive of analytical performance and games outcomes. A study of over seventy instrumented teams revealed...more accurate game decisions. 2 Keywords: Humatics · Serious Games · Human-System Interaction · Instrumentation · Teamwork · Communication Analysis

  14. Prediction of Coronal Mass Ejections From Vector Magnetograms: Quantitative Measures as Predictors

    NASA Technical Reports Server (NTRS)

    Falconer, D. A.; Moore, R. L.; Gary, G. A.; Rose, M. Franklin (Technical Monitor)

    2001-01-01

    We derived two quantitative measures of an active region's global nonpotentiality from the region's vector magnetogram, 1) the net current (I(sub N)), and 2) the length of strong-shear, strong-field main neutral line (Lss), and used these two measures in a pilot study of the CME productivity of 4 active regions. We compared the global nonpotentiality measures to the active regions' CME productivity determined from GOES and Yohkoh/SXT observations. We found that two of the active regions were highly globally nonpotential and were CME productive, while the other two active regions had little global nonpotentiality and produced no CMEs. At the Fall 2000 AGU, we reported on an expanded study (12 active regions and 17 magnetograms) in which we evaluated four quantitative global measures of an active region's magnetic field and compared these measures with the CME productivity. The four global measures (all derived from MSFC vector magnetograms) included our two previous measures (I(sub N) and L(sub ss)) as well as two new ones, the total magnetic flux (PHI) (a measure of an active region's size), and the normalized twist (alpha (bar)= muIN/PHI). We found that the three quantitative measures of global nonpotentiality (I(sub N), L(sub ss), alpha (bar)) were all well correlated (greater than 99% confidence level) with an active region's CME productivity within plus or minus 2 days of the day of the magnetogram. We will now report on our findings of how good our quantitative measures are as predictors of active-region CME productivity, using only CMEs that occurred after the magnetogram. We report the preliminary skill test of these quantitative measures as predictors. We compare the CME prediction success of our quantitative measures to the CME prediction success based on an active region's past CME productivity. We examine the cases of the handful of false positive and false negatives to look for improvements to our predictors. This work is funded by NSF through the Space

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

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

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

    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 datasetsmore » 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

  16. A new rapid quantitative test for fecal calprotectin predicts endoscopic activity in ulcerative colitis.

    PubMed

    Lobatón, Triana; Rodríguez-Moranta, Francisco; Lopez, Alicia; Sánchez, Elena; Rodríguez-Alonso, Lorena; Guardiola, Jordi

    2013-04-01

    Fecal calprotectin (FC) determined by the enzyme-linked immunosorbent assay (ELISA) test has been proposed as a promising biomarker of endoscopic activity in ulcerative colitis (UC). However, data on its accuracy in predicting endoscopic activity is scarce. Besides, FC determined by the quantitative-point-of-care test (FC-QPOCT) that provides rapid and individual results could optimize its use in clinical practice. The aims of our study were to evaluate the ability of FC to predict endoscopic activity according to the Mayo score in patients with UC when determined by FC-QPOCT and to compare it with the ELISA test (FC-ELISA). FC was determined simultaneously by FC-ELISA and FC-QPOCT in patients with UC undergoing colonoscopy. Clinical disease activity and endoscopy were assessed according to the Mayo score. Blood tests were taken to analyze serological biomarkers. A total of 146 colonoscopies were performed on 123 patients with UC. FC-QPOCT correlated more closely with the Mayo endoscopic subscore (Spearman's correlation coefficient rank r = 0.727, P < 0.001) than clinical activity (r = 0.636, P < 0.001), platelets (r = 0.381, P < 0.001), leucocytes (r = 0.300, P < 0.001), and C-reactive protein (r = 0.291, P = 0.002). The prediction of "endoscopic remission" (Mayo endoscopic subscore ≤1) with FC-QPOCT (280 µg/g) and FC-ELISA (250 µg/g) presented an area under the curve of 0.906 and 0.924, respectively. The interclass correlation index between both tests was 0.904 (95% confidence interval, 0.864-0.932; P < 0.001). FC determined by QPOCT was an accurate surrogate marker of "endoscopic remission" in UC and presented a good correlation with the FC-ELISA test.

  17. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.

    PubMed

    Jones, David T; Singh, Tanya; Kosciolek, Tomasz; Tetchner, Stuart

    2015-04-01

    Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

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

    PubMed

    Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

    2017-09-01

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

  19. WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

    PubMed

    Chi, Sang-Mun; Nam, Dougu

    2012-04-01

    We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

  20. Sex-specific lean body mass predictive equations are accurate in the obese paediatric population

    PubMed Central

    Jackson, Lanier B.; Henshaw, Melissa H.; Carter, Janet; Chowdhury, Shahryar M.

    2015-01-01

    Background The clinical assessment of lean body mass (LBM) is challenging in obese children. A sex-specific predictive equation for LBM derived from anthropometric data was recently validated in children. Aim The purpose of this study was to independently validate these predictive equations in the obese paediatric population. Subjects and methods Obese subjects aged 4–21 were analysed retrospectively. Predicted LBM (LBMp) was calculated using equations previously developed in children. Measured LBM (LBMm) was derived from dual-energy x-ray absorptiometry. Agreement was expressed as [(LBMm-LBMp)/LBMm] with 95% limits of agreement. Results Of 310 enrolled patients, 195 (63%) were females. The mean age was 11.8 ± 3.4 years and mean BMI Z-score was 2.3 ± 0.4. The average difference between LBMm and LBMp was −0.6% (−17.0%, 15.8%). Pearson’s correlation revealed a strong linear relationship between LBMm and LBMp (r=0.97, p<0.01). Conclusion This study validates the use of these clinically-derived sex-specific LBM predictive equations in the obese paediatric population. Future studies should use these equations to improve the ability to accurately classify LBM in obese children. PMID:26287383

  1. Quantitative fetal fibronectin and cervical length to predict preterm birth in asymptomatic women with previous cervical surgery.

    PubMed

    Vandermolen, Brooke I; Hezelgrave, Natasha L; Smout, Elizabeth M; Abbott, Danielle S; Seed, Paul T; Shennan, Andrew H

    2016-10-01

    Quantitative fetal fibronectin testing has demonstrated accuracy for prediction of spontaneous preterm birth in asymptomatic women with a history of preterm birth. Predictive accuracy in women with previous cervical surgery (a potentially different risk mechanism) is not known. We sought to compare the predictive accuracy of cervicovaginal fluid quantitative fetal fibronectin and cervical length testing in asymptomatic women with previous cervical surgery to that in women with 1 previous preterm birth. We conducted a prospective blinded secondary analysis of a larger observational study of cervicovaginal fluid quantitative fetal fibronectin concentration in asymptomatic women measured with a Hologic 10Q system (Hologic, Marlborough, MA). Prediction of spontaneous preterm birth (<30, <34, and <37 weeks) with cervicovaginal fluid quantitative fetal fibronectin concentration in primiparous women who had undergone at least 1 invasive cervical procedure (n = 473) was compared with prediction in women who had previous spontaneous preterm birth, preterm prelabor rupture of membranes, or late miscarriage (n = 821). Relationship with cervical length was explored. The rate of spontaneous preterm birth <34 weeks in the cervical surgery group was 3% compared with 9% in previous spontaneous preterm birth group. Receiver operating characteristic curves comparing quantitative fetal fibronectin for prediction at all 3 gestational end points were comparable between the cervical surgery and previous spontaneous preterm birth groups (34 weeks: area under the curve, 0.78 [95% confidence interval 0.64-0.93] vs 0.71 [95% confidence interval 0.64-0.78]; P = .39). Prediction of spontaneous preterm birth using cervical length compared with quantitative fetal fibronectin for prediction of preterm birth <34 weeks of gestation offered similar prediction (area under the curve, 0.88 [95% confidence interval 0.79-0.96] vs 0.77 [95% confidence interval 0.62-0.92], P = .12 in the cervical

  2. Accurate prediction of cation-π interaction energy using substituent effects.

    PubMed

    Sayyed, Fareed Bhasha; Suresh, Cherumuttathu H

    2012-06-14

    (M(+))' and ΔV(min). All the Φ-X···M(+) systems showed good agreement between the calculated and predicted E(M(+))() values, suggesting that the ΔV(min) approach to substituent effect is accurate and useful for predicting the interactive behavior of substituted π-systems with cations.

  3. Global Quantitative Modeling of Chromatin Factor Interactions

    PubMed Central

    Zhou, Jian; Troyanskaya, Olga G.

    2014-01-01

    Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the “chromatin codes”) remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles — we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions. PMID:24675896

  4. Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI.

    PubMed

    Adler, Sophie; Lorio, Sara; Jacques, Thomas S; Benova, Barbora; Gunny, Roxana; Cross, J Helen; Baldeweg, Torsten; Carmichael, David W

    2017-01-01

    Focal cortical dysplasias (FCDs) are a range of malformations of cortical development each with specific histopathological features. Conventional radiological assessment of standard structural MRI is useful for the localization of lesions but is unable to accurately predict the histopathological features. Quantitative MRI offers the possibility to probe tissue biophysical properties in vivo and may bridge the gap between radiological assessment and ex-vivo histology. This review will cover histological, genetic and radiological features of FCD following the ILAE classification and will explain how quantitative voxel- and surface-based techniques can characterise these features. We will provide an overview of the quantitative MRI measures available, their link with biophysical properties and finally the potential application of quantitative MRI to the problem of FCD subtyping. Future research linking quantitative MRI to FCD histological properties should improve clinical protocols, allow better characterisation of lesions in vivo and tailored surgical planning to the individual.

  5. Advanced 2-dimensional quantitative coronary angiographic analysis for prediction of fractional flow reserve in intermediate coronary stenoses.

    PubMed

    Opolski, Maksymilian P; Pregowski, Jerzy; Kruk, Mariusz; Kepka, Cezary; Staruch, Adam D; Witkowski, Adam

    2014-07-01

    The widespread clinical application of coronary computed tomography angiography (CCTA) has resulted in increased referral patterns of patients with intermediate coronary stenoses to invasive coronary angiography. We evaluated the application of advanced quantitative coronary angiography (A-QCA) for predicting fractional flow reserve (FFR) in intermediate coronary lesions detected on CCTA. Fifty-six patients with 66 single intermediate coronary lesions (≥ 50% to 80% stenosis) on CCTA prospectively underwent coronary angiography and FFR. A-QCA including calculation of the Poiseuille-based index defined as the ratio of lesion length to the fourth power of the minimal lumen diameter (MLD) was performed. Significant stenosis was defined as FFR ≤ 0.80. The mean FFR was 0.86 ± 0.09, and 18 lesions (27%) were functionally significant. FFR correlated with lesion length (R=-0.303, P=0.013), MLD (R=0.527, P<0.001), diameter stenosis (R=-0.404, P=0.001), minimum lumen area (MLA) (R=0.530, P<0.001), lumen stenosis (R=-0.400, P=0.001), and Poiseuille-based index (R=-0.602, P<0.001). The optimal cutoff values for MLD, MLA, diameter stenosis, and lumen stenosis were ≤ 1.3 mm, ≤ 1.5 mm, >44%, and >69%, respectively (maximum negative predictive value of 94% for MLA, maximum positive predictive value of 58% for diameter stenosis). The Poiseuille-based index was the most accurate (C statistic 0.86, sensitivity 100%, specificity 71%, positive predictive value 56%, and negative predictive value 100%) predictor of FFR ≤ 0.80, but showed the lowest interobserver agreement (intraclass correlation coefficient 0.37). A-QCA might be used to rule out significant ischemia in intermediate stenoses detected by CCTA. The diagnostic application of the Poiseuille-based angiographic index is precluded by its high interobserver variability.

  6. Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

    PubMed

    Arcon, Juan Pablo; Defelipe, Lucas A; Modenutti, Carlos P; López, Elias D; Alvarez-Garcia, Daniel; Barril, Xavier; Turjanski, Adrián G; Martí, Marcelo A

    2017-04-24

    One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 μs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

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

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    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 resultsmore » 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.« less

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

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe 3+ 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 %Fe 3+ 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 yieldmore » 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.« less

  9. Accurate quantitation of circulating cell-free mitochondrial DNA in plasma by droplet digital PCR.

    PubMed

    Ye, Wei; Tang, Xiaojun; Liu, Chu; Wen, Chaowei; Li, Wei; Lyu, Jianxin

    2017-04-01

    To establish a method for accurate quantitation of circulating cell-free mitochondrial DNA (ccf-mtDNA) in plasma by droplet digital PCR (ddPCR), we designed a ddPCR method to determine the copy number of ccf-mtDNA by amplifying mitochondrial ND1 (MT-ND1). To evaluate the sensitivity and specificity of the method, a recombinant pMD18-T plasmid containing MT-ND1 sequences and mtDNA-deleted (ρ 0 ) HeLa cells were used, respectively. Subsequently, different plasma samples were prepared for ddPCR to evaluate the feasibility of detecting plasma ccf-mtDNA. In the results, the ddPCR method showed high sensitivity and specificity. When the DNA was extracted from plasma prior to ddPCR, the ccf-mtDNA copy number was higher than that measured without extraction. This difference was not due to a PCR inhibitor, such as EDTA-Na 2 , an anti-coagulant in plasma, because standard EDTA-Na 2 concentration (5 mM) did not significantly inhibit ddPCR reactions. The difference might be attributable to plasma exosomal mtDNA, which was 4.21 ± 0.38 copies/μL of plasma, accounting for ∼19% of plasma ccf-mtDNA. Therefore, ddPCR can quickly and reliably detect ccf-mtDNA from plasma with a prior DNA extraction step, providing for a more accurate detection of ccf-mtDNA. The direct use of plasma as a template in ddPCR is suitable for the detection of exogenous cell-free nucleic acids within plasma, but not of nucleic acids that have a vesicle-associated form, such as exosomal mtDNA. Graphical Abstract Designs of the present work. *: Module 1, #: Module 2, &: Module 3.

  10. Absolute Measurements of Macrophage Migration Inhibitory Factor and Interleukin-1-β mRNA Levels Accurately Predict Treatment Response in Depressed Patients.

    PubMed

    Cattaneo, Annamaria; Ferrari, Clarissa; Uher, Rudolf; Bocchio-Chiavetto, Luisella; Riva, Marco Andrea; Pariante, Carmine M

    2016-10-01

    Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms. Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins. We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration. We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more assertive antidepressant strategies

  11. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

    PubMed Central

    Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

    2016-01-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  12. Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

    PubMed

    Creasy, John M; Midya, Abhishek; Chakraborty, Jayasree; Adams, Lauryn B; Gomes, Camilla; Gonen, Mithat; Seastedt, Kenneth P; Sutton, Elizabeth J; Cercek, Andrea; Kemeny, Nancy E; Shia, Jinru; Balachandran, Vinod P; Kingham, T Peter; Allen, Peter J; DeMatteo, Ronald P; Jarnagin, William R; D'Angelica, Michael I; Do, Richard K G; Simpson, Amber L

    2018-06-19

    This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R 2 . Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R 2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be

  13. Assessing deep and shallow learning methods for quantitative prediction of acute chemical toxicity.

    PubMed

    Liu, Ruifeng; Madore, Michael; Glover, Kyle P; Feasel, Michael G; Wallqvist, Anders

    2018-05-02

    Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30,000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

  14. Accurate indel prediction using paired-end short reads

    PubMed Central

    2013-01-01

    Background One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives. Results Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org) in Arabidopsis thaliana. Conclusion In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/. PMID:23442375

  15. Accurate and robust genomic prediction of celiac disease using statistical learning.

    PubMed

    Abraham, Gad; Tye-Din, Jason A; Bhalala, Oneil G; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

    2014-02-01

    Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.

  16. Quantitative Phase Microscopy for Accurate Characterization of Microlens Arrays

    NASA Astrophysics Data System (ADS)

    Grilli, Simonetta; Miccio, Lisa; Merola, Francesco; Finizio, Andrea; Paturzo, Melania; Coppola, Sara; Vespini, Veronica; Ferraro, Pietro

    Microlens arrays are of fundamental importance in a wide variety of applications in optics and photonics. This chapter deals with an accurate digital holography-based characterization of both liquid and polymeric microlenses fabricated by an innovative pyro-electrowetting process. The actuation of liquid and polymeric films is obtained through the use of pyroelectric charges generated into polar dielectric lithium niobate crystals.

  17. DOSIMETRY MODELING OF INHALED FORMALDEHYDE: BINNING NASAL FLUX PREDICTIONS FOR QUANTITATIVE RISK ASSESSMENT

    EPA Science Inventory

    Dosimetry Modeling of Inhaled Formaldehyde: Binning Nasal Flux Predictions for Quantitative Risk Assessment. Kimbell, J.S., Overton, J.H., Subramaniam, R.P., Schlosser, P.M., Morgan, K.T., Conolly, R.B., and Miller, F.J. (2001). Toxicol. Sci. 000, 000:000.

    Interspecies e...

  18. Accurate quantitative CF-LIBS analysis of both major and minor elements in alloys via iterative correction of plasma temperature and spectral intensity

    NASA Astrophysics Data System (ADS)

    Shuxia, ZHAO; Lei, ZHANG; Jiajia, HOU; Yang, ZHAO; Wangbao, YIN; Weiguang, MA; Lei, DONG; Liantuan, XIAO; Suotang, JIA

    2018-03-01

    The chemical composition of alloys directly determines their mechanical behaviors and application fields. Accurate and rapid analysis of both major and minor elements in alloys plays a key role in metallurgy quality control and material classification processes. A quantitative calibration-free laser-induced breakdown spectroscopy (CF-LIBS) analysis method, which carries out combined correction of plasma temperature and spectral intensity by using a second-order iterative algorithm and two boundary standard samples, is proposed to realize accurate composition measurements. Experimental results show that, compared to conventional CF-LIBS analysis, the relative errors for major elements Cu and Zn and minor element Pb in the copper-lead alloys has been reduced from 12%, 26% and 32% to 1.8%, 2.7% and 13.4%, respectively. The measurement accuracy for all elements has been improved substantially.

  19. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

    DOE PAGES

    Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe; ...

    2017-04-07

    In this study, the increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBAmore » predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.« less

  20. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

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

    Bordbar, Aarash; Yurkovich, James T.; Paglia, Giuseppe

    In this study, the increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBAmore » predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.« less

  1. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

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

    Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler

    The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

  2. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

    DOE PAGES

    Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler; ...

    2016-07-02

    The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

  3. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    PubMed

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

  4. Predicting plant biomass accumulation from image-derived parameters

    PubMed Central

    Chen, Dijun; Shi, Rongli; Pape, Jean-Michel; Neumann, Kerstin; Graner, Andreas; Chen, Ming; Klukas, Christian

    2018-01-01

    Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

  5. Quantitative modelling in cognitive ergonomics: predicting signals passed at danger.

    PubMed

    Moray, Neville; Groeger, John; Stanton, Neville

    2017-02-01

    This paper shows how to combine field observations, experimental data and mathematical modelling to produce quantitative explanations and predictions of complex events in human-machine interaction. As an example, we consider a major railway accident. In 1999, a commuter train passed a red signal near Ladbroke Grove, UK, into the path of an express. We use the Public Inquiry Report, 'black box' data, and accident and engineering reports to construct a case history of the accident. We show how to combine field data with mathematical modelling to estimate the probability that the driver observed and identified the state of the signals, and checked their status. Our methodology can explain the SPAD ('Signal Passed At Danger'), generate recommendations about signal design and placement and provide quantitative guidance for the design of safer railway systems' speed limits and the location of signals. Practitioner Summary: Detailed ergonomic analysis of railway signals and rail infrastructure reveals problems of signal identification at this location. A record of driver eye movements measures attention, from which a quantitative model for out signal placement and permitted speeds can be derived. The paper is an example of how to combine field data, basic research and mathematical modelling to solve ergonomic design problems.

  6. Quantitative chest computed tomography as a means of predicting exercise performance in severe emphysema.

    PubMed

    Crausman, R S; Ferguson, G; Irvin, C G; Make, B; Newell, J D

    1995-06-01

    We assessed the value of quantitative high-resolution computed tomography (CT) as a diagnostic and prognostic tool in smoking-related emphysema. We performed an inception cohort study of 14 patients referred with emphysema. The diagnosis of emphysema was based on a compatible history, physical examination, chest radiograph, CT scan of the lung, and pulmonary physiologic evaluation. As a group, those who underwent exercise testing were hyperinflated (percentage predicted total lung capacity +/- standard error of the mean = 133 +/- 9%), and there was evidence of air trapping (percentage predicted respiratory volume = 318 +/- 31%) and airflow limitation (forced expiratory volume in 1 sec [FEV1] = 40 +/- 7%). The exercise performance of the group was severely limited (maximum achievable workload = 43 +/- 6%) and was characterized by prominent ventilatory, gas exchange, and pulmonary vascular abnormalities. The quantitative CT index was markedly elevated in all patients (76 +/- 9; n = 14; normal < 4). There were correlations between this quantitative CT index and measures of airflow limitation (FEV1 r2 = .34, p = 09; FEV1/forced vital capacity r2 = .46, p = .04) and between maximum workload achieved (r2 = .93, p = .0001) and maximum oxygen utilization (r2 = .83, p = .0007). Quantitative chest CT assessment of disease severity is correlated with the degree of airflow limitation and exercise impairment in pulmonary emphysema.

  7. Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.

    PubMed

    Gray, Vanessa E; Hause, Ronald J; Luebeck, Jens; Shendure, Jay; Fowler, Douglas M

    2018-01-24

    Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (https://envision.gs.washington.edu/). Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Quantitative Live-Cell Confocal Imaging of 3D Spheroids in a High-Throughput Format.

    PubMed

    Leary, Elizabeth; Rhee, Claire; Wilks, Benjamin T; Morgan, Jeffrey R

    2018-06-01

    Accurately predicting the human response to new compounds is critical to a wide variety of industries. Standard screening pipelines (including both in vitro and in vivo models) often lack predictive power. Three-dimensional (3D) culture systems of human cells, a more physiologically relevant platform, could provide a high-throughput, automated means to test the efficacy and/or toxicity of novel substances. However, the challenge of obtaining high-magnification, confocal z stacks of 3D spheroids and understanding their respective quantitative limitations must be overcome first. To address this challenge, we developed a method to form spheroids of reproducible size at precise spatial locations across a 96-well plate. Spheroids of variable radii were labeled with four different fluorescent dyes and imaged with a high-throughput confocal microscope. 3D renderings of the spheroid had a complex bowl-like appearance. We systematically analyzed these confocal z stacks to determine the depth of imaging and the effect of spheroid size and dyes on quantitation. Furthermore, we have shown that this loss of fluorescence can be addressed through the use of ratio imaging. Overall, understanding both the limitations of confocal imaging and the tools to correct for these limits is critical for developing accurate quantitative assays using 3D spheroids.

  9. Quantitative DNA Methylation Analysis Identifies a Single CpG Dinucleotide Important for ZAP-70 Expression and Predictive of Prognosis in Chronic Lymphocytic Leukemia

    PubMed Central

    Claus, Rainer; Lucas, David M.; Stilgenbauer, Stephan; Ruppert, Amy S.; Yu, Lianbo; Zucknick, Manuela; Mertens, Daniel; Bühler, Andreas; Oakes, Christopher C.; Larson, Richard A.; Kay, Neil E.; Jelinek, Diane F.; Kipps, Thomas J.; Rassenti, Laura Z.; Gribben, John G.; Döhner, Hartmut; Heerema, Nyla A.; Marcucci, Guido; Plass, Christoph; Byrd, John C.

    2012-01-01

    Purpose Increased ZAP-70 expression predicts poor prognosis in chronic lymphocytic leukemia (CLL). Current methods for accurately measuring ZAP-70 expression are problematic, preventing widespread application of these tests in clinical decision making. We therefore used comprehensive DNA methylation profiling of the ZAP-70 regulatory region to identify sites important for transcriptional control. Patients and Methods High-resolution quantitative DNA methylation analysis of the entire ZAP-70 gene regulatory regions was conducted on 247 samples from patients with CLL from four independent clinical studies. Results Through this comprehensive analysis, we identified a small area in the 5′ regulatory region of ZAP-70 that showed large variability in methylation in CLL samples but was universally methylated in normal B cells. High correlation with mRNA and protein expression, as well as activity in promoter reporter assays, revealed that within this differentially methylated region, a single CpG dinucleotide and neighboring nucleotides are particularly important in ZAP-70 transcriptional regulation. Furthermore, by using clustering approaches, we identified a prognostic role for this site in four independent data sets of patients with CLL using time to treatment, progression-free survival, and overall survival as clinical end points. Conclusion Comprehensive quantitative DNA methylation analysis of the ZAP-70 gene in CLL identified important regions responsible for transcriptional regulation. In addition, loss of methylation at a specific single CpG dinucleotide in the ZAP-70 5′ regulatory sequence is a highly predictive and reproducible biomarker of poor prognosis in this disease. This work demonstrates the feasibility of using quantitative specific ZAP-70 methylation analysis as a relevant clinically applicable prognostic test in CLL. PMID:22564988

  10. Feedback about More Accurate versus Less Accurate Trials: Differential Effects on Self-Confidence and Activation

    ERIC Educational Resources Information Center

    Badami, Rokhsareh; VaezMousavi, Mohammad; Wulf, Gabriele; Namazizadeh, Mahdi

    2012-01-01

    One purpose of the present study was to examine whether self-confidence or anxiety would be differentially affected by feedback from more accurate rather than less accurate trials. The second purpose was to determine whether arousal variations (activation) would predict performance. On Day 1, participants performed a golf putting task under one of…

  11. Exploring a new quantitative image marker to assess benefit of chemotherapy to ovarian cancer patients

    NASA Astrophysics Data System (ADS)

    Mirniaharikandehei, Seyedehnafiseh; Patil, Omkar; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

    2017-03-01

    Accurately assessing the potential benefit of chemotherapy to cancer patients is an important prerequisite to developing precision medicine in cancer treatment. The previous study has shown that total psoas area (TPA) measured on preoperative cross-section CT image might be a good image marker to predict long-term outcome of pancreatic cancer patients after surgery. However, accurate and automated segmentation of TPA from the CT image is difficult due to the fuzzy boundary or connection of TPA to other muscle areas. In this study, we developed a new interactive computer-aided detection (ICAD) scheme aiming to segment TPA from the abdominal CT images more accurately and assess the feasibility of using this new quantitative image marker to predict the benefit of ovarian cancer patients receiving Bevacizumab-based chemotherapy. ICAD scheme was applied to identify a CT image slice of interest, which is located at the level of L3 (vertebral spines). The cross-sections of the right and left TPA are segmented using a set of adaptively adjusted boundary conditions. TPA is then quantitatively measured. In addition, recent studies have investigated that muscle radiation attenuation which reflects fat deposition in the tissue might be a good image feature for predicting the survival rate of cancer patients. The scheme and TPA measurement task were applied to a large national clinical trial database involving 1,247 ovarian cancer patients. By comparing with manual segmentation results, we found that ICAD scheme could yield higher accuracy and consistency for this task. Using a new ICAD scheme can provide clinical researchers a useful tool to more efficiently and accurately extract TPA as well as muscle radiation attenuation as new image makers, and allow them to investigate the discriminatory power of it to predict progression-free survival and/or overall survival of the cancer patients before and after taking chemotherapy.

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

    PubMed

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

    2018-04-01

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

  13. Nonesterified fatty acid determination for functional lipidomics: comprehensive ultrahigh performance liquid chromatography-tandem mass spectrometry quantitation, qualification, and parameter prediction.

    PubMed

    Hellmuth, Christian; Weber, Martina; Koletzko, Berthold; Peissner, Wolfgang

    2012-02-07

    Despite their central importance for lipid metabolism, straightforward quantitative methods for determination of nonesterified fatty acid (NEFA) species are still missing. The protocol presented here provides unbiased quantitation of plasma NEFA species by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Simple deproteination of plasma in organic solvent solution yields high accuracy, including both the unbound and initially protein-bound fractions, while avoiding interferences from hydrolysis of esterified fatty acids from other lipid classes. Sample preparation is fast and nonexpensive, hence well suited for automation and high-throughput applications. Separation of isotopologic NEFA is achieved using ultrahigh-performance liquid chromatography (UPLC) coupled to triple quadrupole LC-MS/MS detection. In combination with automated liquid handling, total assay time per sample is less than 15 min. The analytical spectrum extends beyond readily available NEFA standard compounds by a regression model predicting all the relevant analytical parameters (retention time, ion path settings, and response factor) of NEFA species based on chain length and number of double bonds. Detection of 50 NEFA species and accurate quantification of 36 NEFA species in human plasma is described, the highest numbers ever reported for a LC-MS application. Accuracy and precision are within widely accepted limits. The use of qualifier ions supports unequivocal analyte verification. © 2012 American Chemical Society

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

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

  15. A Novel Quantitative Prediction Approach for Astringency Level of Herbs Based on an Electronic Tongue

    PubMed Central

    Han, Xue; Jiang, Hong; Zhang, Dingkun; Zhang, Yingying; Xiong, Xi; Jiao, Jiaojiao; Xu, Runchun; Yang, Ming; Han, Li; Lin, Junzhi

    2017-01-01

    Background: The current astringency evaluation for herbs has become dissatisfied with the requirement of pharmaceutical process. It needed a new method to accurately assess astringency. Methods: First, quinine, sucrose, citric acid, sodium chloride, monosodium glutamate, and tannic acid (TA) were analyzed by electronic tongue (e-tongue) to determine the approximate region of astringency in partial least square (PLS) map. Second, different concentrations of TA were detected to define the standard curve of astringency. Meanwhile, coordinate-concentration relationship could be obtained by fitting the PLS abscissa of standard curve and corresponding concentration. Third, Chebulae Fructus (CF), Yuganzi throat tablets (YGZTT), and Sanlejiang oral liquid (SLJOL) were tested to define the region in PLS map. Finally, the astringent intensities of samples were calculated combining with the standard coordinate-concentration relationship and expressed by concentrations of TA. Then, Euclidean distance (Ed) analysis and human sensory test were processed to verify the results. Results: The fitting equation between concentration and abscissa of TA was Y = 0.00498 × e(−X/0.51035) + 0.10905 (r = 0.999). The astringency of 1, 0.1 mg/mL CF was predicted at 0.28, 0.12 mg/mL TA; 2, 0.2 mg/mL YGZTTs was predicted at 0.18, 0.11 mg/mL TA; 0.002, 0.0002 mg/mL SLJOL was predicted at 0.15, 0.10 mg/mL TA. The validation results showed that the predicted astringency of e-tongue was basically consistent to human sensory and was more accuracy than Ed analysis. Conclusion: The study indicated the established method was objective and feasible. It provided a new quantitative method for astringency of herbs. SUMMARY The astringency of Chebulae Fructus, Yuganzi throat tablets, and Sanlejiang oral liquid was predicted by electronic tongueEuclidean distance analysis and human sensory test verified the resultsA new strategy which was objective, simple, and sensitive to compare astringent intensity of

  16. Accurate prediction of vaccine stability under real storage conditions and during temperature excursions.

    PubMed

    Clénet, Didier

    2018-04-01

    Due to their thermosensitivity, most vaccines must be kept refrigerated from production to use. To successfully carry out global immunization programs, ensuring the stability of vaccines is crucial. In this context, two important issues are critical, namely: (i) predicting vaccine stability and (ii) preventing product damage due to excessive temperature excursions outside of the recommended storage conditions (cold chain break). We applied a combination of advanced kinetics and statistical analyses on vaccine forced degradation data to accurately describe the loss of antigenicity for a multivalent freeze-dried inactivated virus vaccine containing three variants. The screening of large amounts of kinetic models combined with a statistical model selection approach resulted in the identification of two-step kinetic models. Predictions based on kinetic analysis and experimental stability data were in agreement, with approximately five percentage points difference from real values for long-term stability storage conditions, after excursions of temperature and during experimental shipments of freeze-dried products. Results showed that modeling a few months of forced degradation can be used to predict various time and temperature profiles endured by vaccines, i.e. long-term stability, short time excursions outside the labeled storage conditions or shipments at ambient temperature, with high accuracy. Pharmaceutical applications of the presented kinetics-based approach are discussed. Copyright © 2018 The Author. Published by Elsevier B.V. All rights reserved.

  17. Communication—Quantitative Voltammetric Analysis of High Concentration Actinides in Molten Salts

    DOE PAGES

    Hoyt, Nathaniel C.; Willit, James L.; Williamson, Mark A.

    2017-01-18

    Previous electroanalytical studies have shown that cyclic voltammetry can provide accurate quantitative measurements of actinide concentrations at low weight loadings in molten salts. However, above 2 wt%, the techniques were found to underpredict the concentrations of the reactant species. Here this work will demonstrate that much of the discrepancy is caused by uncompensated resistance and cylindrical diffusion. An improved electroanalytical approach has therefore been developed using the results of digital simulations to take these effects into account. This approach allows for accurate electroanalytical predictions across the full range of weight loadings expected to be encountered in operational nuclear fuel processingmore » equipment.« less

  18. Communication—Quantitative Voltammetric Analysis of High Concentration Actinides in Molten Salts

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

    Hoyt, Nathaniel C.; Willit, James L.; Williamson, Mark A.

    Previous electroanalytical studies have shown that cyclic voltammetry can provide accurate quantitative measurements of actinide concentrations at low weight loadings in molten salts. However, above 2 wt%, the techniques were found to underpredict the concentrations of the reactant species. Here this work will demonstrate that much of the discrepancy is caused by uncompensated resistance and cylindrical diffusion. An improved electroanalytical approach has therefore been developed using the results of digital simulations to take these effects into account. This approach allows for accurate electroanalytical predictions across the full range of weight loadings expected to be encountered in operational nuclear fuel processingmore » equipment.« less

  19. Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs).

    PubMed Central

    Newman, M C; McCloskey, J T; Tatara, C P

    1998-01-01

    Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful. PMID:9860900

  20. Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

    NASA Astrophysics Data System (ADS)

    Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

    2016-03-01

    How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

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

    DOE PAGES

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.; ...

    2017-06-05

    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,more » 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. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

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

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

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.

    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,more » 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. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  3. Prediction of Coronal Mass Ejections from Vector Magnetograms: Quantitative Measures as Predictors

    NASA Astrophysics Data System (ADS)

    Falconer, D. A.; Moore, R. L.; Gary, G. A.

    2001-05-01

    In a pilot study of 4 active regions (Falconer, D.A. 2001, JGR, in press), we derived two quantitative measures of an active region's global nonpotentiality from the region's vector magnetogram, 1) the net current (IN), and 2) the length of the strong-shear, strong-field main neutral line (LSS), and used these two measures of the CME productivity of the active regions. We compared the global nonpotentiality measures to the active regions' CME productivity determined from GOES and Yohkoh/SXT observations. We found that two of the active regions were highly globally nonpotential and were CME productive, while the other two active regions had little global nonpotentiality and produced no CMEs. At the Fall 2000 AGU (Falconer, Moore, & Gary, 2000, EOS 81, 48 F998), we reported on an expanded study (12 active regions and 17 magnetograms) in which we evaluated four quantitative global measures of an active region's magnetic field and compared these measures with the CME productivity. The four global measures (all derived from MSFC vector magnetograms) included our two previous measures (IN and LSS) as well as two new ones, the total magnetic flux (Φ ) (a measure of an active region's size), and the normalized twist (α =μ IN/Φ ). We found that the three measures of global nonpotentiality (IN, LSS, α ) were all well correlated (>99% confidence level) with an active region's CME productivity within (2 days of the day of the magnetogram. We will now report on our findings of how good our quantitative measures are as predictors of active-region CME productivity, using only CMEs that occurred after the magnetogram. We report the preliminary skill test of these quantitative measures as predictors. We compare the CME prediction success of our quantitative measures to the CME prediction success based on an active region's past CME productivity. We examine the cases of the handful of false positive and false negatives to look for improvements to our predictors. This work is

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

    PubMed

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

    2018-05-01

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

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

    PubMed

    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.

  6. Accurate Prediction of Severe Allergic Reactions by a Small Set of Environmental Parameters (NDVI, Temperature)

    PubMed Central

    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. PMID:25794106

  7. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    PubMed

    Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Akutsu, Tatsuya; Purcell, Anthony W; Smith, A Ian; Lithgow, Trevor; Daly, Roger J; Song, Jiangning; Chou, Kuo-Chen

    2018-06-27

    Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

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

    PubMed

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

    2016-12-01

    The combination of the qualitative fetal fibronectin test and cervical length measurement has a high negative predictive value for preterm birth within 7 days; however, positive prediction is poor. A new bedside quantitative fetal fibronectin test showed potential additional value over the conventional qualitative test, but there is limited evidence on the combination with cervical length measurement. The purpose of this study was to compare quantitative fetal fibronectin and qualitative fetal fibronectin testing in the prediction of spontaneous preterm birth within 7 days in symptomatic women who undergo cervical length measurement. We performed a European multicenter cohort study in 10 perinatal centers in 5 countries. Women between 24 and 34 weeks of gestation with signs of active labor and intact membranes underwent quantitative fibronectin testing and cervical length measurement. We assessed the risk of preterm birth within 7 days in predefined strata based on fibronectin concentration and cervical length. Of 455 women who were included in the study, 48 women (11%) delivered within 7 days. A combination of cervical length and qualitative fibronectin resulted in the identification of 246 women who were at low risk: 164 women with a cervix between 15 and 30 mm and a negative fibronectin test (<50 ng/mL; preterm birth rate, 2%) and 82 women with a cervix at >30 mm (preterm birth rate, 2%). Use of quantitative fibronectin alone resulted in a predicted risk of preterm birth within 7 days that ranged from 2% in the group with the lowest fibronectin level (<10 ng/mL) to 38% in the group with the highest fibronectin level (>500 ng/mL), with similar accuracy as that of the combination of cervical length and qualitative fibronectin. Combining cervical length and quantitative fibronectin resulted in the identification of an additional 19 women at low risk (preterm birth rate, 5%), using a threshold of 10 ng/mL in women with a cervix at <15 mm, and 6 women at high risk

  9. Quantitative characterization of surface topography using spectral analysis

    NASA Astrophysics Data System (ADS)

    Jacobs, Tevis D. B.; Junge, Till; Pastewka, Lars

    2017-03-01

    Roughness determines many functional properties of surfaces, such as adhesion, friction, and (thermal and electrical) contact conductance. Recent analytical models and simulations enable quantitative prediction of these properties from knowledge of the power spectral density (PSD) of the surface topography. The utility of the PSD is that it contains statistical information that is unbiased by the particular scan size and pixel resolution chosen by the researcher. In this article, we first review the mathematical definition of the PSD, including the one- and two-dimensional cases, and common variations of each. We then discuss strategies for reconstructing an accurate PSD of a surface using topography measurements at different size scales. Finally, we discuss detecting and mitigating artifacts at the smallest scales, and computing upper/lower bounds on functional properties obtained from models. We accompany our discussion with virtual measurements on computer-generated surfaces. This discussion summarizes how to analyze topography measurements to reconstruct a reliable PSD. Analytical models demonstrate the potential for tuning functional properties by rationally tailoring surface topography—however, this potential can only be achieved through the accurate, quantitative reconstruction of the PSDs of real-world surfaces.

  10. Quantitative computed tomography versus spirometry in predicting air leak duration after major lung resection for cancer.

    PubMed

    Ueda, Kazuhiro; Kaneda, Yoshikazu; Sudo, Manabu; Mitsutaka, Jinbo; Li, Tao-Sheng; Suga, Kazuyoshi; Tanaka, Nobuyuki; Hamano, Kimikazu

    2005-11-01

    Emphysema is a well-known risk factor for developing air leak or persistent air leak after pulmonary resection. Although quantitative computed tomography (CT) and spirometry are used to diagnose emphysema, it remains controversial whether these tests are predictive of the duration of postoperative air leak. Sixty-two consecutive patients who were scheduled to undergo major lung resection for cancer were enrolled in this prospective study to define the best predictor of postoperative air leak duration. Preoperative factors analyzed included spirometric variables and area of emphysema (proportion of the low-attenuation area) that was quantified in a three-dimensional CT lung model. Chest tubes were removed the day after disappearance of the air leak, regardless of pleural drainage. Univariate and multivariate proportional hazards analyses were used to determine the influence of preoperative factors on chest tube time (air leak duration). By univariate analysis, site of resection (upper, lower), forced expiratory volume in 1 second, predicted postoperative forced expiratory volume in 1 second, and area of emphysema (< 1%, 1% to 10%, > 10%) were significant predictors of air leak duration. By multivariate analysis, site of resection and area of emphysema were the best independent determinants of air leak duration. The results were similar for patients with a smoking history (n = 40), but neither forced expiratory volume in 1 second nor predicted postoperative forced expiratory volume in 1 second were predictive of air leak duration. Quantitative CT is superior to spirometry in predicting air leak duration after major lung resection for cancer. Quantitative CT may aid in the identification of patients, particularly among those with a smoking history, requiring additional preventive procedures against air leak.

  11. Quantitative inference of population response properties across eccentricity from motion-induced maps in macaque V1

    PubMed Central

    Chen, Ming; Wu, Si; Lu, Haidong D.; Roe, Anna W.

    2013-01-01

    Interpreting population responses in the primary visual cortex (V1) remains a challenge especially with the advent of techniques measuring activations of large cortical areas simultaneously with high precision. For successful interpretation, a quantitatively precise model prediction is of great importance. In this study, we investigate how accurate a spatiotemporal filter (STF) model predicts average response profiles to coherently drifting random dot motion obtained by optical imaging of intrinsic signals in V1 of anesthetized macaques. We establish that orientation difference maps, obtained by subtracting orthogonal axis-of-motion, invert with increasing drift speeds, consistent with the motion streak effect. Consistent with perception, the speed at which the map inverts (the critical speed) depends on cortical eccentricity and systematically increases from foveal to parafoveal. We report that critical speeds and response maps to drifting motion are excellently reproduced by the STF model. Our study thus suggests that the STF model is quantitatively accurate enough to be used as a first model of choice for interpreting responses obtained with intrinsic imaging methods in V1. We show further that this good quantitative correspondence opens the possibility to infer otherwise not easily accessible population receptive field properties from responses to complex stimuli, such as drifting random dot motions. PMID:23197457

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

    PubMed

    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.

  13. Solar Cycle Predictions

    NASA Technical Reports Server (NTRS)

    Pesnell, William Dean

    2012-01-01

    Solar cycle predictions are needed to plan long-term space missions; just like weather predictions are needed to plan the launch. Fleets of satellites circle the Earth collecting many types of science data, protecting astronauts, and relaying information. All of these satellites are sensitive at some level to solar cycle effects. Predictions of drag on LEO spacecraft are one of the most important. Launching a satellite with less propellant can mean a higher orbit, but unanticipated solar activity and increased drag can make that a Pyrrhic victory as you consume the reduced propellant load more rapidly. Energetic events at the Sun can produce crippling radiation storms that endanger all assets in space. Solar cycle predictions also anticipate the shortwave emissions that cause degradation of solar panels. Testing solar dynamo theories by quantitative predictions of what will happen in 5-20 years is the next arena for solar cycle predictions. A summary and analysis of 75 predictions of the amplitude of the upcoming Solar Cycle 24 is presented. The current state of solar cycle predictions and some anticipations how those predictions could be made more accurate in the future will be discussed.

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

    PubMed

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

    2016-08-01

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

  15. SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

    PubMed Central

    2014-01-01

    Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

  16. Rigour in quantitative research.

    PubMed

    Claydon, Leica Sarah

    2015-07-22

    This article which forms part of the research series addresses scientific rigour in quantitative research. It explores the basis and use of quantitative research and the nature of scientific rigour. It examines how the reader may determine whether quantitative research results are accurate, the questions that should be asked to determine accuracy and the checklists that may be used in this process. Quantitative research has advantages in nursing, since it can provide numerical data to help answer questions encountered in everyday practice.

  17. Accurate FRET Measurements within Single Diffusing Biomolecules Using Alternating-Laser Excitation

    PubMed Central

    Lee, Nam Ki; Kapanidis, Achillefs N.; Wang, You; Michalet, Xavier; Mukhopadhyay, Jayanta; Ebright, Richard H.; Weiss, Shimon

    2005-01-01

    Fluorescence resonance energy transfer (FRET) between a donor (D) and an acceptor (A) at the single-molecule level currently provides qualitative information about distance, and quantitative information about kinetics of distance changes. Here, we used the sorting ability of confocal microscopy equipped with alternating-laser excitation (ALEX) to measure accurate FRET efficiencies and distances from single molecules, using corrections that account for cross-talk terms that contaminate the FRET-induced signal, and for differences in the detection efficiency and quantum yield of the probes. ALEX yields accurate FRET independent of instrumental factors, such as excitation intensity or detector alignment. Using DNA fragments, we showed that ALEX-based distances agree well with predictions from a cylindrical model of DNA; ALEX-based distances fit better to theory than distances obtained at the ensemble level. Distance measurements within transcription complexes agreed well with ensemble-FRET measurements, and with structural models based on ensemble-FRET and x-ray crystallography. ALEX can benefit structural analysis of biomolecules, especially when such molecules are inaccessible to conventional structural methods due to heterogeneity or transient nature. PMID:15653725

  18. Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

    PubMed Central

    2017-01-01

    Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr. PMID:28821014

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

    PubMed

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

    2017-08-01

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

  20. Quantitative Lymphoscintigraphy to Predict the Possibility of Lymphedema Development After Breast Cancer Surgery: Retrospective Clinical Study.

    PubMed

    Kim, Paul; Lee, Ju Kang; Lim, Oh Kyung; Park, Heung Kyu; Park, Ki Deok

    2017-12-01

    To predict the probability of lymphedema development in breast cancer patients in the early post-operation stage, we investigated the ability of quantitative lymphoscintigraphic assessment. This retrospective study included 201 patients without lymphedema after unilateral breast cancer surgery. Lymphoscintigraphy was performed between 4 and 8 weeks after surgery to evaluate the lymphatic system in the early postoperative stage. Quantitative lymphoscintigraphy was performed using four methods: ratio of radiopharmaceutical clearance rate of the affected to normal hand; ratio of radioactivity of the affected to normal hand; ratio of radiopharmaceutical uptake rate of the affected to normal axilla (RUA); and ratio of radioactivity of the affected to normal axilla (RRA). During a 1-year follow-up, patients with a circumferential interlimb difference of 2 cm at any measurement location and a 200-mL interlimb volume difference were diagnosed with lymphedema. We investigated the difference in quantitative lymphoscintigraphic assessment between the non-lymphedema and lymphedema groups. Quantitative lymphoscintigraphic assessment revealed that the RUA and RRA were significantly lower in the lymphedema group than in the non-lymphedema group. After adjusting the model for all significant variables (body mass index, N-stage, T-stage, type of surgery, and type of lymph node surgery), RRA was associated with lymphedema (odds ratio=0.14; 95% confidence interval, 0.04-0.46; p=0.001). In patients in the early postoperative stage after unilateral breast cancer surgery, quantitative lymphoscintigraphic assessment can be used to predict the probability of developing lymphedema.

  1. A quantitative test of population genetics using spatiogenetic patterns in bacterial colonies.

    PubMed

    Korolev, Kirill S; Xavier, João B; Nelson, David R; Foster, Kevin R

    2011-10-01

    It is widely accepted that population-genetics theory is the cornerstone of evolutionary analyses. Empirical tests of the theory, however, are challenging because of the complex relationships between space, dispersal, and evolution. Critically, we lack quantitative validation of the spatial models of population genetics. Here we combine analytics, on- and off-lattice simulations, and experiments with bacteria to perform quantitative tests of the theory. We study two bacterial species, the gut microbe Escherichia coli and the opportunistic pathogen Pseudomonas aeruginosa, and show that spatiogenetic patterns in colony biofilms of both species are accurately described by an extension of the one-dimensional stepping-stone model. We use one empirical measure, genetic diversity at the colony periphery, to parameterize our models and show that we can then accurately predict another key variable: the degree of short-range cell migration along an edge. Moreover, the model allows us to estimate other key parameters, including effective population size (density) at the expansion frontier. While our experimental system is a simplification of natural microbial community, we argue that it constitutes proof of principle that the spatial models of population genetics can quantitatively capture organismal evolution.

  2. Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada

    NASA Astrophysics Data System (ADS)

    Maurya, S. P.; Singh, K. H.; Singh, N. P.

    2018-05-01

    In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region.

  3. Further evaluation of quantitative structure--activity relationship models for the prediction of the skin sensitization potency of selected fragrance allergens.

    PubMed

    Patlewicz, Grace Y; Basketter, David A; Pease, Camilla K Smith; Wilson, Karen; Wright, Zoe M; Roberts, David W; Bernard, Guillaume; Arnau, Elena Giménez; Lepoittevin, Jean-Pierre

    2004-02-01

    Fragrance substances represent a very diverse group of chemicals; a proportion of them are associated with the ability to cause allergic reactions in the skin. Efforts to find substitute materials are hindered by the need to undertake animal testing for determining both skin sensitization hazard and potency. One strategy to avoid such testing is through an understanding of the relationships between chemical structure and skin sensitization, so-called structure-activity relationships. In recent work, we evaluated 2 groups of fragrance chemicals -- saturated aldehydes and alpha,beta-unsaturated aldehydes. Simple quantitative structure-activity relationship (QSAR) models relating the EC3 values [derived from the local lymph node assay (LLNA)] to physicochemical properties were developed for both sets of aldehydes. In the current study, we evaluated an additional group of carbonyl-containing compounds to test the predictive power of the developed QSARs and to extend their scope. The QSAR models were used to predict EC3 values of 10 newly selected compounds. Local lymph node assay data generated for these compounds demonstrated that the original QSARs were fairly accurate, but still required improvement. Development of these QSAR models has provided us with a better understanding of the potential mechanisms of action for aldehydes, and hence how to avoid or limit allergy. Knowledge generated from this work is being incorporated into new/improved rules for sensitization in the expert toxicity prediction system, deductive estimation of risk from existing knowledge (DEREK).

  4. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    PubMed

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

    PubMed

    Wen, Ping-Ping; Shi, Shao-Ping; Xu, Hao-Dong; Wang, Li-Na; Qiu, Jian-Ding

    2016-10-15

    As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights

  6. Post-anoxic quantitative MRI changes may predict emergence from coma and functional outcomes at discharge.

    PubMed

    Reynolds, Alexandra S; Guo, Xiaotao; Matthews, Elizabeth; Brodie, Daniel; Rabbani, Leroy E; Roh, David J; Park, Soojin; Claassen, Jan; Elkind, Mitchell S V; Zhao, Binsheng; Agarwal, Sachin

    2017-08-01

    Traditional predictors of neurological prognosis after cardiac arrest are unreliable after targeted temperature management. Absence of pupillary reflexes remains a reliable predictor of poor outcome. Diffusion-weighted imaging has emerged as a potential predictor of recovery, and here we compare imaging characteristics to pupillary exam. We identified 69 patients who had MRIs within seven days of arrest and used a semi-automated algorithm to perform quantitative volumetric analysis of apparent diffusion coefficient (ADC) sequences at various thresholds. Area under receiver operating characteristic curves (ROC-AUC) were estimated to compare predictive values of quantitative MRI with pupillary exam at days 3, 5 and 7 post-arrest, for persistence of coma and functional outcomes at discharge. Cerebral Performance Category scores of 3-4 were considered poor outcome. Excluding patients where life support was withdrawn, ≥2.8% diffusion restriction of the entire brain at an ADC of ≤650×10 -6 m 2 /s was 100% specific and 68% sensitive for failure to wake up from coma before discharge. The ROC-AUC of ADC changes at ≤450×10 -6 mm 2 /s and ≤650×10 -6 mm 2 /s were significantly superior in predicting failure to wake up from coma compared to bilateral absence of pupillary reflexes. Among survivors, >0.01% of diffusion restriction of the entire brain at an ADC ≤450×10 -6 m 2 /s was 100% specific and 46% sensitive for poor functional outcome at discharge. The ROC curve predicting poor functional outcome at ADC ≤450×10 -6 mm 2 /s had an AUC of 0.737 (0.574-0.899, p=0.04). Post-anoxic diffusion changes using quantitative brain MRI may aid in predicting persistent coma and poor functional outcomes at hospital discharge. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Method for accurate quantitation of background tissue optical properties in the presence of emission from a strong fluorescence marker

    NASA Astrophysics Data System (ADS)

    Bravo, Jaime; Davis, Scott C.; Roberts, David W.; Paulsen, Keith D.; Kanick, Stephen C.

    2015-03-01

    Quantification of targeted fluorescence markers during neurosurgery has the potential to improve and standardize surgical distinction between normal and cancerous tissues. However, quantitative analysis of marker fluorescence is complicated by tissue background absorption and scattering properties. Correction algorithms that transform raw fluorescence intensity into quantitative units, independent of absorption and scattering, require a paired measurement of localized white light reflectance to provide estimates of the optical properties. This study focuses on the unique problem of developing a spectral analysis algorithm to extract tissue absorption and scattering properties from white light spectra that contain contributions from both elastically scattered photons and fluorescence emission from a strong fluorophore (i.e. fluorescein). A fiber-optic reflectance device was used to perform measurements in a small set of optical phantoms, constructed with Intralipid (1% lipid), whole blood (1% volume fraction) and fluorescein (0.16-10 μg/mL). Results show that the novel spectral analysis algorithm yields accurate estimates of tissue parameters independent of fluorescein concentration, with relative errors of blood volume fraction, blood oxygenation fraction (BOF), and the reduced scattering coefficient (at 521 nm) of <7%, <1%, and <22%, respectively. These data represent a first step towards quantification of fluorescein in tissue in vivo.

  8. Demonstration of a viable quantitative theory for interplanetary type II radio bursts

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

    Schmidt, J. M., E-mail: jschmidt@physics.usyd.edu.au; Cairns, Iver H.

    Between 29 November and 1 December 2013 the two widely separated spacecraft STEREO A and B observed a long lasting, intermittent, type II radio burst for the extended frequency range ≈ 4 MHz to 30 kHz, including an intensification when the shock wave of the associated coronal mass ejection (CME) reached STEREO A. We demonstrate for the first time our ability to quantitatively and accurately simulate the fundamental (F) and harmonic (H) emission of type II bursts from the higher corona (near 11 solar radii) to 1 AU. Our modeling requires the combination of data-driven three-dimensional magnetohydrodynamic simulations for the CME andmore » plasma background, carried out with the BATS-R-US code, with an analytic quantitative kinetic model for both F and H radio emission, including the electron reflection at the shock, growth of Langmuir waves and radio waves, and the radiations propagation to an arbitrary observer. The intensities and frequencies of the observed radio emissions vary hugely by factors ≈ 10{sup 6} and ≈ 10{sup 3}, respectively; the theoretical predictions are impressively accurate, being typically in error by less than a factor of 10 and 20 %, for both STEREO A and B. We also obtain accurate predictions for the timing and characteristics of the shock and local radio onsets at STEREO A, the lack of such onsets at STEREO B, and the z-component of the magnetic field at STEREO A ahead of the shock, and in the sheath. Very strong support is provided by these multiple agreements for the theory, the efficacy of the BATS-R-US code, and the vision of using type IIs and associated data-theory iterations to predict whether a CME will impact Earth’s magnetosphere and drive space weather events.« less

  9. Demonstration of a viable quantitative theory for interplanetary type II radio bursts

    NASA Astrophysics Data System (ADS)

    Schmidt, J. M.; Cairns, Iver H.

    2016-03-01

    Between 29 November and 1 December 2013 the two widely separated spacecraft STEREO A and B observed a long lasting, intermittent, type II radio burst for the extended frequency range ≈ 4 MHz to 30 kHz, including an intensification when the shock wave of the associated coronal mass ejection (CME) reached STEREO A. We demonstrate for the first time our ability to quantitatively and accurately simulate the fundamental (F) and harmonic (H) emission of type II bursts from the higher corona (near 11 solar radii) to 1 AU. Our modeling requires the combination of data-driven three-dimensional magnetohydrodynamic simulations for the CME and plasma background, carried out with the BATS-R-US code, with an analytic quantitative kinetic model for both F and H radio emission, including the electron reflection at the shock, growth of Langmuir waves and radio waves, and the radiations propagation to an arbitrary observer. The intensities and frequencies of the observed radio emissions vary hugely by factors ≈ 106 and ≈ 103, respectively; the theoretical predictions are impressively accurate, being typically in error by less than a factor of 10 and 20 %, for both STEREO A and B. We also obtain accurate predictions for the timing and characteristics of the shock and local radio onsets at STEREO A, the lack of such onsets at STEREO B, and the z-component of the magnetic field at STEREO A ahead of the shock, and in the sheath. Very strong support is provided by these multiple agreements for the theory, the efficacy of the BATS-R-US code, and the vision of using type IIs and associated data-theory iterations to predict whether a CME will impact Earth's magnetosphere and drive space weather events.

  10. NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

    PubMed

    Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

    2013-01-01

    Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

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

    PubMed

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

    2016-05-07

    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 H2 (+). 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

  12. Does mesenteric venous imaging assessment accurately predict pathologic invasion in localized pancreatic ductal adenocarcinoma?

    PubMed

    Clanton, Jesse; Oh, Stephen; Kaplan, Stephen J; Johnson, Emily; Ross, Andrew; Kozarek, Richard; Alseidi, Adnan; Biehl, Thomas; Picozzi, Vincent J; Helton, William S; Coy, David; Dorer, Russell; Rocha, Flavio G

    2018-05-09

    Accurate prediction of mesenteric venous involvement in pancreatic ductal adenocarcinoma (PDAC) is necessary for adequate staging and treatment. A retrospective cohort study was conducted in PDAC patients at a single institution. All patients with resected PDAC and staging CT and EUS between 2003 and 2014 were included and sub-divided into "upfront resected" and "neoadjuvant chemotherapy (NAC)" groups. Independent imaging re-review was correlated to venous resection and venous invasion. Sensitivity, specificity, positive and negative predictive values were then calculated. A total of 109 patients underwent analysis, 60 received upfront resection, and 49 NAC. Venous resection (30%) and vein invasion (13%) was less common in patients resected upfront than those who received NAC (53% and 16%, respectively). Both CT and EUS had poor sensitivity (14-44%) but high specificity (75-95%) for detecting venous resection and vein invasion in patients resected upfront, whereas sensitivity was high (84-100%) and specificity was low (27-44%) after NAC. Preoperative CT and EUS in PDAC have similar efficacy but different predictive capacity in assessing mesenteric venous involvement depending on whether patients are resected upfront or received NAC. Both modalities appear to significantly overestimate true vascular involvement and should be interpreted in the appropriate clinical context. Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

  13. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    PubMed

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed Central

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

    2015-01-01

    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. PMID:26038575

  16. Quantitation of Staphylococcus aureus in Seawater Using CHROMagar™ SA

    PubMed Central

    Pombo, David; Hui, Jennifer; Kurano, Michelle; Bankowski, Matthew J; Seifried, Steven E

    2010-01-01

    A microbiological algorithm has been developed to analyze beach water samples for the determination of viable colony forming units (CFU) of Staphylococcus aureus (S. aureus). Membrane filtration enumeration of S. aureus from recreational beach waters using the chromogenic media CHROMagar™SA alone yields a positive predictive value (PPV) of 70%. Presumptive CHROMagar™SA colonies were confirmed as S. aureus by 24-hour tube coagulase test. Combined, these two tests yield a PPV of 100%. This algorithm enables accurate quantitation of S. aureus in seawater in 72 hours and could support risk-prediction processes for recreational waters. A more rapid protocol, utilizing a 4-hour tube coagulase confirmatory test, enables a 48-hour turnaround time with a modest false negative rate of less than 10%. PMID:20222490

  17. Quantitative prediction of solvation free energy in octanol of organic compounds.

    PubMed

    Delgado, Eduardo J; Jaña, Gonzalo A

    2009-03-01

    The free energy of solvation, DeltaGS0, in octanol of organic compounds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a DeltaGS0 range from about -50 to 0 kJ.mol(-1). The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ.mol(-1), just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set.

  18. Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.

    PubMed

    Zou, Lingyun; Nan, Chonghan; Hu, Fuquan

    2013-12-15

    Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished. A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

  19. Inverse methods for 3D quantitative optical coherence elasticity imaging (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Dong, Li; Wijesinghe, Philip; Hugenberg, Nicholas; Sampson, David D.; Munro, Peter R. T.; Kennedy, Brendan F.; Oberai, Assad A.

    2017-02-01

    In elastography, quantitative elastograms are desirable as they are system and operator independent. Such quantification also facilitates more accurate diagnosis, longitudinal studies and studies performed across multiple sites. In optical elastography (compression, surface-wave or shear-wave), quantitative elastograms are typically obtained by assuming some form of homogeneity. This simplifies data processing at the expense of smearing sharp transitions in elastic properties, and/or introducing artifacts in these regions. Recently, we proposed an inverse problem-based approach to compression OCE that does not assume homogeneity, and overcomes the drawbacks described above. In this approach, the difference between the measured and predicted displacement field is minimized by seeking the optimal distribution of elastic parameters. The predicted displacements and recovered elastic parameters together satisfy the constraint of the equations of equilibrium. This approach, which has been applied in two spatial dimensions assuming plane strain, has yielded accurate material property distributions. Here, we describe the extension of the inverse problem approach to three dimensions. In addition to the advantage of visualizing elastic properties in three dimensions, this extension eliminates the plane strain assumption and is therefore closer to the true physical state. It does, however, incur greater computational costs. We address this challenge through a modified adjoint problem, spatially adaptive grid resolution, and three-dimensional decomposition techniques. Through these techniques the inverse problem is solved on a typical desktop machine within a wall clock time of 20 hours. We present the details of the method and quantitative elasticity images of phantoms and tissue samples.

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

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

    Ureña-López, L. Arturo; Gonzalez-Morales, Alma X., E-mail: lurena@ugto.mx, E-mail: alma.gonzalez@fisica.ugto.mx

    2016-07-01

    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. The new method makes use of appropriate angular variables that simplify the writing of the equations of motion, and which also show that the usual field variables play a secondary role in the cosmological dynamics. We apply the method to a scalar fieldmore » 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 confirmed that there exists a Jeans wavenumber k {sub J} , directly related to the suppression of linear perturbations at wavenumbers k > k {sub J} , and which is verified to be k {sub J} = a √ mH . 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 the prediction of cosmological observables from scalar field dark matter models.« less

  1. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding

    PubMed Central

    Nissley, Daniel A.; Sharma, Ajeet K.; Ahmed, Nabeel; Friedrich, Ulrike A.; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P.

    2016-01-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally—a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process. PMID:26887592

  2. An accurate method of extracting fat droplets in liver images for quantitative evaluation

    NASA Astrophysics Data System (ADS)

    Ishikawa, Masahiro; Kobayashi, Naoki; Komagata, Hideki; Shinoda, Kazuma; Yamaguchi, Masahiro; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2015-03-01

    The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.

  3. The value of assessing pulmonary venous flow velocity for predicting severity of mitral regurgitation: A quantitative assessment integrating left ventricular function

    NASA Technical Reports Server (NTRS)

    Pu, M.; Griffin, B. P.; Vandervoort, P. M.; Stewart, W. J.; Fan, X.; Cosgrove, D. M.; Thomas, J. D.

    1999-01-01

    Although alteration in pulmonary venous flow has been reported to relate to mitral regurgitant severity, it is also known to vary with left ventricular (LV) systolic and diastolic dysfunction. There are few data relating pulmonary venous flow to quantitative indexes of mitral regurgitation (MR). The object of this study was to assess quantitatively the accuracy of pulmonary venous flow for predicting MR severity by using transesophageal echocardiographic measurement in patients with variable LV dysfunction. This study consisted of 73 patients undergoing heart surgery with mild to severe MR. Regurgitant orifice area (ROA), regurgitant stroke volume (RSV), and regurgitant fraction (RF) were obtained by quantitative transesophageal echocardiography and proximal isovelocity surface area. Both left and right upper pulmonary venous flow velocities were recorded and their patterns classified by the ratio of systolic to diastolic velocity: normal (>/=1), blunted (<1), and systolic reversal (<0). Twenty-three percent of patients had discordant patterns between the left and right veins. When the most abnormal patterns either in the left or right vein were used for analysis, the ratio of peak systolic to diastolic flow velocity was negatively correlated with ROA (r = -0.74, P <.001), RSV (r = -0.70, P <.001), and RF (r = -0.66, P <.001) calculated by the Doppler thermodilution method; values were r = -0.70, r = -0.67, and r = -0.57, respectively (all P <.001), for indexes calculated by the proximal isovelocity surface area method. The sensitivity, specificity, and predictive values of the reversed pulmonary venous flow pattern for detecting a large ROA (>0.3 cm(2)) were 69%, 98%, and 97%, respectively. The sensitivity, specificity, and predictive values of the normal pulmonary venous flow pattern for detecting a small ROA (<0.3 cm(2)) were 60%, 96%, and 94%, respectively. However, the blunted pattern had low sensitivity (22%), specificity (61%), and predictive values (30

  4. A gene expression biomarker accurately predicts estrogen ...

    EPA Pesticide Factsheets

    The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

  5. Prediction of quantitative intrathoracic fluid volume to diagnose pulmonary oedema using LabVIEW.

    PubMed

    Urooj, Shabana; Khan, M; Ansari, A Q; Lay-Ekuakille, Aimé; Salhan, Ashok K

    2012-01-01

    Pulmonary oedema is a life-threatening disease that requires special attention in the area of research and clinical diagnosis. Computer-based techniques are rarely used to quantify the intrathoracic fluid volume (IFV) for diagnostic purposes. This paper discusses a software program developed to detect and diagnose pulmonary oedema using LabVIEW. The software runs on anthropometric dimensions and physiological parameters, mainly transthoracic electrical impedance (TEI). This technique is accurate and faster than existing manual techniques. The LabVIEW software was used to compute the parameters required to quantify IFV. An equation relating per cent control and IFV was obtained. The results of predicted TEI and measured TEI were compared with previously reported data to validate the developed program. It was found that the predicted values of TEI obtained from the computer-based technique were much closer to the measured values of TEI. Six new subjects were enrolled to measure and predict transthoracic impedance and hence to quantify IFV. A similar difference was also observed in the measured and predicted values of TEI for the new subjects.

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

    PubMed

    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/.

  7. A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis

    PubMed Central

    Noren, David P.; Long, Byron L.; Norel, Raquel; Rrhissorrakrai, Kahn; Hess, Kenneth; Hu, Chenyue Wendy; Bisberg, Alex J.; Schultz, Andre; Engquist, Erik; Liu, Li; Lin, Xihui; Chen, Gregory M.; Xie, Honglei; Hunter, Geoffrey A. M.; Norman, Thea; Friend, Stephen H.; Stolovitzky, Gustavo; Kornblau, Steven; Qutub, Amina A.

    2016-01-01

    Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response. PMID:27351836

  8. Quantitative Prediction of Solvation Free Energy in Octanol of Organic Compounds

    PubMed Central

    Delgado, Eduardo J.; Jaña, Gonzalo A.

    2009-01-01

    The free energy of solvation, ΔGS0, in octanol of organic compunds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a ΔGS0 range from about −50 to 0 kJ·mol−1. The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ·mol−1, just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set. PMID:19399236

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

    PubMed

    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.

  10. The Separation and Quantitation of Peptides with and without Oxidation of Methionine and Deamidation of Asparagine Using Hydrophilic Interaction Liquid Chromatography with Mass Spectrometry (HILIC-MS)

    NASA Astrophysics Data System (ADS)

    Badgett, Majors J.; Boyes, Barry; Orlando, Ron

    2017-05-01

    Peptides with deamidated asparagine residues and oxidized methionine residues are often not resolved sufficiently to allow quantitation of their native and modified forms using reversed phase (RP) chromatography. The accurate quantitation of these modifications is vital in protein biotherapeutic analysis because they can affect a protein's function, activity, and stability. We demonstrate here that hydrophilic interaction liquid chromatography (HILIC) adequately and predictably separates peptides with these modifications from their native counterparts. Furthermore, coefficients describing the extent of the hydrophilicity of these modifications have been derived and were incorporated into a previously made peptide retention prediction model that is capable of predicting the retention times of peptides with and without these modifications.

  11. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment.

    PubMed

    Young, Jonathan; Modat, Marc; Cardoso, Manuel J; Mendelson, Alex; Cash, Dave; Ourselin, Sebastien

    2013-01-01

    Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy

  12. A general method for bead-enhanced quantitation by flow cytometry

    PubMed Central

    Montes, Martin; Jaensson, Elin A.; Orozco, Aaron F.; Lewis, Dorothy E.; Corry, David B.

    2009-01-01

    Flow cytometry provides accurate relative cellular quantitation (percent abundance) of cells from diverse samples, but technical limitations of most flow cytometers preclude accurate absolute quantitation. Several quantitation standards are now commercially available which, when added to samples, permit absolute quantitation of CD4+ T cells. However, these reagents are limited by their cost, technical complexity, requirement for additional software and/or limited applicability. Moreover, few studies have validated the use of such reagents in complex biological samples, especially for quantitation of non-T cells. Here we show that addition to samples of known quantities of polystyrene fluorescence standardization beads permits accurate quantitation of CD4+ T cells from complex cell samples. This procedure, here termed single bead-enhanced cytofluorimetry (SBEC), was equally capable of enumerating eosinophils as well as subcellular fragments of apoptotic cells, moieties with very different optical and fluorescent characteristics. Relative to other proprietary products, SBEC is simple, inexpensive and requires no special software, suggesting that the method is suitable for the routine quantitation of most cells and other particles by flow cytometry. PMID:17067632

  13. Quantitative somatosensory testing of the penis: optimizing the clinical neurological examination.

    PubMed

    Bleustein, Clifford B; Eckholdt, Haftan; Arezzo, Joseph C; Melman, Arnold

    2003-06-01

    Quantitative somatosensory testing, including vibration, pressure, spatial perception and thermal thresholds of the penis, has demonstrated neuropathy in patients with a history of erectile dysfunction of all etiologies. We evaluated which measurement of neurological function of the penis was best at predicting erectile dysfunction and examined the impact of location on the penis for quantitative somatosensory testing measurements. A total of 107 patients were evaluated. All patients were required to complete the erectile function domain of the International Index of Erectile Function (IIEF) questionnaire, of whom 24 had no complaints of erectile dysfunction and scored within the "normal" range on the IIEF. Patients were subsequently tested on ventral middle penile shaft, proximal dorsal midline penile shaft and glans penis (with foreskin retracted) for vibration, pressure, spatial perception, and warm and cold thermal thresholds. Mixed models repeated measures analysis of variance controlling for age, diabetes and hypertension revealed that method of measurement (quantitative somatosensory testing) was predictive of IIEF score (F = 209, df = 4,1315, p <0.001), while site of measurement on the penis was not. To determine the best method of measurement, we used hierarchical regression, which revealed that warm temperature was the best predictor of erectile dysfunction with pseudo R(2) = 0.19, p <0.0007. There was no significant improvement in predicting erectile dysfunction when another test was added. Using 37C and greater as the warm thermal threshold yielded a sensitivity of 88.5%, specificity 70.0% and positive predictive value 85.5%. Quantitative somatosensory testing using warm thermal threshold measurements taken at the glans penis can be used alone to assess the neurological status of the penis. Warm thermal thresholds alone offer a quick, noninvasive accurate method of evaluating penile neuropathy in an office setting.

  14. A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

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

    St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.

    Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

  15. A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

    DOE PAGES

    St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.; ...

    2017-07-24

    Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

  16. Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster

    PubMed Central

    Edwards, Stefan M.; Sørensen, Izel F.; Sarup, Pernille; Mackay, Trudy F. C.; Sørensen, Peter

    2016-01-01

    Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits. PMID:27235308

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

    PubMed

    Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

    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 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.

  18. Towards more accurate vegetation mortality predictions

    DOE PAGES

    Sevanto, Sanna Annika; Xu, Chonggang

    2016-09-26

    Predicting the fate of vegetation under changing climate is one of the major challenges of the climate modeling community. Here, terrestrial vegetation dominates the carbon and water cycles over land areas, and dramatic changes in vegetation cover resulting from stressful environmental conditions such as drought feed directly back to local and regional climate, potentially leading to a vicious cycle where vegetation recovery after a disturbance is delayed or impossible.

  19. Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.

    PubMed

    Park, Soo Hyun; Talebi, Mohammad; Amos, Ruth I J; Tyteca, Eva; Haddad, Paul R; Szucs, Roman; Pohl, Christopher A; Dolan, John W

    2017-11-10

    Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Q ext(F2) 2 >0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min. Crown Copyright

  20. Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

    USGS Publications Warehouse

    Anderson, Ryan; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott M.; Morris, Richard V.; Ehlmann, Bethany L.; Dyar, M. Darby

    2017-01-01

    Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.

  1. A high order accurate finite element algorithm for high Reynolds number flow prediction

    NASA Technical Reports Server (NTRS)

    Baker, A. J.

    1978-01-01

    A Galerkin-weighted residuals formulation is employed to establish an implicit finite element solution algorithm for generally nonlinear initial-boundary value problems. Solution accuracy, and convergence rate with discretization refinement, are quantized in several error norms, by a systematic study of numerical solutions to several nonlinear parabolic and a hyperbolic partial differential equation characteristic of the equations governing fluid flows. Solutions are generated using selective linear, quadratic and cubic basis functions. Richardson extrapolation is employed to generate a higher-order accurate solution to facilitate isolation of truncation error in all norms. Extension of the mathematical theory underlying accuracy and convergence concepts for linear elliptic equations is predicted for equations characteristic of laminar and turbulent fluid flows at nonmodest Reynolds number. The nondiagonal initial-value matrix structure introduced by the finite element theory is determined intrinsic to improved solution accuracy and convergence. A factored Jacobian iteration algorithm is derived and evaluated to yield a consequential reduction in both computer storage and execution CPU requirements while retaining solution accuracy.

  2. A High Resolution/Accurate Mass (HRAM) Data-Dependent MS3 Neutral Loss Screening, Classification, and Relative Quantitation Methodology for Carbonyl Compounds in Saliva

    NASA Astrophysics Data System (ADS)

    Dator, Romel; Carrà, Andrea; Maertens, Laura; Guidolin, Valeria; Villalta, Peter W.; Balbo, Silvia

    2017-04-01

    Reactive carbonyl compounds (RCCs) are ubiquitous in the environment and are generated endogenously as a result of various physiological and pathological processes. These compounds can react with biological molecules inducing deleterious processes believed to be at the basis of their toxic effects. Several of these compounds are implicated in neurotoxic processes, aging disorders, and cancer. Therefore, a method characterizing exposures to these chemicals will provide insights into how they may influence overall health and contribute to disease pathogenesis. Here, we have developed a high resolution accurate mass (HRAM) screening strategy allowing simultaneous identification and relative quantitation of DNPH-derivatized carbonyls in human biological fluids. The screening strategy involves the diagnostic neutral loss of hydroxyl radical triggering MS3 fragmentation, which is only observed in positive ionization mode of DNPH-derivatized carbonyls. Unique fragmentation pathways were used to develop a classification scheme for characterizing known and unanticipated/unknown carbonyl compounds present in saliva. Furthermore, a relative quantitation strategy was implemented to assess variations in the levels of carbonyl compounds before and after exposure using deuterated d 3 -DNPH. This relative quantitation method was tested on human samples before and after exposure to specific amounts of alcohol. The nano-electrospray ionization (nano-ESI) in positive mode afforded excellent sensitivity with detection limits on-column in the high-attomole levels. To the best of our knowledge, this is the first report of a method using HRAM neutral loss screening of carbonyl compounds. In addition, the method allows simultaneous characterization and relative quantitation of DNPH-derivatized compounds using nano-ESI in positive mode.

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

    NASA Astrophysics Data System (ADS)

    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

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

    PubMed

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

    2016-01-01

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

  5. Evaluation of an ensemble of genetic models for prediction of a quantitative trait.

    PubMed

    Milton, Jacqueline N; Steinberg, Martin H; Sebastiani, Paola

    2014-01-01

    Many genetic markers have been shown to be associated with common quantitative traits in genome-wide association studies. Typically these associated genetic markers have small to modest effect sizes and individually they explain only a small amount of the variability of the phenotype. In order to build a genetic prediction model without fitting a multiple linear regression model with possibly hundreds of genetic markers as predictors, researchers often summarize the joint effect of risk alleles into a genetic score that is used as a covariate in the genetic prediction model. However, the prediction accuracy can be highly variable and selecting the optimal number of markers to be included in the genetic score is challenging. In this manuscript we present a strategy to build an ensemble of genetic prediction models from data and we show that the ensemble-based method makes the challenge of choosing the number of genetic markers more amenable. Using simulated data with varying heritability and number of genetic markers, we compare the predictive accuracy and inclusion of true positive and false positive markers of a single genetic prediction model and our proposed ensemble method. The results show that the ensemble of genetic models tends to include a larger number of genetic variants than a single genetic model and it is more likely to include all of the true genetic markers. This increased sensitivity is obtained at the price of a lower specificity that appears to minimally affect the predictive accuracy of the ensemble.

  6. Highly predictive and interpretable models for PAMPA permeability.

    PubMed

    Sun, Hongmao; Nguyen, Kimloan; Kerns, Edward; Yan, Zhengyin; Yu, Kyeong Ri; Shah, Pranav; Jadhav, Ajit; Xu, Xin

    2017-02-01

    Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

    PubMed

    Attiyeh, Marc A; Chakraborty, Jayasree; Doussot, Alexandre; Langdon-Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P; D'Angelica, Michael I; DeMatteo, Ronald P; Jarnagin, William R; Kingham, T Peter; Allen, Peter J; Simpson, Amber L; Do, Richard K

    2018-04-01

    Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

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

    PubMed

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

    2018-06-01

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

  9. The Separation and Quantitation of Peptides with and without Oxidation of Methionine and Deamidation of Asparagine Using Hydrophilic Interaction Liquid Chromatography with Mass Spectrometry (HILIC-MS).

    PubMed

    Badgett, Majors J; Boyes, Barry; Orlando, Ron

    2017-05-01

    Peptides with deamidated asparagine residues and oxidized methionine residues are often not resolved sufficiently to allow quantitation of their native and modified forms using reversed phase (RP) chromatography. The accurate quantitation of these modifications is vital in protein biotherapeutic analysis because they can affect a protein's function, activity, and stability. We demonstrate here that hydrophilic interaction liquid chromatography (HILIC) adequately and predictably separates peptides with these modifications from their native counterparts. Furthermore, coefficients describing the extent of the hydrophilicity of these modifications have been derived and were incorporated into a previously made peptide retention prediction model that is capable of predicting the retention times of peptides with and without these modifications. Graphical Abstract ᅟ.

  10. CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

    PubMed

    Testa, Alison C; Hane, James K; Ellwood, Simon R; Oliver, Richard P

    2015-03-11

    The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against

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

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

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

  12. A quantitative reconstruction software suite for SPECT imaging

    NASA Astrophysics Data System (ADS)

    Namías, Mauro; Jeraj, Robert

    2017-11-01

    Quantitative Single Photon Emission Tomography (SPECT) imaging allows for measurement of activity concentrations of a given radiotracer in vivo. Although SPECT has usually been perceived as non-quantitative by the medical community, the introduction of accurate CT based attenuation correction and scatter correction from hybrid SPECT/CT scanners has enabled SPECT systems to be as quantitative as Positron Emission Tomography (PET) systems. We implemented a software suite to reconstruct quantitative SPECT images from hybrid or dedicated SPECT systems with a separate CT scanner. Attenuation, scatter and collimator response corrections were included in an Ordered Subset Expectation Maximization (OSEM) algorithm. A novel scatter fraction estimation technique was introduced. The SPECT/CT system was calibrated with a cylindrical phantom and quantitative accuracy was assessed with an anthropomorphic phantom and a NEMA/IEC image quality phantom. Accurate activity measurements were achieved at an organ level. This software suite helps increasing quantitative accuracy of SPECT scanners.

  13. Assessment of umbilical artery flow and fetal heart rate to predict delivery time in bitches.

    PubMed

    Giannico, Amália Turner; Garcia, Daniela Aparecida Ayres; Gil, Elaine Mayumi Ueno; Sousa, Marlos Gonçalves; Froes, Tilde Rodrigues

    2016-10-15

    The aim of this study was to quantitatively investigate the oscillation of the fetal heart rate (HR) in advance of normal delivery and whether this index could be used to indicate impending delivery. In addition, fetal HR oscillation and umbilical artery resistive index (RI) were correlated to determine if the combination of these parameters provided a more accurate prediction of the time of delivery. Sonographic evaluation was performed in 11 pregnant bitches to evaluate the fetal HR and umbilical artery RI at the following antepartum times: 120 to 96 hours, 72 to 48 hours, 24 to 12 hours, and 12 to 1 hours. Statistical analysis indicated a correlation between the oscillation of fetal HR and the umbilical artery RI. As delivery approached a considerable reduction in the umbilical artery RI was documented and greater oscillations between maximum and minimum HRs occurred. We conclude that the quantitative analysis of fetal HR oscillations may be used to predict the time of delivery in bitches. The combination of fetal HR and umbilical artery RI together may provide more accurate predictions of time of delivery. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

    PubMed Central

    Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A.; Rusyn, Ivan; Tropsha, Alexander

    2009-01-01

    Background Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Objective A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. Methods and results A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD50 values from chemical descriptors. All models were extensively validated using special protocols. Conclusions The novelty of this modeling approach is that it uses the relationships

  15. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

    PubMed

    Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A; Rusyn, Ivan; Tropsha, Alexander

    2009-08-01

    Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols. The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only

  16. Predictive microbiology: Quantitative science delivering quantifiable benefits to the meat industry and other food industries.

    PubMed

    McMeekin, T A

    2007-09-01

    Predictive microbiology is considered in the context of the conference theme "chance, innovation and challenge", together with the impact of quantitative approaches on food microbiology, generally. The contents of four prominent texts on predictive microbiology are analysed and the major contributions of two meat microbiologists, Drs. T.A. Roberts and C.O. Gill, to the early development of predictive microbiology are highlighted. These provide a segue into R&D trends in predictive microbiology, including the Refrigeration Index, an example of science-based, outcome-focussed food safety regulation. Rapid advances in technologies and systems for application of predictive models are indicated and measures to judge the impact of predictive microbiology are suggested in terms of research outputs and outcomes. The penultimate section considers the future of predictive microbiology and advances that will become possible when data on population responses are combined with data derived from physiological and molecular studies in a systems biology approach. Whilst the emphasis is on science and technology for food safety management, it is suggested that decreases in foodborne illness will also arise from minimising human error by changing the food safety culture.

  17. Accurate prediction of cardiorespiratory fitness using cycle ergometry in minimally disabled persons with relapsing-remitting multiple sclerosis.

    PubMed

    Motl, Robert W; Fernhall, Bo

    2012-03-01

    To examine the accuracy of predicting peak oxygen consumption (VO(2peak)) primarily from peak work rate (WR(peak)) recorded during a maximal, incremental exercise test on a cycle ergometer among persons with relapsing-remitting multiple sclerosis (RRMS) who had minimal disability. Cross-sectional study. Clinical research laboratory. Women with RRMS (n=32) and sex-, age-, height-, and weight-matched healthy controls (n=16) completed an incremental exercise test on a cycle ergometer to volitional termination. Not applicable. Measured and predicted VO(2peak) and WR(peak). There were strong, statistically significant associations between measured and predicted VO(2peak) in the overall sample (R(2)=.89, standard error of the estimate=127.4 mL/min) and subsamples with (R(2)=.89, standard error of the estimate=131.3 mL/min) and without (R(2)=.85, standard error of the estimate=126.8 mL/min) multiple sclerosis (MS) based on the linear regression analyses. Based on the 95% confidence limits for worst-case errors, the equation predicted VO(2peak) within 10% of its true value in 95 of every 100 subjects with MS. Peak VO(2) can be accurately predicted in persons with RRMS who have minimal disability as it is in controls by using established equations and WR(peak) recorded from a maximal, incremental exercise test on a cycle ergometer. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  18. Quantitative prediction of perceptual decisions during near-threshold fear detection

    NASA Astrophysics Data System (ADS)

    Pessoa, Luiz; Padmala, Srikanth

    2005-04-01

    A fundamental goal of cognitive neuroscience is to explain how mental decisions originate from basic neural mechanisms. The goal of the present study was to investigate the neural correlates of perceptual decisions in the context of emotional perception. To probe this question, we investigated how fluctuations in functional MRI (fMRI) signals were correlated with behavioral choice during a near-threshold fear detection task. fMRI signals predicted behavioral choice independently of stimulus properties and task accuracy in a network of brain regions linked to emotional processing: posterior cingulate cortex, medial prefrontal cortex, right inferior frontal gyrus, and left insula. We quantified the link between fMRI signals and behavioral choice in a whole-brain analysis by determining choice probabilities by means of signal-detection theory methods. Our results demonstrate that voxel-wise fMRI signals can reliably predict behavioral choice in a quantitative fashion (choice probabilities ranged from 0.63 to 0.78) at levels comparable to neuronal data. We suggest that the conscious decision that a fearful face has been seen is represented across a network of interconnected brain regions that prepare the organism to appropriately handle emotionally challenging stimuli and that regulate the associated emotional response. decision making | emotion | functional MRI

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

    ERIC Educational Resources Information Center

    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…

  20. A predictive biophysical model of translational coupling to coordinate and control protein expression in bacterial operons

    PubMed Central

    Tian, Tian; Salis, Howard M.

    2015-01-01

    Natural and engineered genetic systems require the coordinated expression of proteins. In bacteria, translational coupling provides a genetically encoded mechanism to control expression level ratios within multi-cistronic operons. We have developed a sequence-to-function biophysical model of translational coupling to predict expression level ratios in natural operons and to design synthetic operons with desired expression level ratios. To quantitatively measure ribosome re-initiation rates, we designed and characterized 22 bi-cistronic operon variants with systematically modified intergenic distances and upstream translation rates. We then derived a thermodynamic free energy model to calculate de novo initiation rates as a result of ribosome-assisted unfolding of intergenic RNA structures. The complete biophysical model has only five free parameters, but was able to accurately predict downstream translation rates for 120 synthetic bi-cistronic and tri-cistronic operons with rationally designed intergenic regions and systematically increased upstream translation rates. The biophysical model also accurately predicted the translation rates of the nine protein atp operon, compared to ribosome profiling measurements. Altogether, the biophysical model quantitatively predicts how translational coupling controls protein expression levels in synthetic and natural bacterial operons, providing a deeper understanding of an important post-transcriptional regulatory mechanism and offering the ability to rationally engineer operons with desired behaviors. PMID:26117546

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

    NASA Astrophysics Data System (ADS)

    Xie, Tian; Grossman, Jeffrey C.

    2018-04-01

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

  2. Magnetic Resonance Imaging of Intracranial Hypotension: Diagnostic Value of Combined Qualitative Signs and Quantitative Metrics.

    PubMed

    Aslan, Kerim; Gunbey, Hediye Pinar; Tomak, Leman; Ozmen, Zafer; Incesu, Lutfi

    The aim of this study was to investigate whether the use of combination quantitative metrics (mamillopontine distance [MPD], pontomesencephalic angle, and mesencephalon anterior-posterior/medial-lateral diameter ratios) with qualitative signs (dural enhancement, subdural collections/hematoma, venous engorgement, pituitary gland enlargements, and tonsillar herniations) provides a more accurate diagnosis of intracranial hypotension (IH). The quantitative metrics and qualitative signs of 34 patients and 34 control subjects were assessed by 2 independent observers. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of quantitative metrics and qualitative signs, and for the diagnosis of IH, optimum cutoff values of quantitative metrics were found with ROC analysis. Combined ROC curve was measured for the quantitative metrics, and qualitative signs combinations in determining diagnostic accuracy and sensitivity, specificity, and positive and negative predictive values were found, and the best model combination was formed. Whereas MPD and pontomesencephalic angle were significantly lower in patients with IH when compared with the control group (P < 0.001), mesencephalon anterior-posterior/medial-lateral diameter ratio was significantly higher (P < 0.001). For qualitative signs, the highest individual distinctive power was dural enhancement with area under the ROC curve (AUC) of 0.838. For quantitative metrics, the highest individual distinctive power was MPD with AUC of 0.947. The best accuracy in the diagnosis of IH was obtained by combination of dural enhancement, venous engorgement, and MPD with an AUC of 1.00. This study showed that the combined use of dural enhancement, venous engorgement, and MPD had diagnostic accuracy of 100 % for the diagnosis of IH. Therefore, a more accurate IH diagnosis can be provided with combination of quantitative metrics with qualitative signs.

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

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

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » 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.« less

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

    DOE PAGES

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; ...

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » 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.« less

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

    PubMed

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

    2017-03-03

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

  6. Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

    NASA Astrophysics Data System (ADS)

    Costanzi, Stefano; Tikhonova, Irina G.; Harden, T. Kendall; Jacobson, Kenneth A.

    2009-11-01

    Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

  7. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    EPA Science Inventory

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  8. Accurate prediction of acute fish toxicity of fragrance chemicals with the RTgill-W1 cell assay.

    PubMed

    Natsch, Andreas; Laue, Heike; Haupt, Tina; von Niederhäusern, Valentin; Sanders, Gordon

    2018-03-01

    Testing for acute fish toxicity is an integral part of the environmental safety assessment of chemicals. A true replacement of primary fish tissue was recently proposed using cell viability in a fish gill cell line (RTgill-W1) as a means of predicting acute toxicity, showing good predictivity on 35 chemicals. To promote regulatory acceptance, the predictivity and applicability domain of novel tests need to be carefully evaluated on chemicals with existing high-quality in vivo data. We applied the RTgill-W1 cell assay to 38 fragrance chemicals with a wide range of both physicochemical properties and median lethal concentration (LC50) values and representing a diverse range of chemistries. A strong correlation (R 2  = 0.90-0.94) between the logarithmic in vivo LC50 values, based on fish mortality, and the logarithmic in vitro median effect concentration (EC50) values based on cell viability was observed. A leave-one-out analysis illustrates a median under-/overprediction from in vitro EC50 values to in vivo LC50 values by a factor of 1.5. This assay offers a simple, accurate, and reliable alternative to in vivo acute fish toxicity testing for chemicals, presumably acting mainly by a narcotic mode of action. Furthermore, the present study provides validation of the predictivity of the RTgill-W1 assay on a completely independent set of chemicals that had not been previously tested and indicates that fragrance chemicals are clearly within the applicability domain. Environ Toxicol Chem 2018;37:931-941. © 2017 SETAC. © 2017 SETAC.

  9. Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

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

    Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens

    We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

  10. Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

    DOE PAGES

    Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; ...

    2016-12-15

    We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

  11. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

    PubMed Central

    Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J.; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T.; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J.

    2017-01-01

    Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival. PMID:28401902

  12. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

    PubMed

    Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J

    2017-04-12

    Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

  13. Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

    PubMed

    Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

    2018-05-01

    Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

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

    PubMed Central

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

    2012-01-01

    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. PMID:23162788

  15. Quantitative aspects of inductively coupled plasma mass spectrometry

    NASA Astrophysics Data System (ADS)

    Bulska, Ewa; Wagner, Barbara

    2016-10-01

    Accurate determination of elements in various kinds of samples is essential for many areas, including environmental science, medicine, as well as industry. Inductively coupled plasma mass spectrometry (ICP-MS) is a powerful tool enabling multi-elemental analysis of numerous matrices with high sensitivity and good precision. Various calibration approaches can be used to perform accurate quantitative measurements by ICP-MS. They include the use of pure standards, matrix-matched standards, or relevant certified reference materials, assuring traceability of the reported results. This review critically evaluates the advantages and limitations of different calibration approaches, which are used in quantitative analyses by ICP-MS. Examples of such analyses are provided. This article is part of the themed issue 'Quantitative mass spectrometry'.

  16. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble.

    PubMed

    Zhang, Ming; Xu, Yan; Li, Lei; Liu, Zi; Yang, Xibei; Yu, Dong-Jun

    2018-06-01

    RNA 5-methylcytosine (m 5 C) is an important post-transcriptional modification that plays an indispensable role in biological processes. The accurate identification of m 5 C sites from primary RNA sequences is especially useful for deeply understanding the mechanisms and functions of m 5 C. Due to the difficulty and expensive costs of identifying m 5 C sites with wet-lab techniques, developing fast and accurate machine-learning-based prediction methods is urgently needed. In this study, we proposed a new m 5 C site predictor, called M5C-HPCR, by introducing a novel heuristic nucleotide physicochemical property reduction (HPCR) algorithm and classifier ensemble. HPCR extracts multiple reducts of physical-chemical properties for encoding discriminative features, while the classifier ensemble is applied to integrate multiple base predictors, each of which is trained based on a separate reduct of the physical-chemical properties obtained from HPCR. Rigorous jackknife tests on two benchmark datasets demonstrate that M5C-HPCR outperforms state-of-the-art m 5 C site predictors, with the highest values of MCC (0.859) and AUC (0.962). We also implemented the webserver of M5C-HPCR, which is freely available at http://cslab.just.edu.cn:8080/M5C-HPCR/. Copyright © 2018 Elsevier Inc. All rights reserved.

  17. Young children's core symbolic and nonsymbolic quantitative knowledge in the prediction of later mathematics achievement.

    PubMed

    Geary, David C; vanMarle, Kristy

    2016-12-01

    At the beginning of preschool (M = 46 months of age), 197 (94 boys) children were administered tasks that assessed a suite of nonsymbolic and symbolic quantitative competencies as well as their executive functions, verbal and nonverbal intelligence, preliteracy skills, and their parents' education level. The children's mathematics achievement was assessed at the end of preschool (M = 64 months). We used a series of Bayesian and standard regression analyses to winnow this broad set of competencies down to the core subset of quantitative skills that predict later mathematics achievement, controlling other factors. This knowledge included children's fluency in reciting the counting string, their understanding of the cardinal value of number words, and recognition of Arabic numerals, as well as their sensitivity to the relative quantity of 2 collections of objects. The results inform theoretical models of the foundations of children's early quantitative development and have practical implications for the design of early interventions for children at risk for poor long-term mathematics achievement. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  18. Predicting perturbation patterns from the topology of biological networks.

    PubMed

    Santolini, Marc; Barabási, Albert-László

    2018-06-20

    High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.

  19. Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

    PubMed

    Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

    2015-11-13

    The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science

  20. New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

    PubMed

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

    2004-11-21

    Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred).

  1. Early prediction of coma recovery after cardiac arrest with blinded pupillometry.

    PubMed

    Solari, Daria; Rossetti, Andrea O; Carteron, Laurent; Miroz, John-Paul; Novy, Jan; Eckert, Philippe; Oddo, Mauro

    2017-06-01

    Prognostication studies on comatose cardiac arrest (CA) patients are limited by lack of blinding, potentially causing overestimation of outcome predictors and self-fulfilling prophecy. Using a blinded approach, we analyzed the value of quantitative automated pupillometry to predict neurological recovery after CA. We examined a prospective cohort of 103 comatose adult patients who were unconscious 48 hours after CA and underwent repeated measurements of quantitative pupillary light reflex (PLR) using the Neurolight-Algiscan device. Clinical examination, electroencephalography (EEG), somatosensory evoked potentials (SSEP), and serum neuron-specific enolase were performed in parallel, as part of standard multimodal assessment. Automated pupillometry results were blinded to clinicians involved in patient care. Cerebral Performance Categories (CPC) at 1 year was the outcome endpoint. Survivors (n = 50 patients; 32 CPC 1, 16 CPC 2, 2 CPC 3) had higher quantitative PLR (median = 20 [range = 13-41] vs 11 [0-55] %, p < 0.0001) and constriction velocity (1.46 [0.85-4.63] vs 0.94 [0.16-4.97] mm/s, p < 0.0001) than nonsurvivors. At 48 hours, a quantitative PLR < 13% had 100% specificity and positive predictive value to predict poor recovery (0% false-positive rate), and provided equal performance to that of EEG and SSEP. Reduced quantitative PLR correlated with higher serum neuron-specific enolase (Spearman r = -0.52, p < 0.0001). Reduced quantitative PLR correlates with postanoxic brain injury and, when compared to standard multimodal assessment, is highly accurate in predicting long-term prognosis after CA. This is the first prognostication study to show the value of automated pupillometry using a blinded approach to minimize self-fulfilling prophecy. Ann Neurol 2017;81:804-810. © 2017 American Neurological Association.

  2. Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.

    PubMed

    Wu, J; Awate, S P; Licht, D J; Clouchoux, C; du Plessis, A J; Avants, B B; Vossough, A; Gee, J C; Limperopoulos, C

    2015-07-01

    Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates. © 2015 by American Journal of Neuroradiology.

  3. The NAFLD Index: A Simple and Accurate Screening Tool for the Prediction of Non-Alcoholic Fatty Liver Disease.

    PubMed

    Ichino, Naohiro; Osakabe, Keisuke; Sugimoto, Keiko; Suzuki, Koji; Yamada, Hiroya; Takai, Hiroji; Sugiyama, Hiroko; Yukitake, Jun; Inoue, Takashi; Ohashi, Koji; Hata, Tadayoshi; Hamajima, Nobuyuki; Nishikawa, Toru; Hashimoto, Senju; Kawabe, Naoto; Yoshioka, Kentaro

    2015-01-01

    Non-alcoholic fatty liver disease (NAFLD) is a common debilitating condition in many industrialized countries that increases the risk of cardiovascular disease. The aim of this study was to derive a simple and accurate screening tool for the prediction of NAFLD in the Japanese population. A total of 945 participants, 279 men and 666 women living in Hokkaido, Japan, were enrolled among residents who attended a health check-up program from 2010 to 2014. Participants with an alcohol consumption > 20 g/day and/or a chronic liver disease, such as chronic hepatitis B, chronic hepatitis C or autoimmune hepatitis, were excluded from this study. Clinical and laboratory data were examined to identify predictive markers of NAFLD. A new predictive index for NAFLD, the NAFLD index, was constructed for men and for women. The NAFLD index for men = -15.5693+0.3264 [BMI] +0.0134 [triglycerides (mg/dl)], and for women = -31.4686+0.3683 [BMI] +2.5699 [albumin (g/dl)] +4.6740[ALT/AST] -0.0379 [HDL cholesterol (mg/dl)]. The AUROC of the NAFLD index for men and for women was 0.87(95% CI 0.88-1.60) and 0.90 (95% CI 0.66-1.02), respectively. The cut-off point of -5.28 for men predicted NAFLD with an accuracy of 82.8%. For women, the cut-off point of -7.65 predicted NAFLD with an accuracy of 87.7%. A new index for the non-invasive prediction of NAFLD, the NAFLD index, was constructed using available clinical and laboratory data. This index is a simple screening tool to predict the presence of NAFLD.

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

    PubMed

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

    2018-06-01

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

  5. Impact of reconstruction parameters on quantitative I-131 SPECT

    NASA Astrophysics Data System (ADS)

    van Gils, C. A. J.; Beijst, C.; van Rooij, R.; de Jong, H. W. A. M.

    2016-07-01

    Radioiodine therapy using I-131 is widely used for treatment of thyroid disease or neuroendocrine tumors. Monitoring treatment by accurate dosimetry requires quantitative imaging. The high energy photons however render quantitative SPECT reconstruction challenging, potentially requiring accurate correction for scatter and collimator effects. The goal of this work is to assess the effectiveness of various correction methods on these effects using phantom studies. A SPECT/CT acquisition of the NEMA IEC body phantom was performed. Images were reconstructed using the following parameters: (1) without scatter correction, (2) with triple energy window (TEW) scatter correction and (3) with Monte Carlo-based scatter correction. For modelling the collimator-detector response (CDR), both (a) geometric Gaussian CDRs as well as (b) Monte Carlo simulated CDRs were compared. Quantitative accuracy, contrast to noise ratios and recovery coefficients were calculated, as well as the background variability and the residual count error in the lung insert. The Monte Carlo scatter corrected reconstruction method was shown to be intrinsically quantitative, requiring no experimentally acquired calibration factor. It resulted in a more accurate quantification of the background compartment activity density compared with TEW or no scatter correction. The quantification error relative to a dose calibrator derived measurement was found to be  <1%,-26% and 33%, respectively. The adverse effects of partial volume were significantly smaller with the Monte Carlo simulated CDR correction compared with geometric Gaussian or no CDR modelling. Scatter correction showed a small effect on quantification of small volumes. When using a weighting factor, TEW correction was comparable to Monte Carlo reconstruction in all measured parameters, although this approach is clinically impractical since this factor may be patient dependent. Monte Carlo based scatter correction including accurately simulated CDR

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

    PubMed

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

    2017-12-12

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

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

    NASA Technical Reports Server (NTRS)

    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.

  8. Prediction of trabecular bone qualitative properties using scanning quantitative ultrasound

    NASA Astrophysics Data System (ADS)

    Qin, Yi-Xian; Lin, Wei; Mittra, Erik; Xia, Yi; Cheng, Jiqi; Judex, Stefan; Rubin, Clint; Müller, Ralph

    2013-11-01

    Microgravity induced bone loss represents a critical health problem in astronauts, particularly occurred in weight-supporting skeleton, which leads to osteopenia and increase of fracture risk. Lack of suitable evaluation modality makes it difficult for monitoring skeletal status in long term space mission and increases potential risk of complication. Such disuse osteopenia and osteoporosis compromise trabecular bone density, and architectural and mechanical properties. While X-ray based imaging would not be practical in space, quantitative ultrasound may provide advantages to characterize bone density and strength through wave propagation in complex trabecular structure. This study used a scanning confocal acoustic diagnostic and navigation system (SCAN) to evaluate trabecular bone quality in 60 cubic trabecular samples harvested from adult sheep. Ultrasound image based SCAN measurements in structural and strength properties were validated by μCT and compressive mechanical testing. This result indicated a moderately strong negative correlations observed between broadband ultrasonic attenuation (BUA) and μCT-determined bone volume fraction (BV/TV, R2=0.53). Strong correlations were observed between ultrasound velocity (UV) and bone's mechanical strength and structural parameters, i.e., bulk Young's modulus (R2=0.67) and BV/TV (R2=0.85). The predictions for bone density and mechanical strength were significantly improved by using a linear combination of both BUA and UV, yielding R2=0.92 for BV/TV and R2=0.71 for bulk Young's modulus. These results imply that quantitative ultrasound can characterize trabecular structural and mechanical properties through measurements of particular ultrasound parameters, and potentially provide an excellent estimation for bone's structural integrity.

  9. Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships (MCBIOS)

    EPA Science Inventory

    Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships Jie Liu1,2, Richard Judson1, Matthew T. Martin1, Huixiao Hong3, Imran Shah1 1National Center for Computational Toxicology (NCCT), US EPA, RTP, NC...

  10. Multi-scale modeling of microstructure dependent intergranular brittle fracture using a quantitative phase-field based method

    DOE PAGES

    Chakraborty, Pritam; Zhang, Yongfeng; Tonks, Michael R.

    2015-12-07

    In this study, the fracture behavior of brittle materials is strongly influenced by their underlying microstructure that needs explicit consideration for accurate prediction of fracture properties and the associated scatter. In this work, a hierarchical multi-scale approach is pursued to model microstructure sensitive brittle fracture. A quantitative phase-field based fracture model is utilized to capture the complex crack growth behavior in the microstructure and the related parameters are calibrated from lower length scale atomistic simulations instead of engineering scale experimental data. The workability of this approach is demonstrated by performing porosity dependent intergranular fracture simulations in UO 2 and comparingmore » the predictions with experiments.« less

  11. Multi-scale modeling of microstructure dependent intergranular brittle fracture using a quantitative phase-field based method

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

    Chakraborty, Pritam; Zhang, Yongfeng; Tonks, Michael R.

    In this study, the fracture behavior of brittle materials is strongly influenced by their underlying microstructure that needs explicit consideration for accurate prediction of fracture properties and the associated scatter. In this work, a hierarchical multi-scale approach is pursued to model microstructure sensitive brittle fracture. A quantitative phase-field based fracture model is utilized to capture the complex crack growth behavior in the microstructure and the related parameters are calibrated from lower length scale atomistic simulations instead of engineering scale experimental data. The workability of this approach is demonstrated by performing porosity dependent intergranular fracture simulations in UO 2 and comparingmore » the predictions with experiments.« less

  12. Quantitative aspects of inductively coupled plasma mass spectrometry

    PubMed Central

    Wagner, Barbara

    2016-01-01

    Accurate determination of elements in various kinds of samples is essential for many areas, including environmental science, medicine, as well as industry. Inductively coupled plasma mass spectrometry (ICP-MS) is a powerful tool enabling multi-elemental analysis of numerous matrices with high sensitivity and good precision. Various calibration approaches can be used to perform accurate quantitative measurements by ICP-MS. They include the use of pure standards, matrix-matched standards, or relevant certified reference materials, assuring traceability of the reported results. This review critically evaluates the advantages and limitations of different calibration approaches, which are used in quantitative analyses by ICP-MS. Examples of such analyses are provided. This article is part of the themed issue ‘Quantitative mass spectrometry’. PMID:27644971

  13. New Equation for Prediction of Martensite Start Temperature in High Carbon Ferrous Alloys

    NASA Astrophysics Data System (ADS)

    Park, Jihye; Shim, Jae-Hyeok; Lee, Seok-Jae

    2018-02-01

    Since previous equations fail to predict M S temperature of high carbon ferrous alloys, we first propose an equation for prediction of M S temperature of ferrous alloys containing > 2 wt pct C. The presence of carbides (Fe3C and Cr-rich M 7C3) is thermodynamically considered to estimate the C concentration in austenite. Especially, equations individually specialized for lean and high Cr alloys very accurately reproduce experimental results. The chemical driving force for martensitic transformation is quantitatively analyzed based on the calculation of T 0 temperature.

  14. Benchmarking B-Cell Epitope Prediction with Quantitative Dose-Response Data on Antipeptide Antibodies: Towards Novel Pharmaceutical Product Development

    PubMed Central

    Caoili, Salvador Eugenio C.

    2014-01-01

    B-cell epitope prediction can enable novel pharmaceutical product development. However, a mechanistically framed consensus has yet to emerge on benchmarking such prediction, thus presenting an opportunity to establish standards of practice that circumvent epistemic inconsistencies of casting the epitope prediction task as a binary-classification problem. As an alternative to conventional dichotomous qualitative benchmark data, quantitative dose-response data on antibody-mediated biological effects are more meaningful from an information-theoretic perspective in the sense that such effects may be expressed as probabilities (e.g., of functional inhibition by antibody) for which the Shannon information entropy (SIE) can be evaluated as a measure of informativeness. Accordingly, half-maximal biological effects (e.g., at median inhibitory concentrations of antibody) correspond to maximally informative data while undetectable and maximal biological effects correspond to minimally informative data. This applies to benchmarking B-cell epitope prediction for the design of peptide-based immunogens that elicit antipeptide antibodies with functionally relevant cross-reactivity. Presently, the Immune Epitope Database (IEDB) contains relatively few quantitative dose-response data on such cross-reactivity. Only a small fraction of these IEDB data is maximally informative, and many more of them are minimally informative (i.e., with zero SIE). Nevertheless, the numerous qualitative data in IEDB suggest how to overcome the paucity of informative benchmark data. PMID:24949474

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

    PubMed

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

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t

  16. Towards more accurate and reliable predictions for nuclear applications

    NASA Astrophysics Data System (ADS)

    Goriely, Stephane; Hilaire, Stephane; Dubray, Noel; Lemaître, Jean-François

    2017-09-01

    The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. Nowadays mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenological inputs in the evaluation of nuclear data. The latest achievements to determine nuclear masses within the non-relativistic HFB approach, including the related uncertainties in the model predictions, are discussed. Similarly, recent efforts to determine fission observables within the mean-field approach are described and compared with more traditional existing models.

  17. Quantitative prediction of collision-induced vibration-rotation distributions from physical data

    NASA Astrophysics Data System (ADS)

    Marsh, Richard J.; McCaffery, Anthony J.

    2003-04-01

    We describe a rapid, accurate technique for computing state-to-state cross-sections in collision-induced vibration-rotation transfer (VRT) using only physical data, i.e. spectroscopic constants, bond length, mass and velocity distribution. The probability of linear-to-angular momentum (AM) conversion is calculated for a set of trajectories, each of which is subjected to energy conservation boundary conditions. No mechanism is specified for inducing vibrational state change. In the model, this constitutes a velocity or momentum barrier that must be overcome before rotational AM may be generated in the new vibrational state. The method is subjected to stringent testing by calculating state-to-state VRT probabilities for diatomics in highly excited vibrational, rotational and electronic states. Comparison is made to experimental data and to results from quantum mechanical and from quasi-classical trajectory calculations. There is quantitative agreement with data from all three sources, indicating that despite its simplicity the essential physics of collisions involving highly excited species is captured in the model. We develop further the concept of the molecular efficiency factor as an indicative parameter in collision dynamics, and derive an expression for ji > 0 and for VRT.

  18. Accurate electrical prediction of memory array through SEM-based edge-contour extraction using SPICE simulation

    NASA Astrophysics Data System (ADS)

    Shauly, Eitan; Rotstein, Israel; Peltinov, Ram; Latinski, Sergei; Adan, Ofer; Levi, Shimon; Menadeva, Ovadya

    2009-03-01

    The continues transistors scaling efforts, for smaller devices, similar (or larger) drive current/um and faster devices, increase the challenge to predict and to control the transistor off-state current. Typically, electrical simulators like SPICE, are using the design intent (as-drawn GDS data). At more sophisticated cases, the simulators are fed with the pattern after lithography and etch process simulations. As the importance of electrical simulation accuracy is increasing and leakage is becoming more dominant, there is a need to feed these simulators, with more accurate information extracted from physical on-silicon transistors. Our methodology to predict changes in device performances due to systematic lithography and etch effects was used in this paper. In general, the methodology consists on using the OPCCmaxTM for systematic Edge-Contour-Extraction (ECE) from transistors, taking along the manufacturing and includes any image distortions like line-end shortening, corner rounding and line-edge roughness. These measurements are used for SPICE modeling. Possible application of this new metrology is to provide a-head of time, physical and electrical statistical data improving time to market. In this work, we applied our methodology to analyze a small and large array's of 2.14um2 6T-SRAM, manufactured using Tower Standard Logic for General Purposes Platform. 4 out of the 6 transistors used "U-Shape AA", known to have higher variability. The predicted electrical performances of the transistors drive current and leakage current, in terms of nominal values and variability are presented. We also used the methodology to analyze an entire SRAM Block array. Study of an isolation leakage and variability are presented.

  19. A Novel Two-Step Hierarchial Quantitative Structure-Activity Relationship Modeling Workflow for Predicting Acute Toxicity of Chemicals in Rodents

    EPA Science Inventory

    Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database co...

  20. Predictive values of semi-quantitative procalcitonin test and common biomarkers for the clinical outcomes of community-acquired pneumonia.

    PubMed

    Ugajin, Motoi; Yamaki, Kenichi; Hirasawa, Natsuko; Yagi, Takeo

    2014-04-01

    The semi-quantitative serum procalcitonin test (Brahms PCT-Q) is available conveniently in clinical practice. However, there are few data on the relationship between results for this semi-quantitative procalcitonin test and clinical outcomes of community-acquired pneumonia (CAP). We investigated the usefulness of this procalcitonin test for predicting the clinical outcomes of CAP in comparison with severity scoring systems and the blood urea nitrogen/serum albumin (B/A) ratio, which has been reported to be a simple but reliable prognostic indicator in our prior CAP study. This retrospective study included data from subjects who were hospitalized for CAP from August 2010 through October 2012 and who were administered the semi-quantitative serum procalcitonin test on admission. The demographic characteristics; laboratory biomarkers; microbiological test results; Pneumonia Severity Index scores; confusion, urea nitrogen, breathing frequency, blood pressure, ≥ 65 years of age (CURB-65) scale scores; and age, dehydration, respiratory failure, orientation disturbance, pressure (A-DROP) scale scores on hospital admission were retrieved from their medical charts. The outcomes were mortality within 28 days of hospital admission and the need for intensive care. Of the 213 subjects with CAP who were enrolled in the study, 20 died within 28 days of hospital admission, and 32 required intensive care. Mortality did not differ significantly among subjects with different semi-quantitative serum procalcitonin levels; however, subjects with serum procalcitonin levels ≥ 10.0 ng/mL were more likely to require intensive care than those with lower levels (P < .001). The elevation of semi-quantitative serum procalcitonin levels was more frequently observed in subjects with proven etiology, especially pneumococcal pneumonia. Using the receiver operating characteristic curves for mortality, the area under the curve was 0.86 for Pneumonia Severity Index class, 0.81 for B/A ratio, 0

  1. Quantitative analysis and predictive engineering of self-rolling of nanomembranes under anisotropic mismatch strain

    NASA Astrophysics Data System (ADS)

    Chen, Cheng; Song, Pengfei; Meng, Fanchao; Li, Xiao; Liu, Xinyu; Song, Jun

    2017-12-01

    The present work presents a quantitative modeling framework for investigating the self-rolling of nanomembranes under different lattice mismatch strain anisotropy. The effect of transverse mismatch strain on the roll-up direction and curvature has been systematically studied employing both analytical modeling and numerical simulations. The bidirectional nature of the self-rolling of nanomembranes and the critical role of transverse strain in affecting the rolling behaviors have been demonstrated. Two fabrication strategies, i.e., third-layer deposition and corner geometry engineering, have been proposed to predictively manipulate the bidirectional rolling competition of strained nanomembranes, so as to achieve controlled, unidirectional roll-up. In particular for the strategy of corner engineering, microfabrication experiments have been performed to showcase its practical application and effectiveness. Our study offers new mechanistic knowledge towards understanding and predictive engineering of self-rolling of nanomembranes with improved roll-up yield.

  2. Estimating energy expenditure in vascular surgery patients: Are predictive equations accurate enough?

    PubMed

    Suen, J; Thomas, J M; Delaney, C L; Spark, J I; Miller, M D

    2016-12-01

    Malnutrition is prevalent in vascular surgical patients who commonly seek tertiary care at advanced stages of disease. Adjunct nutrition support is therefore pertinent to optimise patient outcomes. To negate consequences related to excessive or suboptimal dietary energy intake, it is essential to accurately determine energy expenditure and subsequent requirements. This study aims to compare resting energy expenditure (REE) measured by indirect calorimetry, a commonly used comparator, to REE estimated by predictive equations (Schofield, Harris-Benedict equations and Miller equation) to determine the most suitable equation for vascular surgery patients. Data were collected from four studies that measured REE in 77 vascular surgery patients. Bland-Altman analyses were conducted to explore agreement. Presence of fixed or proportional bias was assessed by linear regression analyses. In comparison to measured REE, on average REE was overestimated when Schofield (+857 kJ/day), Harris-Benedict (+801 kJ/day) and Miller (+71 kJ/day) equations were used. Wide limits of agreement led to an over or underestimation from 1552 to 1755 kJ. Proportional bias was absent in Schofield (R 2  = 0.005, p = 0.54) and Harris-Benedict equations (R 2  = 0.045, p = 0.06) but was present in the Miller equation (R 2  = 0.210, p < 0.01) even after logarithmic transformation (R 2  = 0.213, p < 0.01). Whilst the Miller equation tended to overestimate resting energy expenditure and was affected by proportional bias, the limits of agreement and mean bias were smaller compared to Schofield and Harris-Benedict equations. This suggested that it is the preferred predictive equation for vascular surgery patients. Future research to refine the Miller equation to improve its overall accuracy will better inform the provision of nutritional support for vascular surgery patients and subsequently improve outcomes. Alternatively, an equation might be developed specifically for use with

  3. A Pilot Comparative Study of Quantitative Ultrasound, Conventional Ultrasound, and MRI for Predicting Histology-Determined Steatosis Grade in Adult Nonalcoholic Fatty Liver Disease

    PubMed Central

    Paige, Jeremy S.; Bernstein, Gregory S.; Heba, Elhamy; Costa, Eduardo A. C.; Fereirra, Marilia; Wolfson, Tanya; Gamst, Anthony C.; Valasek, Mark A.; Lin, Grace Y.; Han, Aiguo; Erdman, John W.; O’Brien, William D.; Andre, Michael P.; Loomba, Rohit; Sirlin, Claude B.

    2017-01-01

    OBJECTIVE The purpose of this study is to explore the diagnostic performance of two investigational quantitative ultrasound (QUS) parameters, attenuation coefficient and backscatter coefficient, in comparison with conventional ultrasound (CUS) and MRI-estimated proton density fat fraction (PDFF) for predicting histology-confirmed steatosis grade in adults with nonalcoholic fatty liver disease (NAFLD). SUBJECTS AND METHODS In this prospectively designed pilot study, 61 adults with histology-confirmed NAFLD were enrolled from September 2012 to February 2014. Subjects underwent QUS, CUS, and MRI examinations within 100 days of clinical-care liver biopsy. QUS parameters (attenuation coefficient and backscatter coefficient) were estimated using a reference phantom technique by two analysts independently. Three-point ordinal CUS scores intended to predict steatosis grade (1, 2, or 3) were generated independently by two radiologists on the basis of QUS features. PDFF was estimated using an advanced chemical shift–based MRI technique. Using histologic examination as the reference standard, ROC analysis was performed. Optimal attenuation coefficient, backscatter coefficient, and PDFF cutoff thresholds were identified, and the accuracy of attenuation coefficient, backscatter coefficient, PDFF, and CUS to predict steatosis grade was determined. Interobserver agreement for attenuation coefficient, backscatter coefficient, and CUS was analyzed. RESULTS CUS had 51.7% grading accuracy. The raw and cross-validated steatosis grading accuracies were 61.7% and 55.0%, respectively, for attenuation coefficient, 68.3% and 68.3% for backscatter coefficient, and 76.7% and 71.3% for MRI-estimated PDFF. Interobserver agreements were 53.3% for CUS (κ = 0.61), 90.0% for attenuation coefficient (κ = 0.87), and 71.7% for backscatter coefficient (κ = 0.82) (p < 0.0001 for all). CONCLUSION Preliminary observations suggest that QUS parameters may be more accurate and provide higher

  4. A Pilot Comparative Study of Quantitative Ultrasound, Conventional Ultrasound, and MRI for Predicting Histology-Determined Steatosis Grade in Adult Nonalcoholic Fatty Liver Disease.

    PubMed

    Paige, Jeremy S; Bernstein, Gregory S; Heba, Elhamy; Costa, Eduardo A C; Fereirra, Marilia; Wolfson, Tanya; Gamst, Anthony C; Valasek, Mark A; Lin, Grace Y; Han, Aiguo; Erdman, John W; O'Brien, William D; Andre, Michael P; Loomba, Rohit; Sirlin, Claude B

    2017-05-01

    The purpose of this study is to explore the diagnostic performance of two investigational quantitative ultrasound (QUS) parameters, attenuation coefficient and backscatter coefficient, in comparison with conventional ultrasound (CUS) and MRI-estimated proton density fat fraction (PDFF) for predicting histology-confirmed steatosis grade in adults with nonalcoholic fatty liver disease (NAFLD). In this prospectively designed pilot study, 61 adults with histology-confirmed NAFLD were enrolled from September 2012 to February 2014. Subjects underwent QUS, CUS, and MRI examinations within 100 days of clinical-care liver biopsy. QUS parameters (attenuation coefficient and backscatter coefficient) were estimated using a reference phantom technique by two analysts independently. Three-point ordinal CUS scores intended to predict steatosis grade (1, 2, or 3) were generated independently by two radiologists on the basis of QUS features. PDFF was estimated using an advanced chemical shift-based MRI technique. Using histologic examination as the reference standard, ROC analysis was performed. Optimal attenuation coefficient, backscatter coefficient, and PDFF cutoff thresholds were identified, and the accuracy of attenuation coefficient, backscatter coefficient, PDFF, and CUS to predict steatosis grade was determined. Interobserver agreement for attenuation coefficient, backscatter coefficient, and CUS was analyzed. CUS had 51.7% grading accuracy. The raw and cross-validated steatosis grading accuracies were 61.7% and 55.0%, respectively, for attenuation coefficient, 68.3% and 68.3% for backscatter coefficient, and 76.7% and 71.3% for MRI-estimated PDFF. Interobserver agreements were 53.3% for CUS (κ = 0.61), 90.0% for attenuation coefficient (κ = 0.87), and 71.7% for backscatter coefficient (κ = 0.82) (p < 0.0001 for all). Preliminary observations suggest that QUS parameters may be more accurate and provide higher interobserver agreement than CUS for predicting hepatic

  5. Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

    PubMed

    Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

    2018-06-01

    Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

  6. A fast and accurate method to predict 2D and 3D aerodynamic boundary layer flows

    NASA Astrophysics Data System (ADS)

    Bijleveld, H. A.; Veldman, A. E. P.

    2014-12-01

    A quasi-simultaneous interaction method is applied to predict 2D and 3D aerodynamic flows. This method is suitable for offshore wind turbine design software as it is a very accurate and computationally reasonably cheap method. This study shows the results for a NACA 0012 airfoil. The two applied solvers converge to the experimental values when the grid is refined. We also show that in separation the eigenvalues remain positive thus avoiding the Goldstein singularity at separation. In 3D we show a flow over a dent in which separation occurs. A rotating flat plat is used to show the applicability of the method for rotating flows. The shown capabilities of the method indicate that the quasi-simultaneous interaction method is suitable for design methods for offshore wind turbine blades.

  7. Predicting total organic halide formation from drinking water chlorination using quantitative structure-property relationships.

    PubMed

    Luilo, G B; Cabaniss, S E

    2011-10-01

    Chlorinating water which contains dissolved organic matter (DOM) produces disinfection byproducts, the majority of unknown structure. Hence, the total organic halide (TOX) measurement is used as a surrogate for toxic disinfection byproducts. This work derives a robust quantitative structure-property relationship (QSPR) for predicting the TOX formation potential of model compounds. Literature data for 49 compounds were used to train the QSPR in moles of chlorine per mole of compound (Cp) (mol-Cl/mol-Cp). The resulting QSPR has four descriptors, calibration [Formula: see text] of 0.72 and standard deviation of estimation of 0.43 mol-Cl/mol-Cp. Internal and external validation indicate that the QSPR has good predictive power and low bias (‰<‰1%). Applying this QSPR to predict TOX formation by DOM surrogates - tannic acid, two model fulvic acids and two agent-based model assemblages - gave a predicted TOX range of 136-184 µg-Cl/mg-C, consistent with experimental data for DOM, which ranged from 78 to 192 µg-Cl/mg-C. However, the limited structural variation in the training data may limit QSPR applicability; studies of more sulfur-containing compounds, heterocyclic compounds and high molecular weight compounds could lead to a more widely applicable QSPR.

  8. Quantitative Prediction of Computational Quality (so the S and C Folks will Accept it)

    NASA Technical Reports Server (NTRS)

    Hemsch, Michael J.; Luckring, James M.; Morrison, Joseph H.

    2004-01-01

    Our choice of title may seem strange but we mean each word. In this talk, we are not going to be concerned with computations made "after the fact", i.e. those for which data are available and which are being conducted for explanation and insight. Here we are interested in preventing S&C design problems by finding them through computation before data are available. For such a computation to have any credibility with those who absorb the risk, it is necessary to quantitatively PREDICT the quality of the computational results.

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

    PubMed

    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

  10. Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

    PubMed Central

    Xue, Yi; Skrynnikov, Nikolai R

    2014-01-01

    Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields. PMID:24452989

  11. Prediction of trabecular bone qualitative properties using scanning quantitative ultrasound

    PubMed Central

    Qin, Yi-Xian; Lin, Wei; Mittra, Erik; Xia, Yi; Cheng, Jiqi; Judex, Stefan; Rubin, Clint; Müller, Ralph

    2012-01-01

    Microgravity induced bone loss represents a critical health problem in astronauts, particularly occurred in weight-supporting skeleton, which leads to osteopenia and increase of fracture risk. Lack of suitable evaluation modality makes it difficult for monitoring skeletal status in long term space mission and increases potential risk of complication. Such disuse osteopenia and osteoporosis compromise trabecular bone density, and architectural and mechanical properties. While X-ray based imaging would not be practical in space, quantitative ultrasound may provide advantages to characterize bone density and strength through wave propagation in complex trabecular structure. This study used a scanning confocal acoustic diagnostic and navigation system (SCAN) to evaluate trabecular bone quality in 60 cubic trabecular samples harvested from adult sheep. Ultrasound image based SCAN measurements in structural and strength properties were validated by μCT and compressive mechanical testing. This result indicated a moderately strong negative correlations observed between broadband ultrasonic attenuation (BUA) and μCT-determined bone volume fraction (BV/TV, R2=0.53). Strong correlations were observed between ultrasound velocity (UV) and bone’s mechanical strength and structural parameters, i.e., bulk Young’s modulus (R2=0.67) and BV/TV (R2=0.85). The predictions for bone density and mechanical strength were significantly improved by using a linear combination of both BUA and UV, yielding R2=0.92 for BV/TV and R2=0.71 for bulk Young’s modulus. These results imply that quantitative ultrasound can characterize trabecular structural and mechanical properties through measurements of particular ultrasound parameters, and potentially provide an excellent estimation for bone’s structural integrity. PMID:23976803

  12. Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

    PubMed

    Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

    2014-08-15

    A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Toward the accurate first-principles prediction of ionization equilibria in proteins.

    PubMed

    Khandogin, Jana; Brooks, Charles L

    2006-08-08

    The calculation of pK(a) values for ionizable sites in proteins has been traditionally based on numerical solutions of the Poisson-Boltzmann equation carried out using a high-resolution protein structure. In this paper, we present a method based on continuous constant pH molecular dynamics (CPHMD) simulations, which allows the first-principles description of protein ionization equilibria. Our method utilizes an improved generalized Born implicit solvent model with an approximate Debye-Hückel screening function to account for salt effects and the replica-exchange (REX) protocol for enhanced conformational and protonation state sampling. The accuracy and robustness of the present method are demonstrated by 1 ns REX-CPHMD titration simulations of 10 proteins, which exhibit anomalously large pK(a) shifts for the carboxylate and histidine side chains. The experimental pK(a) values of these proteins are reliably reproduced with a root-mean-square error ranging from 0.6 unit for proteins containing few buried ionizable side chains to 1.0 unit or slightly higher for proteins containing ionizable side chains deeply buried in the core and experiencing strong charge-charge interactions. This unprecedented level of agreement with experimental benchmarks for the de novo calculation of pK(a) values suggests that the CPHMD method is maturing into a practical tool for the quantitative prediction of protein ionization equilibria, and this, in turn, opens a door to atomistic simulations of a wide variety of pH-coupled conformational phenomena in biological macromolecules such as protein folding or misfolding, aggregation, ligand binding, membrane interaction, and catalysis.

  14. 3D ECG- and respiratory-gated non-contrast-enhanced (CE) perfusion MRI for postoperative lung function prediction in non-small-cell lung cancer patients: A comparison with thin-section quantitative computed tomography, dynamic CE-perfusion MRI, and perfusion scan.

    PubMed

    Ohno, Yoshiharu; Seki, Shinichiro; Koyama, Hisanobu; Yoshikawa, Takeshi; Matsumoto, Sumiaki; Takenaka, Daisuke; Kassai, Yoshimori; Yui, Masao; Sugimura, Kazuro

    2015-08-01

    To compare predictive capabilities of non-contrast-enhanced (CE)- and dynamic CE-perfusion MRIs, thin-section multidetector computed tomography (CT) (MDCT), and perfusion scan for postoperative lung function in non-small cell lung cancer (NSCLC) patients. Sixty consecutive pathologically diagnosed NSCLC patients were included and prospectively underwent thin-section MDCT, non-CE-, and dynamic CE-perfusion MRIs and perfusion scan, and had their pre- and postoperative forced expiratory volume in one second (FEV1 ) measured. Postoperative percent FEV1 (po%FEV1 ) was then predicted from the fractional lung volume determined on semiquantitatively assessed non-CE- and dynamic CE-perfusion MRIs, from the functional lung volumes determined on quantitative CT, from the number of segments observed on qualitative CT, and from uptakes detected on perfusion scans within total and resected lungs. Predicted po%FEV1 s were then correlated with actual po%FEV1 s, which were %FEV1 s measured postoperatively. The limits of agreement were also determined. All predicted po%FEV1 s showed significant correlation (0.73 ≤ r ≤ 0.93, P < 0.0001) and limits of agreement with actual po%FEV1 (non-CE-perfusion MRI: 0.3 ± 10.0%, dynamic CE-perfusion MRI: 1.0 ± 10.8%, perfusion scan: 2.2 ± 14.1%, quantitative CT: 1.2 ± 9.0%, qualitative CT: 1.5 ± 10.2%). Non-CE-perfusion MRI may be able to predict postoperative lung function more accurately than qualitatively assessed MDCT and perfusion scan. © 2014 Wiley Periodicals, Inc.

  15. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

    PubMed Central

    Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

    2015-01-01

    Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

  16. Development of a New Model for Accurate Prediction of Cloud Water Deposition on Vegetation

    NASA Astrophysics Data System (ADS)

    Katata, G.; Nagai, H.; Wrzesinsky, T.; Klemm, O.; Eugster, W.; Burkard, R.

    2006-12-01

    Scarcity of water resources in arid and semi-arid areas is of great concern in the light of population growth and food shortages. Several experiments focusing on cloud (fog) water deposition on the land surface suggest that cloud water plays an important role in water resource in such regions. A one-dimensional vegetation model including the process of cloud water deposition on vegetation has been developed to better predict cloud water deposition on the vegetation. New schemes to calculate capture efficiency of leaf, cloud droplet size distribution, and gravitational flux of cloud water were incorporated in the model. Model calculations were compared with the data acquired at the Norway spruce forest at the Waldstein site, Germany. High performance of the model was confirmed by comparisons of calculated net radiation, sensible and latent heat, and cloud water fluxes over the forest with measurements. The present model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the Lovett model, which is a commonly used cloud water deposition model. Detailed calculations of evapotranspiration and of turbulent exchange of heat and water vapor within the canopy and the modifications are necessary for accurate prediction of cloud water deposition. Numerical experiments to examine the dependence of cloud water deposition on the vegetation species (coniferous and broad-leaved trees, flat and cylindrical grasses) and structures (Leaf Area Index (LAI) and canopy height) are performed using the presented model. The results indicate that the differences of leaf shape and size have a large impact on cloud water deposition. Cloud water deposition also varies with the growth of vegetation and seasonal change of LAI. We found that the coniferous trees whose height and LAI are 24 m and 2.0 m2m-2, respectively, produce the largest amount of cloud water deposition in all combinations of vegetation species and structures in the

  17. [A new method of processing quantitative PCR data].

    PubMed

    Ke, Bing-Shen; Li, Guang-Yun; Chen, Shi-Min; Huang, Xiang-Yan; Chen, Ying-Jian; Xu, Jun

    2003-05-01

    Today standard PCR can't satisfy the need of biotechnique development and clinical research any more. After numerous dynamic research, PE company found there is a linear relation between initial template number and cycling time when the accumulating fluorescent product is detectable.Therefore,they developed a quantitative PCR technique to be used in PE7700 and PE5700. But the error of this technique is too great to satisfy the need of biotechnique development and clinical research. A better quantitative PCR technique is needed. The mathematical model submitted here is combined with the achievement of relative science,and based on the PCR principle and careful analysis of molecular relationship of main members in PCR reaction system. This model describes the function relation between product quantity or fluorescence intensity and initial template number and other reaction conditions, and can reflect the accumulating rule of PCR product molecule accurately. Accurate quantitative PCR analysis can be made use this function relation. Accumulated PCR product quantity can be obtained from initial template number. Using this model to do quantitative PCR analysis,result error is only related to the accuracy of fluorescence intensity or the instrument used. For an example, when the fluorescence intensity is accurate to 6 digits and the template size is between 100 to 1,000,000, the quantitative result accuracy will be more than 99%. The difference of result error is distinct using same condition,same instrument but different analysis method. Moreover,if the PCR quantitative analysis system is used to process data, it will get result 80 times of accuracy than using CT method.

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

    PubMed

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

    2015-02-05

    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). Copyright © 2013 Elsevier B.V. All rights reserved.

  19. The role of quantitative estrogen receptor status in predicting tumor response at surgery in breast cancer patients treated with neoadjuvant chemotherapy.

    PubMed

    Raphael, Jacques; Gandhi, Sonal; Li, Nim; Lu, Fang-I; Trudeau, Maureen

    2017-07-01

    Estrogen receptor (ER) negative (-) breast cancer (BC) patients have better tumor response rates than ER-positive (+) patients after neoadjuvant chemotherapy (NCT). We conducted a retrospective review using the institutional database "Biomatrix" to assess the value of quantitative ER status in predicting tumor response at surgery and to identify potential predictors of survival outcomes. Univariate followed by multivariable regression analyses were conducted to assess the association between quantitative ER and tumor response assessed as tumor size reduction and pathologic complete response (pCR). Predictors of recurrence-free survival (RFS) were identified using a cox proportional hazards model (CPH). A log-rank test was used to compare RFS between groups if a significant predictor was identified. 304 patients were included with a median follow-up of 43.3 months (Q1-Q3 28.7-61.1) and a mean age of 49.7 years (SD 10.9). Quantitative ER was inversely associated with tumor size reduction and pCR (OR 0.99, 95% CI 0.99-1.00, p = 0.027 and 0.98 95% CI 0.97-0.99, p < 0.0001, respectively). A cut-off of 60 and 80% predicted best the association with tumor size reduction and pCR, respectively. pCR was shown to be an independent predictor of RFS (HR 0.17, 95% CI 0.07-0.43, p = 0.0002) in all patients. At 5 years, 93% of patients with pCR and 72% of patients with residual tumor were recurrence-free, respectively (p = 0.0012). Quantitative ER status is inversely associated with tumor response in BC patients treated with NCT. A cut-off of 60 and 80% predicts best the association with tumor size reduction and pCR, respectively. Therefore, patients with an ER status higher than the cut-off might benefit from a neoadjuvant endocrine therapy approach. Patients with pCR had better survival outcomes independently of their tumor phenotype. Further prospective studies are needed to validate the clinical utility of quantitative ER as a predictive marker of tumor response.

  20. Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer.

    PubMed

    Covarrubias-Pazaran, Giovanny

    2016-01-01

    Most traits of agronomic importance are quantitative in nature, and genetic markers have been used for decades to dissect such traits. Recently, genomic selection has earned attention as next generation sequencing technologies became feasible for major and minor crops. Mixed models have become a key tool for fitting genomic selection models, but most current genomic selection software can only include a single variance component other than the error, making hybrid prediction using additive, dominance and epistatic effects unfeasible for species displaying heterotic effects. Moreover, Likelihood-based software for fitting mixed models with multiple random effects that allows the user to specify the variance-covariance structure of random effects has not been fully exploited. A new open-source R package called sommer is presented to facilitate the use of mixed models for genomic selection and hybrid prediction purposes using more than one variance component and allowing specification of covariance structures. The use of sommer for genomic prediction is demonstrated through several examples using maize and wheat genotypic and phenotypic data. At its core, the program contains three algorithms for estimating variance components: Average information (AI), Expectation-Maximization (EM) and Efficient Mixed Model Association (EMMA). Kernels for calculating the additive, dominance and epistatic relationship matrices are included, along with other useful functions for genomic analysis. Results from sommer were comparable to other software, but the analysis was faster than Bayesian counterparts in the magnitude of hours to days. In addition, ability to deal with missing data, combined with greater flexibility and speed than other REML-based software was achieved by putting together some of the most efficient algorithms to fit models in a gentle environment such as R.

  1. Quantitative LC-MS of polymers: determining accurate molecular weight distributions by combined size exclusion chromatography and electrospray mass spectrometry with maximum entropy data processing.

    PubMed

    Gruendling, Till; Guilhaus, Michael; Barner-Kowollik, Christopher

    2008-09-15

    We report on the successful application of size exclusion chromatography (SEC) combined with electrospray ionization mass spectrometry (ESI-MS) and refractive index (RI) detection for the determination of accurate molecular weight distributions of synthetic polymers, corrected for chromatographic band broadening. The presented method makes use of the ability of ESI-MS to accurately depict the peak profiles and retention volumes of individual oligomers eluting from the SEC column, whereas quantitative information on the absolute concentration of oligomers is obtained from the RI-detector only. A sophisticated computational algorithm based on the maximum entropy principle is used to process the data gained by both detectors, yielding an accurate molecular weight distribution, corrected for chromatographic band broadening. Poly(methyl methacrylate) standards with molecular weights up to 10 kDa serve as model compounds. Molecular weight distributions (MWDs) obtained by the maximum entropy procedure are compared to MWDs, which were calculated by a conventional calibration of the SEC-retention time axis with peak retention data obtained from the mass spectrometer. Comparison showed that for the employed chromatographic system, distributions below 7 kDa were only weakly influenced by chromatographic band broadening. However, the maximum entropy algorithm could successfully correct the MWD of a 10 kDa standard for band broadening effects. Molecular weight averages were between 5 and 14% lower than the manufacturer stated data obtained by classical means of calibration. The presented method demonstrates a consistent approach for analyzing data obtained by coupling mass spectrometric detectors and concentration sensitive detectors to polymer liquid chromatography.

  2. 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

    NASA Technical Reports Server (NTRS)

    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

  3. Quantitative Graphics in Newspapers.

    ERIC Educational Resources Information Center

    Tankard, James W., Jr.

    The use of quantitative graphics in newspapers requires achieving a balance between being accurate and getting the attention of the reader. The statistical representations in newspapers are drawn by graphic designers whose key technique is fusion--the striking combination of two visual images. This technique often results in visual puns,…

  4. Accurate prediction of pregnancy viability by means of a simple scoring system.

    PubMed

    Bottomley, Cecilia; Van Belle, Vanya; Kirk, Emma; Van Huffel, Sabine; Timmerman, Dirk; Bourne, Tom

    2013-01-01

    What is the performance of a simple scoring system to predict whether women will have an ongoing viable intrauterine pregnancy beyond the first trimester? A simple scoring system using demographic and initial ultrasound variables accurately predicts pregnancy viability beyond the first trimester with an area under the curve (AUC) in a receiver operating characteristic curve of 0.924 [95% confidence interval (CI) 0.900-0.947] on an independent test set. Individual demographic and ultrasound factors, such as maternal age, vaginal bleeding and gestational sac size, are strong predictors of miscarriage. Previous mathematical models have combined individual risk factors with reasonable performance. A simple scoring system derived from a mathematical model that can be easily implemented in clinical practice has not previously been described for the prediction of ongoing viability. This was a prospective observational study in a single early pregnancy assessment centre during a 9-month period. A cohort of 1881 consecutive women undergoing transvaginal ultrasound scan at a gestational age <84 days were included. Women were excluded if the first trimester outcome was not known. Demographic features, symptoms and ultrasound variables were tested for their influence on ongoing viability. Logistic regression was used to determine the influence on first trimester viability from demographics and symptoms alone, ultrasound findings alone and then from all the variables combined. Each model was developed on a training data set, and a simple scoring system was derived from this. This scoring system was tested on an independent test data set. The final outcome based on a total of 1435 participants was an ongoing viable pregnancy in 885 (61.7%) and early pregnancy loss in 550 (38.3%) women. The scoring system using significant demographic variables alone (maternal age and amount of bleeding) to predict ongoing viability gave an AUC of 0.724 (95% CI = 0.692-0.756) in the training set

  5. Accurate radiation temperature and chemical potential from quantitative photoluminescence analysis of hot carrier populations.

    PubMed

    Gibelli, François; Lombez, Laurent; Guillemoles, Jean-François

    2017-02-15

    In order to characterize hot carrier populations in semiconductors, photoluminescence measurement is a convenient tool, enabling us to probe the carrier thermodynamical properties in a contactless way. However, the analysis of the photoluminescence spectra is based on some assumptions which will be discussed in this work. We especially emphasize the importance of the variation of the material absorptivity that should be considered to access accurate thermodynamical properties of the carriers, especially by varying the excitation power. The proposed method enables us to obtain more accurate results of thermodynamical properties by taking into account a rigorous physical description and finds direct application in investigating hot carrier solar cells, which are an adequate concept for achieving high conversion efficiencies with a relatively simple device architecture.

  6. Robust and accurate decoding of motoneuron behavior and prediction of the resulting force output.

    PubMed

    Thompson, Christopher K; Negro, Francesco; Johnson, Michael D; Holmes, Matthew R; McPherson, Laura Miller; Powers, Randall K; Farina, Dario; Heckman, Charles J

    2018-05-03

    The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the decerebrate cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibers represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allows for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the decerebrate cat. Our results showed

  7. Accurate perception of negative emotions predicts functional capacity in schizophrenia.

    PubMed

    Abram, Samantha V; Karpouzian, Tatiana M; Reilly, James L; Derntl, Birgit; Habel, Ute; Smith, Matthew J

    2014-04-30

    Several studies suggest facial affect perception (FAP) deficits in schizophrenia are linked to poorer social functioning. However, whether reduced functioning is associated with inaccurate perception of specific emotional valence or a global FAP impairment remains unclear. The present study examined whether impairment in the perception of specific emotional valences (positive, negative) and neutrality were uniquely associated with social functioning, using a multimodal social functioning battery. A sample of 59 individuals with schizophrenia and 41 controls completed a computerized FAP task, and measures of functional capacity, social competence, and social attainment. Participants also underwent neuropsychological testing and symptom assessment. Regression analyses revealed that only accurately perceiving negative emotions explained significant variance (7.9%) in functional capacity after accounting for neurocognitive function and symptoms. Partial correlations indicated that accurately perceiving anger, in particular, was positively correlated with functional capacity. FAP for positive, negative, or neutral emotions were not related to social competence or social attainment. Our findings were consistent with prior literature suggesting negative emotions are related to functional capacity in schizophrenia. Furthermore, the observed relationship between perceiving anger and performance of everyday living skills is novel and warrants further exploration. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Preoperative Cerebral Oxygen Extraction Fraction Imaging Generated from 7T MR Quantitative Susceptibility Mapping Predicts Development of Cerebral Hyperperfusion following Carotid Endarterectomy.

    PubMed

    Nomura, J-I; Uwano, I; Sasaki, M; Kudo, K; Yamashita, F; Ito, K; Fujiwara, S; Kobayashi, M; Ogasawara, K

    2017-12-01

    Preoperative hemodynamic impairment in the affected cerebral hemisphere is associated with the development of cerebral hyperperfusion following carotid endarterectomy. Cerebral oxygen extraction fraction images generated from 7T MR quantitative susceptibility mapping correlate with oxygen extraction fraction images on positron-emission tomography. The present study aimed to determine whether preoperative oxygen extraction fraction imaging generated from 7T MR quantitative susceptibility mapping could identify patients at risk for cerebral hyperperfusion following carotid endarterectomy. Seventy-seven patients with unilateral internal carotid artery stenosis (≥70%) underwent preoperative 3D T2*-weighted imaging using a multiple dipole-inversion algorithm with a 7T MR imager. Quantitative susceptibility mapping images were then obtained, and oxygen extraction fraction maps were generated. Quantitative brain perfusion single-photon emission CT was also performed before and immediately after carotid endarterectomy. ROIs were automatically placed in the bilateral middle cerebral artery territories in all images using a 3D stereotactic ROI template, and affected-to-contralateral ratios in the ROIs were calculated on quantitative susceptibility mapping-oxygen extraction fraction images. Ten patients (13%) showed post-carotid endarterectomy hyperperfusion (cerebral blood flow increases of ≥100% compared with preoperative values in the ROIs on brain perfusion SPECT). Multivariate analysis showed that a high quantitative susceptibility mapping-oxygen extraction fraction ratio was significantly associated with the development of post-carotid endarterectomy hyperperfusion (95% confidence interval, 33.5-249.7; P = .002). Sensitivity, specificity, and positive- and negative-predictive values of the quantitative susceptibility mapping-oxygen extraction fraction ratio for the prediction of the development of post-carotid endarterectomy hyperperfusion were 90%, 84%, 45%, and 98

  10. Predicting ESI/MS Signal Change for Anions in Different Solvents.

    PubMed

    Kruve, Anneli; Kaupmees, Karl

    2017-05-02

    LC/ESI/MS is a technique widely used for qualitative and quantitative analysis in various fields. However, quantification is currently possible only for compounds for which the standard substances are available, as the ionization efficiency of different compounds in ESI source differs by orders of magnitude. In this paper we present an approach for quantitative LC/ESI/MS analysis without standard substances. This approach relies on accurately predicting the ionization efficiencies in ESI source based on a model, which uses physicochemical parameters of analytes. Furthermore, the model has been made transferable between different mobile phases and instrument setups by using a suitable set of calibration compounds. This approach has been validated both in flow injection and chromatographic mode with gradient elution.

  11. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

    PubMed Central

    Maturana, Matias I.; Apollo, Nicholas V.; Hadjinicolaou, Alex E.; Garrett, David J.; Cloherty, Shaun L.; Kameneva, Tatiana; Grayden, David B.; Ibbotson, Michael R.; Meffin, Hamish

    2016-01-01

    Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

  12. Quantitative Predictive Models for Systemic Toxicity (SOT)

    EPA Science Inventory

    Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

  13. Prediction of neonatal respiratory morbidity by quantitative ultrasound lung texture analysis: a multicenter study.

    PubMed

    Palacio, Montse; Bonet-Carne, Elisenda; Cobo, Teresa; Perez-Moreno, Alvaro; Sabrià, Joan; Richter, Jute; Kacerovsky, Marian; Jacobsson, Bo; García-Posada, Raúl A; Bugatto, Fernando; Santisteve, Ramon; Vives, Àngels; Parra-Cordero, Mauro; Hernandez-Andrade, Edgar; Bartha, José Luis; Carretero-Lucena, Pilar; Tan, Kai Lit; Cruz-Martínez, Rogelio; Burke, Minke; Vavilala, Suseela; Iruretagoyena, Igor; Delgado, Juan Luis; Schenone, Mauro; Vilanova, Josep; Botet, Francesc; Yeo, George S H; Hyett, Jon; Deprest, Jan; Romero, Roberto; Gratacos, Eduard

    2017-08-01

    Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure restricts the use of fetal lung maturity assessment. The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis of the fetal lung (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (<39.0 weeks) deliveries. This was a prospective multicenter study conducted in 20 centers worldwide. Fetal lung ultrasound images were obtained at 25.0-38.6 weeks of gestation within 48 hours of delivery, stored in Digital Imaging and Communication in Medicine format, and analyzed with quantusFLM. Physicians were blinded to the analysis. At delivery, perinatal outcomes and the occurrence of neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, were registered. The performance of the ultrasound texture analysis test to predict neonatal respiratory morbidity was evaluated. A total of 883 images were collected, but 17.3% were discarded because of poor image quality or exclusion criteria, leaving 730 observations for the final analysis. The prevalence of neonatal respiratory morbidity was 13.8% (101 of 730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, positive and negative predictive values of 74.3% (75 of 101), 88.6% (557 of 629), 51.0% (75 of 147), and 95.5% (557 of 583), respectively. Accuracy was 86.5% (632 of 730) and positive and negative likelihood ratios were 6.5 and 0.3, respectively. The quantusFLM predicted neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests with the advantage of being a noninvasive technique. Copyright © 2017. Published by Elsevier Inc.

  14. Qualitative and quantitative prediction of volatile compounds from initial amino acid profiles in Korean rice wine (makgeolli) model.

    PubMed

    Kang, Bo-Sik; Lee, Jang-Eun; Park, Hyun-Jin

    2014-06-01

    In Korean rice wine (makgeolli) model, we tried to develop a prediction model capable of eliciting a quantitative relationship between initial amino acids in makgeolli mash and major aromatic compounds, such as fusel alcohols, their acetate esters, and ethyl esters of fatty acids, in makgeolli brewed. Mass-spectrometry-based electronic nose (MS-EN) was used to qualitatively discriminate between makgeollis made from makgeolli mashes with different amino acid compositions. Following this measurement, headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (GC-MS) combined with partial least-squares regression (PLSR) method was employed to quantitatively correlate amino acid composition of makgeolli mash with major aromatic compounds evolved during makgeolli fermentation. In qualitative prediction with MS-EN analysis, the makgeollis were well discriminated according to the volatile compounds derived from amino acids of makgeolli mash. Twenty-seven ion fragments with mass-to-charge ratio (m/z) of 55 to 98 amu were responsible for the discrimination. In GC-MS combined with PLSR method, a quantitative approach between the initial amino acids of makgeolli mash and the fusel compounds of makgeolli demonstrated that coefficient of determination (R(2)) of most of the fusel compounds ranged from 0.77 to 0.94 in good correlation, except for 2-phenylethanol (R(2) = 0.21), whereas R(2) for ethyl esters of MCFAs including ethyl caproate, ethyl caprylate, and ethyl caprate was 0.17 to 0.40 in poor correlation. The amino acids have been known to affect the aroma in alcoholic beverages. In this study, we demonstrated that an electronic nose qualitatively differentiated Korean rice wines (makgeollis) by their volatile compounds evolved from amino acids with rapidity and reproducibility and successively, a quantitative correlation with acceptable R2 between amino acids and fusel compounds could be established via HS-SPME GC-MS combined with partial least

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

    PubMed

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

    2016-04-01

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

  16. [Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS].

    PubMed

    Li, Wen-bing; Yao, Lin-tao; Liu, Mu-hua; Huang, Lin; Yao, Ming-yin; Chen, Tian-bing; He, Xiu-wen; Yang, Ping; Hu, Hui-qin; Nie, Jiang-hui

    2015-05-01

    Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.

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

    PubMed Central

    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

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

    PubMed

    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.

  19. The Quantitative Reasoning for College Science (QuaRCS) Assessment: Emerging Themes from 5 Years of Data

    NASA Astrophysics Data System (ADS)

    Follette, Katherine; Dokter, Erin; Buxner, Sanlyn

    2018-01-01

    The Quantitative Reasoning for College Science (QuaRCS) Assessment is a validated assessment instrument that was designed to measure changes in students' quantitative reasoning skills, attitudes toward mathematics, and ability to accurately assess their own quantitative abilities. It has been administered to more than 5,000 students at a variety of institutions at the start and end of a semester of general education college science instruction. I will begin by briefly summarizing our published work surrounding validation of the instrument and identification of underlying attitudinal factors (composite variables identified via factor analysis) that predict 50% of the variation in students' scores on the assessment. I will then discuss more recent unpublished work, including: (1) Development and validation of an abbreviated version of the assessment (The QuaRCS Light), which results in marked improvements in students' ability to maintain a high effort level throughout the assessment and has broad implications for quantitative reasoning assessments in general, and (2) Our efforts to revise the attitudinal portion of the assessment to better assess math anxiety level, another key factor in student performance on numerical assessments.

  20. Predicting drug hydrolysis based on moisture uptake in various packaging designs.

    PubMed

    Naversnik, Klemen; Bohanec, Simona

    2008-12-18

    An attempt was made to predict the stability of a moisture sensitive drug product based on the knowledge of the dependence of the degradation rate on tablet moisture. The moisture increase inside a HDPE bottle with the drug formulation was simulated with the sorption-desorption moisture transfer model, which, in turn, allowed an accurate prediction of the drug degradation kinetics. The stability prediction, obtained by computer simulation, was made in a considerably shorter time frame and required little resources compared to a conventional stability study. The prediction was finally upgraded to a stochastic Monte Carlo simulation, which allowed quantitative incorporation of uncertainty, stemming from various sources. The resulting distribution of the outcome of interest (amount of degradation product at expiry) is a comprehensive way of communicating the result along with its uncertainty, superior to single-value results or confidence intervals.

  1. Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

    PubMed

    Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

    2015-04-01

    The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Predicting Protein Function by Genomic Context: Quantitative Evaluation and Qualitative Inferences

    PubMed Central

    Huynen, Martijn; Snel, Berend; Lathe, Warren; Bork, Peer

    2000-01-01

    Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes. PMID:10958638

  3. Quantitative Protein Topography Analysis and High-Resolution Structure Prediction Using Hydroxyl Radical Labeling and Tandem-Ion Mass Spectrometry (MS)*

    PubMed Central

    Kaur, Parminder; Kiselar, Janna; Yang, Sichun; Chance, Mark R.

    2015-01-01

    Hydroxyl radical footprinting based MS for protein structure assessment has the goal of understanding ligand induced conformational changes and macromolecular interactions, for example, protein tertiary and quaternary structure, but the structural resolution provided by typical peptide-level quantification is limiting. In this work, we present experimental strategies using tandem-MS fragmentation to increase the spatial resolution of the technique to the single residue level to provide a high precision tool for molecular biophysics research. Overall, in this study we demonstrated an eightfold increase in structural resolution compared with peptide level assessments. In addition, to provide a quantitative analysis of residue based solvent accessibility and protein topography as a basis for high-resolution structure prediction; we illustrate strategies of data transformation using the relative reactivity of side chains as a normalization strategy and predict side-chain surface area from the footprinting data. We tested the methods by examination of Ca+2-calmodulin showing highly significant correlations between surface area and side-chain contact predictions for individual side chains and the crystal structure. Tandem ion based hydroxyl radical footprinting-MS provides quantitative high-resolution protein topology information in solution that can fill existing gaps in structure determination for large proteins and macromolecular complexes. PMID:25687570

  4. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

    PubMed Central

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

    2016-01-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

  5. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

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

    Poulin, Patrick, E-mail: patrick-poulin@videotron.ca; Ekins, Sean; Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201

    A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V{sub ss}) in humans under in vivo conditions. Thismore » correlation method demonstrated inaccurate predictions of V{sub ss} for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V{sub ss} of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.« less

  6. Quantitative spatiotemporal analysis of antibody fragment diffusion and endocytic consumption in tumor spheroids.

    PubMed

    Thurber, Greg M; Wittrup, K Dane

    2008-05-01

    Antibody-based cancer treatment depends upon distribution of the targeting macromolecule throughout tumor tissue, and spatial heterogeneity could significantly limit efficacy in many cases. Antibody distribution in tumor tissue is a function of drug dosage, antigen concentration, binding affinity, antigen internalization, drug extravasation from blood vessels, diffusion in the tumor extracellular matrix, and systemic clearance rates. We have isolated the effects of a subset of these variables by live-cell microscopic imaging of single-chain antibody fragments against carcinoembryonic antigen in LS174T tumor spheroids. The measured rates of scFv penetration and retention were compared with theoretical predictions based on simple scaling criteria. The theory predicts that antibody dose must be large enough to drive a sufficient diffusive flux of antibody to overcome cellular internalization, and exposure time must be long enough to allow penetration to the spheroid center. The experimental results in spheroids are quantitatively consistent with these predictions. Therefore, simple scaling criteria can be applied to accurately predict antibody and antibody fragment penetration distance in tumor tissue.

  7. Quantitative Spatiotemporal Analysis of Antibody Fragment Diffusion and Endocytic Consumption in Tumor Spheroids

    PubMed Central

    Thurber, Greg M.; Wittrup, K. Dane

    2010-01-01

    Antibody-based cancer treatment depends upon distribution of the targeting macromolecule throughout tumor tissue, and spatial heterogeneity could significantly limit efficacy in many cases. Antibody distribution in tumor tissue is a function of drug dosage, antigen concentration, binding affinity, antigen internalization, drug extravasation from blood vessels, diffusion in the tumor extracellular matrix, and systemic clearance rates. We have isolated the effects of a subset of these variables by live-cell microscopic imaging of single-chain antibody fragments against carcinoembryonic antigen in LS174T tumor spheroids. The measured rates of scFv penetration and retention were compared with theoretical predictions based on simple scaling criteria. The theory predicts that antibody dose must be large enough to drive a sufficient diffusive flux of antibody to overcome cellular internalization, and exposure time must be long enough to allow penetration to the spheroid center. The experimental results in spheroids are quantitatively consistent with these predictions. Therefore, simple scaling criteria can be applied to accurately predict antibody and antibody fragment penetration distance in tumor tissue. PMID:18451160

  8. Accurate donor electron wave functions from a multivalley effective mass theory.

    NASA Astrophysics Data System (ADS)

    Pendo, Luke; Hu, Xuedong

    Multivalley effective mass (MEM) theories combine physical intuition with a marginal need for computational resources, but they tend to be insensitive to variations in the wavefunction. However, recent papers suggest full Bloch functions and suitable central cell donor potential corrections are essential to replicating qualitative and quantitative features of the wavefunction. In this talk, we consider a variational MEM method that can accurately predict both spectrum and wavefunction of isolated phosphorus donors. As per Gamble et. al, we employ a truncated series representation of the Bloch function with a tetrahedrally symmetric central cell correction. We use a dynamic dielectric constant, a feature commonly seen in tight-binding methods. Uniquely, we use a freely extensible basis of either all Slater- or all Gaussian-type functions. With a large basis able to capture the influence of higher energy eigenstates, this method is well positioned to consider the influence of external perturbations, such as electric field or applied strain, on the charge density. This work is supported by the US Army Research Office (W911NF1210609).

  9. Precocious quantitative cognition in monkeys.

    PubMed

    Ferrigno, Stephen; Hughes, Kelly D; Cantlon, Jessica F

    2016-02-01

    Basic quantitative abilities are thought to have an innate basis in humans partly because the ability to discriminate quantities emerges early in child development. If humans and nonhuman primates share this developmentally primitive foundation of quantitative reasoning, then this ability should be present early in development across species and should emerge earlier in monkeys than in humans because monkeys mature faster than humans. We report that monkeys spontaneously make accurate quantity choices by 1 year of age in a task that human children begin to perform only at 2.5 to 3 years of age. Additionally, we report that the quantitative sensitivity of infant monkeys is equal to that of the adult animals in their group and that rates of learning do not differ between infant and adult animals. This novel evidence of precocious quantitative reasoning in infant monkeys suggests that human quantitative reasoning shares its early developing foundation with other primates. The data further suggest that early developing components of primate quantitative reasoning are constrained by maturational factors related to genetic development as opposed to learning experience alone.

  10. Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

    PubMed

    Wacker, Soren; Noskov, Sergei Yu

    2018-05-01

    Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost

  11. Accurate thermoelastic tensor and acoustic velocities of NaCl

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

    Marcondes, Michel L., E-mail: michel@if.usp.br; Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455; Shukla, Gaurav, E-mail: shukla@physics.umn.edu

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

  12. Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour.

    PubMed

    Wollstein, Andreas; Walsh, Susan; Liu, Fan; Chakravarthy, Usha; Rahu, Mati; Seland, Johan H; Soubrane, Gisèle; Tomazzoli, Laura; Topouzis, Fotis; Vingerling, Johannes R; Vioque, Jesus; Böhringer, Stefan; Fletcher, Astrid E; Kayser, Manfred

    2017-02-27

    Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing.

  13. Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour

    PubMed Central

    Wollstein, Andreas; Walsh, Susan; Liu, Fan; Chakravarthy, Usha; Rahu, Mati; Seland, Johan H.; Soubrane, Gisèle; Tomazzoli, Laura; Topouzis, Fotis; Vingerling, Johannes R.; Vioque, Jesus; Böhringer, Stefan; Fletcher, Astrid E.; Kayser, Manfred

    2017-01-01

    Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing. PMID:28240252

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

    PubMed

    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

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

    PubMed Central

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

    2010-01-01

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

  16. Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

    PubMed Central

    DeLeon, Orlando; Hodis, Hagit; O’Malley, Yunxia; Johnson, Jacklyn; Salimi, Hamid; Zhai, Yinjie; Winter, Elizabeth; Remec, Claire; Eichelberger, Noah; Van Cleave, Brandon; Puliadi, Ramya; Harrington, Robert D.; Stapleton, Jack T.; Haim, Hillel

    2017-01-01

    The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties

  17. Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

    PubMed

    Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

    2013-12-03

    We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in

  18. Assessment of quantitative structure-activity relationship of toxicity prediction models for Korean chemical substance control legislation

    PubMed Central

    Kim, Kwang-Yon; Shin, Seong Eun; No, Kyoung Tai

    2015-01-01

    Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used

  19. Natural bacterial communities serve as quantitative geochemical biosensors.

    PubMed

    Smith, Mark B; Rocha, Andrea M; Smillie, Chris S; Olesen, Scott W; Paradis, Charles; Wu, Liyou; Campbell, James H; Fortney, Julian L; Mehlhorn, Tonia L; Lowe, Kenneth A; Earles, Jennifer E; Phillips, Jana; Techtmann, Steve M; Joyner, Dominique C; Elias, Dwayne A; Bailey, Kathryn L; Hurt, Richard A; Preheim, Sarah P; Sanders, Matthew C; Yang, Joy; Mueller, Marcella A; Brooks, Scott; Watson, David B; Zhang, Ping; He, Zhili; Dubinsky, Eric A; Adams, Paul D; Arkin, Adam P; Fields, Matthew W; Zhou, Jizhong; Alm, Eric J; Hazen, Terry C

    2015-05-12

    Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination, even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive. Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts. Copyright © 2015 Smith et al.

  20. Natural bacterial communities serve as quantitative geochemical biosensors

    DOE PAGES

    Smith, Mark B.; Rocha, Andrea M.; Smillie, Chris S.; ...

    2015-05-12

    Biological sensors can be engineered to measure a wide range of environmental conditions. Here we show that statistical analysis of DNA from natural microbial communities can be used to accurately identify environmental contaminants, including uranium and nitrate at a nuclear waste site. In addition to contamination, sequence data from the 16S rRNA gene alone can quantitatively predict a rich catalogue of 26 geochemical features collected from 93 wells with highly differing geochemistry characteristics. We extend this approach to identify sites contaminated with hydrocarbons from the Deepwater Horizon oil spill, finding that altered bacterial communities encode a memory of prior contamination,more » even after the contaminants themselves have been fully degraded. We show that the bacterial strains that are most useful for detecting oil and uranium are known to interact with these substrates, indicating that this statistical approach uncovers ecologically meaningful interactions consistent with previous experimental observations. Future efforts should focus on evaluating the geographical generalizability of these associations. Taken as a whole, these results indicate that ubiquitous, natural bacterial communities can be used as in situ environmental sensors that respond to and capture perturbations caused by human impacts. These in situ biosensors rely on environmental selection rather than directed engineering, and so this approach could be rapidly deployed and scaled as sequencing technology continues to become faster, simpler, and less expensive. Here we show that DNA from natural bacterial communities can be used as a quantitative biosensor to accurately distinguish unpolluted sites from those contaminated with uranium, nitrate, or oil. These results indicate that bacterial communities can be used as environmental sensors that respond to and capture perturbations caused by human impacts.« less

  1. Fluorescence correlation spectroscopy analysis for accurate determination of proportion of doubly labeled DNA in fluorescent DNA pool for quantitative biochemical assays.

    PubMed

    Hou, Sen; Sun, Lili; Wieczorek, Stefan A; Kalwarczyk, Tomasz; Kaminski, Tomasz S; Holyst, Robert

    2014-01-15

    Fluorescent double-stranded DNA (dsDNA) molecules labeled at both ends are commonly produced by annealing of complementary single-stranded DNA (ssDNA) molecules, labeled with fluorescent dyes at the same (3' or 5') end. Because the labeling efficiency of ssDNA is smaller than 100%, the resulting dsDNA have two, one or are without a dye. Existing methods are insufficient to measure the percentage of the doubly-labeled dsDNA component in the fluorescent DNA sample and it is even difficult to distinguish the doubly-labeled DNA component from the singly-labeled component. Accurate measurement of the percentage of such doubly labeled dsDNA component is a critical prerequisite for quantitative biochemical measurements, which has puzzled scientists for decades. We established a fluorescence correlation spectroscopy (FCS) system to measure the percentage of doubly labeled dsDNA (PDL) in the total fluorescent dsDNA pool. The method is based on comparative analysis of the given sample and a reference dsDNA sample prepared by adding certain amount of unlabeled ssDNA into the original ssDNA solution. From FCS autocorrelation functions, we obtain the number of fluorescent dsDNA molecules in the focal volume of the confocal microscope and PDL. We also calculate the labeling efficiency of ssDNA. The method requires minimal amount of material. The samples have the concentration of DNA in the nano-molar/L range and the volume of tens of microliters. We verify our method by using restriction enzyme Hind III to cleave the fluorescent dsDNA. The kinetics of the reaction depends strongly on PDL, a critical parameter for quantitative biochemical measurements. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology

    EPA Science Inventory

    A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose–response, and time-course p...

  3. Mitochondrial DNA as a non-invasive biomarker: Accurate quantification using real time quantitative PCR without co-amplification of pseudogenes and dilution bias

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

    Malik, Afshan N., E-mail: afshan.malik@kcl.ac.uk; Shahni, Rojeen; Rodriguez-de-Ledesma, Ana

    2011-08-19

    Highlights: {yields} Mitochondrial dysfunction is central to many diseases of oxidative stress. {yields} 95% of the mitochondrial genome is duplicated in the nuclear genome. {yields} Dilution of untreated genomic DNA leads to dilution bias. {yields} Unique primers and template pretreatment are needed to accurately measure mitochondrial DNA content. -- Abstract: Circulating mitochondrial DNA (MtDNA) is a potential non-invasive biomarker of cellular mitochondrial dysfunction, the latter known to be central to a wide range of human diseases. Changes in MtDNA are usually determined by quantification of MtDNA relative to nuclear DNA (Mt/N) using real time quantitative PCR. We propose that themore » methodology for measuring Mt/N needs to be improved and we have identified that current methods have at least one of the following three problems: (1) As much of the mitochondrial genome is duplicated in the nuclear genome, many commonly used MtDNA primers co-amplify homologous pseudogenes found in the nuclear genome; (2) use of regions from genes such as {beta}-actin and 18S rRNA which are repetitive and/or highly variable for qPCR of the nuclear genome leads to errors; and (3) the size difference of mitochondrial and nuclear genomes cause a 'dilution bias' when template DNA is diluted. We describe a PCR-based method using unique regions in the human mitochondrial genome not duplicated in the nuclear genome; unique single copy region in the nuclear genome and template treatment to remove dilution bias, to accurately quantify MtDNA from human samples.« less

  4. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models

    NASA Astrophysics Data System (ADS)

    Blackman, Jonathan; Field, Scott E.; Galley, Chad R.; Szilágyi, Béla; Scheel, Mark A.; Tiglio, Manuel; Hemberger, Daniel A.

    2015-09-01

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic -2Yℓm waveform modes resolved by the NR code up to ℓ=8 . We compare our surrogate model to effective one body waveforms from 50 M⊙ to 300 M⊙ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  5. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    PubMed

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

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

    PubMed

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

    2016-05-01

    Adrenal venous sampling (AVS) is the only available method to distinguish bilateral from unilateral primary aldosteronism (PA). AVS has several drawbacks, so it is reasonable to avoid this procedure when the results would not affect clinical management. Our objective was to identify a clinical criterion that can reliably predict nonlateralized AVS as a surrogate for bilateral PA that is not treated surgically. A retrospective diagnostic cross-sectional study conducted at Slovenian national endocrine referral center included 69 consecutive patients (mean age 56 ± 8 years, 21 females) with PA who underwent AVS. PA was confirmed with the saline infusion test (SIT). AVS was performed sequentially during continuous adrenocorticotrophic hormone (ACTH) infusion. The main outcome measures were variables associated with nonlateralized AVS to derive a clinical prediction rule. Sixty-seven (97%) patients had a successful AVS and were included in the statistical analysis. A total of 39 (58%) patients had nonlateralized AVS. The combined criterion of serum potassium ≥3.5 mmol/L, post-SIT aldosterone <18 ng/dL, and either no or bilateral tumor found on computed tomography (CT) imaging had perfect estimated specificity (and thus 100% positive predictive value) for bilateral PA, saving an estimated 16% of the patients (11/67) from unnecessary AVS. The best overall classification accuracy (50/67 = 75%) was achieved using the post-SIT aldosterone level <18 ng/dL alone, which yielded 74% sensitivity and 75% specificity for predicting nonlateralized AVS. Our clinical prediction criterion appears to accurately determine a subset of patients with bilateral PA who could avoid unnecessary AVS and immediately commence with medical treatment.

  7. Prediction of Safety Margin and Optimization of Dosing Protocol for a Novel Antibiotic using Quantitative Systems Pharmacology Modeling.

    PubMed

    Woodhead, Jeffrey L; Paech, Franziska; Maurer, Martina; Engelhardt, Marc; Schmitt-Hoffmann, Anne H; Spickermann, Jochen; Messner, Simon; Wind, Mathias; Witschi, Anne-Therese; Krähenbühl, Stephan; Siler, Scott Q; Watkins, Paul B; Howell, Brett A

    2018-06-07

    Elevations of liver enzymes have been observed in clinical trials with BAL30072, a novel antibiotic. In vitro assays have identified potential mechanisms for the observed hepatotoxicity, including electron transport chain (ETC) inhibition and reactive oxygen species (ROS) generation. DILIsym, a quantitative systems pharmacology (QSP) model of drug-induced liver injury, has been used to predict the likelihood that each mechanism explains the observed toxicity. DILIsym was also used to predict the safety margin for a novel BAL30072 dosing scheme; it was predicted to be low. DILIsym was then used to recommend potential modifications to this dosing scheme; weight-adjusted dosing and a requirement to assay plasma alanine aminotransferase (ALT) daily and stop dosing as soon as ALT increases were observed improved the predicted safety margin of BAL30072 and decreased the predicted likelihood of severe injury. This research demonstrates a potential application for QSP modeling in improving the safety profile of candidate drugs. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

  8. Quantitative power Doppler ultrasound measures of peripheral joint synovitis in poor prognosis early rheumatoid arthritis predict radiographic progression.

    PubMed

    Sreerangaiah, Dee; Grayer, Michael; Fisher, Benjamin A; Ho, Meilien; Abraham, Sonya; Taylor, Peter C

    2016-01-01

    To assess the value of quantitative vascular imaging by power Doppler US (PDUS) as a tool that can be used to stratify patient risk of joint damage in early seropositive RA while still biologic naive but on synthetic DMARD treatment. Eighty-five patients with seropositive RA of <3 years duration had clinical, laboratory and imaging assessments at 0 and 12 months. Imaging assessments consisted of radiographs of the hands and feet, two-dimensional (2D) high-frequency and PDUS imaging of 10 MCP joints that were scored for erosions and vascularity and three-dimensional (3D) PDUS of MCP joints and wrists that were scored for vascularity. Severe deterioration on radiographs and ultrasonography was seen in 45 and 28% of patients, respectively. The 3D power Doppler volume and 2D vascularity scores were the most useful US predictors of deterioration. These variables were modelled in two equations that estimate structural damage over 12 months. The equations had a sensitivity of 63.2% and specificity of 80.9% for predicting radiographic structural damage and a sensitivity of 54.2% and specificity of 96.7% for predicting structural damage on ultrasonography. In seropositive early RA, quantitative vascular imaging by PDUS has clinical utility in predicting which patients will derive benefit from early use of biologic therapy. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  9. Quantitative Prediction of Rate Constants for Aqueous Racemization To Avoid Pointless Stereoselective Syntheses

    PubMed Central

    Ballard, Andrew; Ahmad, Hiwa O.; Narduolo, Stefania; Rosa, Lucy; Chand, Nikki; Cosgrove, David A.; Varkonyi, Peter; Asaad, Nabil; Tomasi, Simone

    2017-01-01

    Abstract Racemization has a large impact upon the biological properties of molecules but the chemical scope of compounds with known rate constants for racemization in aqueous conditions was hitherto limited. To address this remarkable blind spot, we have measured the kinetics for racemization of 28 compounds using circular dichroism and 1H NMR spectroscopy. We show that rate constants for racemization (measured by ourselves and others) correlate well with deprotonation energies from quantum mechanical (QM) and group contribution calculations. Such calculations thus provide predictions of the second‐order rate constants for general‐base‐catalyzed racemization that are usefully accurate. When applied to recent publications describing the stereoselective synthesis of compounds of purported biological value, the calculations reveal that racemization would be sufficiently fast to render these expensive syntheses pointless. PMID:29072355

  10. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.

    PubMed

    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.

  11. Quantitative analysis of red wine tannins using Fourier-transform mid-infrared spectrometry.

    PubMed

    Fernandez, Katherina; Agosin, Eduardo

    2007-09-05

    Tannin content and composition are critical quality components of red wines. No spectroscopic method assessing these phenols in wine has been described so far. We report here a new method using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometric techniques for the quantitative analysis of red wine tannins. Calibration models were developed using protein precipitation and phloroglucinolysis as analytical reference methods. After spectra preprocessing, six different predictive partial least-squares (PLS) models were evaluated, including the use of interval selection procedures such as iPLS and CSMWPLS. PLS regression with full-range (650-4000 cm(-1)), second derivative of the spectra and phloroglucinolysis as the reference method gave the most accurate determination for tannin concentration (RMSEC = 2.6%, RMSEP = 9.4%, r = 0.995). The prediction of the mean degree of polymerization (mDP) of the tannins also gave a reasonable prediction (RMSEC = 6.7%, RMSEP = 10.3%, r = 0.958). These results represent the first step in the development of a spectroscopic methodology for the quantification of several phenolic compounds that are critical for wine quality.

  12. A Quantitative Study of Oxygen as a Metabolic Regulator

    NASA Technical Reports Server (NTRS)

    Radhakrishnan, Krishnan; LaManna, Joseph C.; Cabera, Marco E.

    2000-01-01

    An acute reduction in oxygen delivery to a tissue is associated with metabolic changes aimed at maintaining ATP homeostasis. However, given the complexity of the human bio-energetic system, it is difficult to determine quantitatively how cellular metabolic processes interact to maintain ATP homeostasis during stress (e.g., hypoxia, ischemia, and exercise). In particular, we are interested in determining mechanisms relating cellular oxygen concentration to observed metabolic responses at the cellular, tissue, organ, and whole body levels and in quantifying how changes in tissue oxygen availability affect the pathways of ATP synthesis and the metabolites that control these pathways. In this study; we extend a previously developed mathematical model of human bioenergetics, to provide a physicochemical framework that permits quantitative understanding of oxygen as a metabolic regulator. Specifically, the enhancement - sensitivity analysis - permits studying the effects of variations in tissue oxygenation and parameters controlling cellular respiration on glycolysis, lactate production, and pyruvate oxidation. The analysis can distinguish between parameters that must be determined accurately and those that require less precision, based on their effects on model predictions. This capability may prove to be important in optimizing experimental design, thus reducing use of animals.

  13. Accurate prediction of complex free surface flow around a high speed craft using a single-phase level set method

    NASA Astrophysics Data System (ADS)

    Broglia, Riccardo; Durante, Danilo

    2017-11-01

    This paper focuses on the analysis of a challenging free surface flow problem involving a surface vessel moving at high speeds, or planing. The investigation is performed using a general purpose high Reynolds free surface solver developed at CNR-INSEAN. The methodology is based on a second order finite volume discretization of the unsteady Reynolds-averaged Navier-Stokes equations (Di Mascio et al. in A second order Godunov—type scheme for naval hydrodynamics, Kluwer Academic/Plenum Publishers, Dordrecht, pp 253-261, 2001; Proceedings of 16th international offshore and polar engineering conference, San Francisco, CA, USA, 2006; J Mar Sci Technol 14:19-29, 2009); air/water interface dynamics is accurately modeled by a non standard level set approach (Di Mascio et al. in Comput Fluids 36(5):868-886, 2007a), known as the single-phase level set method. In this algorithm the governing equations are solved only in the water phase, whereas the numerical domain in the air phase is used for a suitable extension of the fluid dynamic variables. The level set function is used to track the free surface evolution; dynamic boundary conditions are enforced directly on the interface. This approach allows to accurately predict the evolution of the free surface even in the presence of violent breaking waves phenomena, maintaining the interface sharp, without any need to smear out the fluid properties across the two phases. This paper is aimed at the prediction of the complex free-surface flow field generated by a deep-V planing boat at medium and high Froude numbers (from 0.6 up to 1.2). In the present work, the planing hull is treated as a two-degree-of-freedom rigid object. Flow field is characterized by the presence of thin water sheets, several energetic breaking waves and plungings. The computational results include convergence of the trim angle, sinkage and resistance under grid refinement; high-quality experimental data are used for the purposes of validation, allowing to

  14. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

    2017-04-04

    Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

  15. Automated selected reaction monitoring software for accurate label-free protein quantification.

    PubMed

    Teleman, Johan; Karlsson, Christofer; Waldemarson, Sofia; Hansson, Karin; James, Peter; Malmström, Johan; Levander, Fredrik

    2012-07-06

    Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

  16. QUANTITATIVE TEMPLATE FOR SUBTYPING PRIMARY PROGRESSIVE APHASIA

    PubMed Central

    Mesulam, Marsel; Wieneke, Christina; Rogalski, Emily; Cobia, Derin; Thompson, Cynthia; Weintraub, Sandra

    2009-01-01

    Objective To provide a quantitative algorithm for classifying primary progressive aphasia (PPA) into agrammatic (PPA-G), semantic (PPA-S) and logopenic (PPA-L) variants, each of which is known to have a different probability of association with Alzheimer’s disease (AD) versus frontotemporal lobar degeneration (FTLD). Design Prospectively and consecutively enrolled 16 PPA patients tested with neuropsychological instruments and magnetic resonance imaging (MRI). Setting University medical center. Participants PPA patients recruited nationally in the USA as part of a longitudinal study. Results A two-dimensional template, reflecting performance on tests of syntax (Northwestern Anagram Test) and lexical semantics (Peabody Picture Vocabulary Test), classified all 16 patients in concordance with a clinical diagnosis that had been made prior to the administration of the quantitative tests. All three subtypes had distinctly asymmetrical atrophy of the left perisylvian language network. Each subtype also had distinctive peak atrophy sites. Only PPA-G had peak atrophy in the IFG (Broca’s area), only PPA-S had peak atrophy in the anterior temporal lobe, and only PPA-L had peak atrophy in area 37. Conclusions Once an accurate root diagnosis of PPA is made, subtyping can be quantitatively guided using a two-dimensional template based on orthogonal tasks of grammatical competence and word comprehension. Although the choice of tasks and precise cut-off levels may evolve in time, this set of 16 patients demonstrates the feasibility of using a simple algorithm for clinico-anatomical classification in PPA. Prospective studies will show whether this suptyping can improve the clinical prediction of underlying neuropathology. PMID:20008661

  17. Quantitative biology: where modern biology meets physical sciences.

    PubMed

    Shekhar, Shashank; Zhu, Lian; Mazutis, Linas; Sgro, Allyson E; Fai, Thomas G; Podolski, Marija

    2014-11-05

    Quantitative methods and approaches have been playing an increasingly important role in cell biology in recent years. They involve making accurate measurements to test a predefined hypothesis in order to compare experimental data with predictions generated by theoretical models, an approach that has benefited physicists for decades. Building quantitative models in experimental biology not only has led to discoveries of counterintuitive phenomena but has also opened up novel research directions. To make the biological sciences more quantitative, we believe a two-pronged approach needs to be taken. First, graduate training needs to be revamped to ensure biology students are adequately trained in physical and mathematical sciences and vice versa. Second, students of both the biological and the physical sciences need to be provided adequate opportunities for hands-on engagement with the methods and approaches necessary to be able to work at the intersection of the biological and physical sciences. We present the annual Physiology Course organized at the Marine Biological Laboratory (Woods Hole, MA) as a case study for a hands-on training program that gives young scientists the opportunity not only to acquire the tools of quantitative biology but also to develop the necessary thought processes that will enable them to bridge the gap between these disciplines. © 2014 Shekhar, Zhu, Mazutis, Sgro, Fai, and Podolski. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  18. Continuously growing rodent molars result from a predictable quantitative evolutionary change over 50 million years

    PubMed Central

    Mushegyan, Vagan; Eronen, Jussi T.; Lawing, A. Michelle; Sharir, Amnon; Janis, Christine; Jernvall, Jukka; Klein, Ophir D.

    2015-01-01

    Summary The fossil record is widely informative about evolution, but fossils are not systematically used to study the evolution of stem cell-driven renewal. Here, we examined evolution of the continuous growth (hypselodonty) of rodent molar teeth, which is fuelled by the presence of dental stem cells. We studied occurrences of 3500 North American rodent fossils, ranging from 50 million years ago (mya) to 2 mya. We examined changes in molar height to determine if evolution of hypselodonty shows distinct patterns in the fossil record, and we found that hypselodont taxa emerged through intermediate forms of increasing crown height. Next, we designed a Markov simulation model, which replicated molar height increases throughout the Cenozoic, and, moreover, evolution of hypselodonty. Thus, by extension, the retention of the adult stem-cell niche appears to be a predictable quantitative rather than a stochastic qualitative process. Our analyses predict that hypselodonty will eventually become the dominant phenotype. PMID:25921530

  19. Quantitative structure-property relationships for prediction of boiling point, vapor pressure, and melting point.

    PubMed

    Dearden, John C

    2003-08-01

    Boiling point, vapor pressure, and melting point are important physicochemical properties in the modeling of the distribution and fate of chemicals in the environment. However, such data often are not available, and therefore must be estimated. Over the years, many attempts have been made to calculate boiling points, vapor pressures, and melting points by using quantitative structure-property relationships, and this review examines and discusses the work published in this area, and concentrates particularly on recent studies. A number of software programs are commercially available for the calculation of boiling point, vapor pressure, and melting point, and these have been tested for their predictive ability with a test set of 100 organic chemicals.

  20. Quantitative fetal fibronectin testing in combination with cervical length measurement in the prediction of spontaneous preterm delivery in symptomatic women.

    PubMed

    Bruijn, Mmc; Vis, J Y; Wilms, F F; Oudijk, M A; Kwee, A; Porath, M M; Oei, G; Scheepers, Hcj; Spaanderman, Mea; Bloemenkamp, Kwm; Haak, M C; Bolte, A C; Vandenbussche, Fpha; Woiski, M D; Bax, C J; Cornette, Jmj; Duvekot, J J; Nij Bijvanck, Bwa; van Eyck, J; Franssen, Mtm; Sollie, K M; van der Post, Jam; Bossuyt, Pmm; Opmeer, B C; Kok, M; Mol, Bwj; van Baaren, G-J

    2016-11-01

    To evaluate whether in symptomatic women, the combination of quantitative fetal fibronectin (fFN) testing and cervical length (CL) improves the prediction of preterm delivery (PTD) within 7 days compared with qualitative fFN and CL. Post hoc analysis of frozen fFN samples of a nationwide cohort study. Ten perinatal centres in the Netherlands. Symptomatic women between 24 and 34 weeks of gestation. The risk of PTD <7 days was estimated in predefined CL and fFN strata. We used logistic regression to develop a model including quantitative fFN and CL, and one including qualitative fFN (threshold 50 ng/ml) and CL. We compared the models' capacity to identify women at low risk (<5%) for delivery within 7 days using a reclassification table. Spontaneous delivery within 7 days after study entry. We studied 350 women, of whom 69 (20%) delivered within 7 days. The risk of PTD in <7 days ranged from 2% in the lowest fFN group (<10 ng/ml) to 71% in the highest group (>500 ng/ml). Multivariable logistic regression showed an increasing risk of PTD in <7 days with rising fFN concentration [10-49 ng/ml: odds ratio (OR) 1.3, 95% confidence interval (95% CI) 0.23-7.0; 50-199 ng/ml: OR 3.2, 95% CI 0.79-13; 200-499 ng/ml: OR 9.0, 95% CI 2.3-35; >500 ng/ml: OR 39, 95% CI 9.4-164] and shortening of the CL (OR 0.86 per mm, 95% CI 0.82-0.90). Use of quantitative fFN instead of qualitative fFN resulted in reclassification of 18 (5%) women from high to low risk, of whom one (6%) woman delivered within 7 days. In symptomatic women, quantitative fFN testing does not improve the prediction of PTD within 7 days compared with qualitative fFN testing in combination with CL measurement in terms of reclassification from high to low (<5%) risk, but it adds value across the risk range. Quantitative fFN testing adds value to qualitative fFN testing with CL measurement in the prediction of PTD. © 2015 Royal College of Obstetricians and Gynaecologists.

  1. Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli

    NASA Astrophysics Data System (ADS)

    Chang, Catherine Ching Han; Li, Chen; Webb, Geoffrey I.; Tey, Bengti; Song, Jiangning; Ramanan, Ramakrishnan Nagasundara

    2016-03-01

    Periplasmic expression of soluble proteins in Escherichia coli not only offers a much-simplified downstream purification process, but also enhances the probability of obtaining correctly folded and biologically active proteins. Different combinations of signal peptides and target proteins lead to different soluble protein expression levels, ranging from negligible to several grams per litre. Accurate algorithms for rational selection of promising candidates can serve as a powerful tool to complement with current trial-and-error approaches. Accordingly, proteomics studies can be conducted with greater efficiency and cost-effectiveness. Here, we developed a predictor with a two-stage architecture, to predict the real-valued expression level of target protein in the periplasm. The output of the first-stage support vector machine (SVM) classifier determines which second-stage support vector regression (SVR) classifier to be used. When tested on an independent test dataset, the predictor achieved an overall prediction accuracy of 78% and a Pearson’s correlation coefficient (PCC) of 0.77. We further illustrate the relative importance of various features with respect to different models. The results indicate that the occurrence of dipeptide glutamine and aspartic acid is the most important feature for the classification model. Finally, we provide access to the implemented predictor through the Periscope webserver, freely accessible at http://lightning.med.monash.edu/periscope/.

  2. WPC Quantitative Precipitation Forecasts - Day 1

    Science.gov Websites

    to all federal, state, and local government web resources and services. Quantitative Precipitation Prediction Center 5830 University Research Court College Park, Maryland 20740 Weather Prediction Center Web

  3. Toward Quantitative Small Animal Pinhole SPECT: Assessment of Quantitation Accuracy Prior to Image Compensations

    PubMed Central

    Chen, Chia-Lin; Wang, Yuchuan; Lee, Jason J. S.; Tsui, Benjamin M. W.

    2011-01-01

    Purpose We assessed the quantitation accuracy of small animal pinhole single photon emission computed tomography (SPECT) under the current preclinical settings, where image compensations are not routinely applied. Procedures The effects of several common image-degrading factors and imaging parameters on quantitation accuracy were evaluated using Monte-Carlo simulation methods. Typical preclinical imaging configurations were modeled, and quantitative analyses were performed based on image reconstructions without compensating for attenuation, scatter, and limited system resolution. Results Using mouse-sized phantom studies as examples, attenuation effects alone degraded quantitation accuracy by up to −18% (Tc-99m or In-111) or −41% (I-125). The inclusion of scatter effects changed the above numbers to −12% (Tc-99m or In-111) and −21% (I-125), respectively, indicating the significance of scatter in quantitative I-125 imaging. Region-of-interest (ROI) definitions have greater impacts on regional quantitation accuracy for small sphere sources as compared to attenuation and scatter effects. For the same ROI, SPECT acquisitions using pinhole apertures of different sizes could significantly affect the outcome, whereas the use of different radii-of-rotation yielded negligible differences in quantitation accuracy for the imaging configurations simulated. Conclusions We have systematically quantified the influence of several factors affecting the quantitation accuracy of small animal pinhole SPECT. In order to consistently achieve accurate quantitation within 5% of the truth, comprehensive image compensation methods are needed. PMID:19048346

  4. Near infrared spectroscopy as an on-line method to quantitatively determine glycogen and predict ultimate pH in pre rigor bovine M. longissimus dorsi.

    PubMed

    Lomiwes, D; Reis, M M; Wiklund, E; Young, O A; North, M

    2010-12-01

    The potential of near infrared (NIR) spectroscopy as an on-line method to quantify glycogen and predict ultimate pH (pH(u)) of pre rigor beef M. longissimus dorsi (LD) was assessed. NIR spectra (538 to 1677 nm) of pre rigor LD from steers, cows and bulls were collected early post mortem and measurements were made for pre rigor glycogen concentration and pH(u). Spectral and measured data were combined to develop models to quantify glycogen and predict the pH(u) of pre rigor LD. NIR spectra and pre rigor predicted values obtained from quantitative models were shown to be poorly correlated against glycogen and pH(u) (r(2)=0.23 and 0.20, respectively). Qualitative models developed to categorize each muscle according to their pH(u) were able to correctly categorize 42% of high pH(u) samples. Optimum qualitative and quantitative models derived from NIR spectra found low correlation between predicted values and reference measurements. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd.. All rights reserved.

  5. Using radiance predicted by the P3 approximation in a spherical geometry to predict tissue optical properties

    NASA Astrophysics Data System (ADS)

    Dickey, Dwayne J.; Moore, Ronald B.; Tulip, John

    2001-01-01

    For photodynamic therapy of solid tumors, such as prostatic carcinoma, to be achieved, an accurate model to predict tissue parameters and light dose must be found. Presently, most analytical light dosimetry models are fluence based and are not clinically viable for tissue characterization. Other methods of predicting optical properties, such as Monet Carlo, are accurate but far too time consuming for clinical application. However, radiance predicted by the P3-Approximation, an anaylitical solution to the transport equation, may be a viable and accurate alternative. The P3-Approximation accurately predicts optical parameters in intralipid/methylene blue based phantoms in a spherical geometry. The optical parameters furnished by the radiance, when introduced into fluence predicted by both P3- Approximation and Grosjean Theory, correlate well with experimental data. The P3-Approximation also predicts the optical properties of prostate tissue, agreeing with documented optical parameters. The P3-Approximation could be the clinical tool necessary to facilitate PDT of solid tumors because of the limited number of invasive measurements required and the speed in which accurate calculations can be performed.

  6. Quantitative and predictive model of kinetic regulation by E. coli TPP riboswitches

    PubMed Central

    Guedich, Sondés; Puffer-Enders, Barbara; Baltzinger, Mireille; Hoffmann, Guillaume; Da Veiga, Cyrielle; Jossinet, Fabrice; Thore, Stéphane; Bec, Guillaume; Ennifar, Eric; Burnouf, Dominique; Dumas, Philippe

    2016-01-01

    ABSTRACT Riboswitches are non-coding elements upstream or downstream of mRNAs that, upon binding of a specific ligand, regulate transcription and/or translation initiation in bacteria, or alternative splicing in plants and fungi. We have studied thiamine pyrophosphate (TPP) riboswitches regulating translation of thiM operon and transcription and translation of thiC operon in E. coli, and that of THIC in the plant A. thaliana. For all, we ascertained an induced-fit mechanism involving initial binding of the TPP followed by a conformational change leading to a higher-affinity complex. The experimental values obtained for all kinetic and thermodynamic parameters of TPP binding imply that the regulation by A. thaliana riboswitch is governed by mass-action law, whereas it is of kinetic nature for the two bacterial riboswitches. Kinetic regulation requires that the RNA polymerase pauses after synthesis of each riboswitch aptamer to leave time for TPP binding, but only when its concentration is sufficient. A quantitative model of regulation highlighted how the pausing time has to be linked to the kinetic rates of initial TPP binding to obtain an ON/OFF switch in the correct concentration range of TPP. We verified the existence of these pauses and the model prediction on their duration. Our analysis also led to quantitative estimates of the respective efficiency of kinetic and thermodynamic regulations, which shows that kinetically regulated riboswitches react more sharply to concentration variation of their ligand than thermodynamically regulated riboswitches. This rationalizes the interest of kinetic regulation and confirms empirical observations that were obtained by numerical simulations. PMID:26932506

  7. Quantitative and predictive model of kinetic regulation by E. coli TPP riboswitches.

    PubMed

    Guedich, Sondés; Puffer-Enders, Barbara; Baltzinger, Mireille; Hoffmann, Guillaume; Da Veiga, Cyrielle; Jossinet, Fabrice; Thore, Stéphane; Bec, Guillaume; Ennifar, Eric; Burnouf, Dominique; Dumas, Philippe

    2016-01-01

    Riboswitches are non-coding elements upstream or downstream of mRNAs that, upon binding of a specific ligand, regulate transcription and/or translation initiation in bacteria, or alternative splicing in plants and fungi. We have studied thiamine pyrophosphate (TPP) riboswitches regulating translation of thiM operon and transcription and translation of thiC operon in E. coli, and that of THIC in the plant A. thaliana. For all, we ascertained an induced-fit mechanism involving initial binding of the TPP followed by a conformational change leading to a higher-affinity complex. The experimental values obtained for all kinetic and thermodynamic parameters of TPP binding imply that the regulation by A. thaliana riboswitch is governed by mass-action law, whereas it is of kinetic nature for the two bacterial riboswitches. Kinetic regulation requires that the RNA polymerase pauses after synthesis of each riboswitch aptamer to leave time for TPP binding, but only when its concentration is sufficient. A quantitative model of regulation highlighted how the pausing time has to be linked to the kinetic rates of initial TPP binding to obtain an ON/OFF switch in the correct concentration range of TPP. We verified the existence of these pauses and the model prediction on their duration. Our analysis also led to quantitative estimates of the respective efficiency of kinetic and thermodynamic regulations, which shows that kinetically regulated riboswitches react more sharply to concentration variation of their ligand than thermodynamically regulated riboswitches. This rationalizes the interest of kinetic regulation and confirms empirical observations that were obtained by numerical simulations.

  8. Accurate density functional prediction of molecular electron affinity with the scaling corrected Kohn–Sham frontier orbital energies

    NASA Astrophysics Data System (ADS)

    Zhang, DaDi; Yang, Xiaolong; Zheng, Xiao; Yang, Weitao

    2018-04-01

    Electron affinity (EA) is the energy released when an additional electron is attached to an atom or a molecule. EA is a fundamental thermochemical property, and it is closely pertinent to other important properties such as electronegativity and hardness. However, accurate prediction of EA is difficult with density functional theory methods. The somewhat large error of the calculated EAs originates mainly from the intrinsic delocalisation error associated with the approximate exchange-correlation functional. In this work, we employ a previously developed non-empirical global scaling correction approach, which explicitly imposes the Perdew-Parr-Levy-Balduz condition to the approximate functional, and achieve a substantially improved accuracy for the calculated EAs. In our approach, the EA is given by the scaling corrected Kohn-Sham lowest unoccupied molecular orbital energy of the neutral molecule, without the need to carry out the self-consistent-field calculation for the anion.

  9. An effective approach to quantitative analysis of ternary amino acids in foxtail millet substrate based on terahertz spectroscopy.

    PubMed

    Lu, Shao Hua; Li, Bao Qiong; Zhai, Hong Lin; Zhang, Xin; Zhang, Zhuo Yong

    2018-04-25

    Terahertz time-domain spectroscopy has been applied to many fields, however, it still encounters drawbacks in multicomponent mixtures analysis due to serious spectral overlapping. Here, an effective approach to quantitative analysis was proposed, and applied on the determination of the ternary amino acids in foxtail millet substrate. Utilizing three parameters derived from the THz-TDS, the images were constructed and the Tchebichef image moments were used to extract the information of target components. Then the quantitative models were obtained by stepwise regression. The correlation coefficients of leave-one-out cross-validation (R loo-cv 2 ) were more than 0.9595. As for external test set, the predictive correlation coefficients (R p 2 ) were more than 0.8026 and the root mean square error of prediction (RMSE p ) were less than 1.2601. Compared with the traditional methods (PLS and N-PLS methods), our approach is more accurate, robust and reliable, and can be a potential excellent approach to quantify multicomponent with THz-TDS spectroscopy. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    NASA Astrophysics Data System (ADS)

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

    2018-06-01

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

  11. Investigation of a redox-sensitive predictive model of mouse embryonic stem cells differentiation using quantitative nuclease protection assays and glutathione redox status

    EPA Science Inventory

    Investigation of a redox-sensitive predictive model of mouse embryonic stem cell differentiation via quantitative nuclease protection assays and glutathione redox status Chandler KJ,Hansen JM, Knudsen T,and Hunter ES 1. U.S. Environmental Protection Agency, Research Triangl...

  12. Accurate atomistic first-principles calculations of electronic stopping

    DOE PAGES

    Schleife, André; Kanai, Yosuke; Correa, Alfredo A.

    2015-01-20

    In this paper, we show that atomistic first-principles calculations based on real-time propagation within time-dependent density functional theory are capable of accurately describing electronic stopping of light projectile atoms in metal hosts over a wide range of projectile velocities. In particular, we employ a plane-wave pseudopotential scheme to solve time-dependent Kohn-Sham equations for representative systems of H and He projectiles in crystalline aluminum. This approach to simulate nonadiabatic electron-ion interaction provides an accurate framework that allows for quantitative comparison with experiment without introducing ad hoc parameters such as effective charges, or assumptions about the dielectric function. Finally, our work clearlymore » shows that this atomistic first-principles description of electronic stopping is able to disentangle contributions due to tightly bound semicore electrons and geometric aspects of the stopping geometry (channeling versus off-channeling) in a wide range of projectile velocities.« less

  13. Quantitative imaging features: extension of the oncology medical image database

    NASA Astrophysics Data System (ADS)

    Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

    2015-03-01

    Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

  14. Lower NIH stroke scale scores are required to accurately predict a good prognosis in posterior circulation stroke.

    PubMed

    Inoa, Violiza; Aron, Abraham W; Staff, Ilene; Fortunato, Gilbert; Sansing, Lauren H

    2014-01-01

    The NIH stroke scale (NIHSS) is an indispensable tool that aids in the determination of acute stroke prognosis and decision making. Patients with posterior circulation (PC) strokes often present with lower NIHSS scores, which may result in the withholding of thrombolytic treatment from these patients. However, whether these lower initial NIHSS scores predict better long-term prognoses is uncertain. We aimed to assess the utility of the NIHSS at presentation for predicting the functional outcome at 3 months in anterior circulation (AC) versus PC strokes. This was a retrospective analysis of a large prospectively collected database of adults with acute ischemic stroke. Univariate and multivariate analyses were conducted to identify factors associated with outcome. Additional analyses were performed to determine the receiver operating characteristic (ROC) curves for NIHSS scores and outcomes in AC and PC infarctions. Both the optimal cutoffs for maximal diagnostic accuracy and the cutoffs to obtain >80% sensitivity for poor outcomes were determined in AC and PC strokes. The analysis included 1,197 patients with AC stroke and 372 with PC stroke. The median initial NIHSS score for patients with AC strokes was 7 and for PC strokes it was 2. The majority (71%) of PC stroke patients had baseline NIHSS scores ≤4, and 15% of these 'minor' stroke patients had a poor outcome at 3 months. ROC analysis identified that the optimal NIHSS cutoff for outcome prediction after infarction in the AC was 8 and for infarction in the PC it was 4. To achieve >80% sensitivity for detecting patients with a subsequent poor outcome, the NIHSS cutoff for infarctions in the AC was 4 and for infarctions in the PC it was 2. The NIHSS cutoff that most accurately predicts outcomes is 4 points higher in AC compared to PC infarctions. There is potential for poor outcomes in patients with PC strokes and low NIHSS scores, suggesting that thrombolytic treatment should not be withheld from these patients

  15. Calibrant-Free Analyte Quantitation via a Variable Velocity Flow Cell.

    PubMed

    Beck, Jason G; Skuratovsky, Aleksander; Granger, Michael C; Porter, Marc D

    2017-01-17

    In this paper, we describe a novel method for analyte quantitation that does not rely on calibrants, internal standards, or calibration curves but, rather, leverages the relationship between disparate and predictable surface-directed analyte flux to an array of sensing addresses and a measured resultant signal. To reduce this concept to practice, we fabricated two flow cells such that the mean linear fluid velocity, U, was varied systematically over an array of electrodes positioned along the flow axis. This resulted in a predictable variation of the address-directed flux of a redox analyte, ferrocenedimethanol (FDM). The resultant limiting currents measured at a series of these electrodes, and accurately described by a convective-diffusive transport model, provided a means to calculate an "unknown" concentration without the use of calibrants, internal standards, or a calibration curve. Furthermore, the experiment and concentration calculation only takes minutes to perform. Deviation in calculated FDM concentrations from true values was minimized to less than 0.5% when empirically derived values of U were employed.

  16. New analytical model for the ozone electronic ground state potential surface and accurate ab initio vibrational predictions at high energy range.

    PubMed

    Tyuterev, Vladimir G; Kochanov, Roman V; Tashkun, Sergey A; Holka, Filip; Szalay, Péter G

    2013-10-07

    An accurate description of the complicated shape of the potential energy surface (PES) and that of the highly excited vibration states is of crucial importance for various unsolved issues in the spectroscopy and dynamics of ozone and remains a challenge for the theory. In this work a new analytical representation is proposed for the PES of the ground electronic state of the ozone molecule in the range covering the main potential well and the transition state towards the dissociation. This model accounts for particular features specific to the ozone PES for large variations of nuclear displacements along the minimum energy path. The impact of the shape of the PES near the transition state (existence of the "reef structure") on vibration energy levels was studied for the first time. The major purpose of this work was to provide accurate theoretical predictions for ozone vibrational band centres at the energy range near the dissociation threshold, which would be helpful for understanding the very complicated high-resolution spectra and its analyses currently in progress. Extended ab initio electronic structure calculations were carried out enabling the determination of the parameters of a minimum energy path PES model resulting in a new set of theoretical vibrational levels of ozone. A comparison with recent high-resolution spectroscopic data on the vibrational levels gives the root-mean-square deviations below 1 cm(-1) for ozone band centres up to 90% of the dissociation energy. New ab initio vibrational predictions represent a significant improvement with respect to all previously available calculations.

  17. Cloud computing approaches for prediction of ligand binding poses and pathways.

    PubMed

    Lawrenz, Morgan; Shukla, Diwakar; Pande, Vijay S

    2015-01-22

    We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.

  18. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.

    PubMed

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R; Judson, Richard; Corton, J Christopher

    2016-05-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. Published by Oxford University Press on behalf of the Society of Toxicology 2016. This work is written by US Government employees and is in the public domain in the US.

  19. Predicting human blood viscosity in silico

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

    Fedosov, Dmitry A.; Pan, Wenxiao; Caswell, Bruce

    2011-07-05

    Cellular suspensions such as blood are a part of living organisms and their rheological and flow characteristics determine and affect majority of vital functions. The rheological and flow properties of cell suspensions are determined by collective dynamics of cells, their structure or arrangement, cell properties and interactions. We study these relations for blood in silico using a mesoscopic particle-based method and two different models (multi-scale/low-dimensional) of red blood cells. The models yield accurate quantitative predictions of the dependence of blood viscosity on shear rate and hematocrit. We explicitly model cell aggregation interactions and demonstrate the formation of reversible rouleaux structuresmore » resulting in a tremendous increase of blood viscosity at low shear rates and yield stress, in agreement with experiments. The non-Newtonian behavior of such cell suspensions (e.g., shear thinning, yield stress) is analyzed and related to the suspension’s microstructure, deformation and dynamics of single cells. We provide the flrst quantitative estimates of normal stress differences and magnitude of aggregation forces in blood. Finally, the flexibility of the cell models allows them to be employed for quantitative analysis of a much wider class of complex fluids including cell, capsule, and vesicle suspensions.« less

  20. Quantitative Earthquake Prediction on Global and Regional Scales

    NASA Astrophysics Data System (ADS)

    Kossobokov, Vladimir G.

    2006-03-01

    The Earth is a hierarchy of volumes of different size. Driven by planetary convection these volumes are involved into joint and relative movement. The movement is controlled by a wide variety of processes on and around the fractal mesh of boundary zones, and does produce earthquakes. This hierarchy of movable volumes composes a large non-linear dynamical system. Prediction of such a system in a sense of extrapolation of trajectory into the future is futile. However, upon coarse-graining the integral empirical regularities emerge opening possibilities of prediction in a sense of the commonly accepted consensus definition worked out in 1976 by the US National Research Council. Implications of the understanding hierarchical nature of lithosphere and its dynamics based on systematic monitoring and evidence of its unified space-energy similarity at different scales help avoiding basic errors in earthquake prediction claims. They suggest rules and recipes of adequate earthquake prediction classification, comparison and optimization. The approach has already led to the design of reproducible intermediate-term middle-range earthquake prediction technique. Its real-time testing aimed at prediction of the largest earthquakes worldwide has proved beyond any reasonable doubt the effectiveness of practical earthquake forecasting. In the first approximation, the accuracy is about 1-5 years and 5-10 times the anticipated source dimension. Further analysis allows reducing spatial uncertainty down to 1-3 source dimensions, although at a cost of additional failures-to-predict. Despite of limited accuracy a considerable damage could be prevented by timely knowledgeable use of the existing predictions and earthquake prediction strategies. The December 26, 2004 Indian Ocean Disaster seems to be the first indication that the methodology, designed for prediction of M8.0+ earthquakes can be rescaled for prediction of both smaller magnitude earthquakes (e.g., down to M5.5+ in Italy) and

  1. Multicenter Evaluation of a Commercial Cytomegalovirus Quantitative Standard: Effects of Commutability on Interlaboratory Concordance

    PubMed Central

    Shahbazian, M. D.; Valsamakis, A.; Boonyaratanakornkit, J.; Cook, L.; Pang, X. L.; Preiksaitis, J. K.; Schönbrunner, E. R.; Caliendo, A. M.

    2013-01-01

    Commutability of quantitative reference materials has proven important for reliable and accurate results in clinical chemistry. As international reference standards and commercially produced calibration material have become available to address the variability of viral load assays, the degree to which such materials are commutable and the effect of commutability on assay concordance have been questioned. To investigate this, 60 archived clinical plasma samples, which previously tested positive for cytomegalovirus (CMV), were retested by five different laboratories, each using a different quantitative CMV PCR assay. Results from each laboratory were calibrated both with lab-specific quantitative CMV standards (“lab standards”) and with common, commercially available standards (“CMV panel”). Pairwise analyses among laboratories were performed using mean results from each clinical sample, calibrated first with lab standards and then with the CMV panel. Commutability of the CMV panel was determined based on difference plots for each laboratory pair showing plotted values of standards that were within the 95% prediction intervals for the clinical specimens. Commutability was demonstrated for 6 of 10 laboratory pairs using the CMV panel. In half of these pairs, use of the CMV panel improved quantitative agreement compared to use of lab standards. Two of four laboratory pairs for which the CMV panel was noncommutable showed reduced quantitative agreement when that panel was used as a common calibrator. Commutability of calibration material varies across different quantitative PCR methods. Use of a common, commutable quantitative standard can improve agreement across different assays; use of a noncommutable calibrator can reduce agreement among laboratories. PMID:24025907

  2. Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

    PubMed

    Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili

    2016-05-01

    Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

  3. Accurate phase measurements for thick spherical objects using optical quadrature microscopy

    NASA Astrophysics Data System (ADS)

    Warger, William C., II; DiMarzio, Charles A.

    2009-02-01

    In vitro fertilization (IVF) procedures have resulted in the birth of over three million babies since 1978. Yet the live birth rate in the United States was only 34% in 2005, with 32% of the successful pregnancies resulting in multiple births. These multiple pregnancies were directly attributed to the transfer of multiple embryos to increase the probability that a single, healthy embryo was included. Current viability markers used for IVF, such as the cell number, symmetry, size, and fragmentation, are analyzed qualitatively with differential interference contrast (DIC) microscopy. However, this method is not ideal for quantitative measures beyond the 8-cell stage of development because the cells overlap and obstruct the view within and below the cluster of cells. We have developed the phase-subtraction cell-counting method that uses the combination of DIC and optical quadrature microscopy (OQM) to count the number of cells accurately in live mouse embryos beyond the 8-cell stage. We have also created a preliminary analysis to measure the cell symmetry, size, and fragmentation quantitatively by analyzing the relative dry mass from the OQM image in conjunction with the phase-subtraction count. In this paper, we will discuss the characterization of OQM with respect to measuring the phase accurately for spherical samples that are much larger than the depth of field. Once fully characterized and verified with human embryos, this methodology could provide the means for a more accurate method to score embryo viability.

  4. Quantitative Estimation of Plasma Free Drug Fraction in Patients With Varying Degrees of Hepatic Impairment: A Methodological Evaluation.

    PubMed

    Li, Guo-Fu; Yu, Guo; Li, Yanfei; Zheng, Yi; Zheng, Qing-Shan; Derendorf, Hartmut

    2018-07-01

    Quantitative prediction of unbound drug fraction (f u ) is essential for scaling pharmacokinetics through physiologically based approaches. However, few attempts have been made to evaluate the projection of f u values under pathological conditions. The primary objective of this study was to predict f u values (n = 105) of 56 compounds with or without the information of predominant binding protein in patients with varying degrees of hepatic insufficiency by accounting for quantitative changes in molar concentrations of either the major binding protein or albumin plus alpha 1-acid glycoprotein associated with differing levels of hepatic dysfunction. For the purpose of scaling, data pertaining to albumin and α1-acid glycoprotein levels in response to differing degrees of hepatic impairment were systematically collected from 919 adult donors. The results of the present study demonstrate for the first time the feasibility of physiologically based scaling f u in hepatic dysfunction after verifying with experimentally measured data of a wide variety of compounds from individuals with varying degrees of hepatic insufficiency. Furthermore, the high level of predictive accuracy indicates that the inter-relation between the severity of hepatic impairment and these plasma protein levels are physiologically accurate. The present study enhances the confidence in predicting f u in hepatic insufficiency, particularly for albumin-bound drugs. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  5. A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps.

    PubMed

    Nolte, Tom M; Ragas, Ad M J

    2017-03-22

    Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

  6. A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

    PubMed

    Stacey, R Greg; Skinnider, Michael A; Scott, Nichollas E; Foster, Leonard J

    2017-10-23

    An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. PrInCE is the first fast and easy

  7. Hounsfield unit density accurately predicts ESWL success.

    PubMed

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

    2005-01-01

    Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p < 0.0001). We determined by receiver operator curve (ROC) that HDV of 93 or less carries a 90% or better chance of stone freedom following ESWL for upper tract calculi between 5-15mm.

  8. Multiplicative effects model with internal standard in mobile phase for quantitative liquid chromatography-mass spectrometry.

    PubMed

    Song, Mi; Chen, Zeng-Ping; Chen, Yao; Jin, Jing-Wen

    2014-07-01

    Liquid chromatography-mass spectrometry assays suffer from signal instability caused by the gradual fouling of the ion source, vacuum instability, aging of the ion multiplier, etc. To address this issue, in this contribution, an internal standard was added into the mobile phase. The internal standard was therefore ionized and detected together with the analytes of interest by the mass spectrometer to ensure that variations in measurement conditions and/or instrument have similar effects on the signal contributions of both the analytes of interest and the internal standard. Subsequently, based on the unique strategy of adding internal standard in mobile phase, a multiplicative effects model was developed for quantitative LC-MS assays and tested on a proof of concept model system: the determination of amino acids in water by LC-MS. The experimental results demonstrated that the proposed method could efficiently mitigate the detrimental effects of continuous signal variation, and achieved quantitative results with average relative predictive error values in the range of 8.0-15.0%, which were much more accurate than the corresponding results of conventional internal standard method based on the peak height ratio and partial least squares method (their average relative predictive error values were as high as 66.3% and 64.8%, respectively). Therefore, it is expected that the proposed method can be developed and extended in quantitative LC-MS analysis of more complex systems. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Predicting epidermal growth factor receptor gene amplification status in glioblastoma multiforme by quantitative enhancement and necrosis features deriving from conventional magnetic resonance imaging.

    PubMed

    Dong, Fei; Zeng, Qiang; Jiang, Biao; Yu, Xinfeng; Wang, Weiwei; Xu, Jingjing; Yu, Jinna; Li, Qian; Zhang, Minming

    2018-05-01

    To study whether some of the quantitative enhancement and necrosis features in preoperative conventional MRI (cMRI) had a predictive value for epidermal growth factor receptor (EGFR) gene amplification status in glioblastoma multiforme (GBM).Fifty-five patients with pathologically determined GBMs who underwent cMRI were retrospectively reviewed. The following cMRI features were quantitatively measured and recorded: long and short diameters of the enhanced portion (LDE and SDE), maximum and minimum thickness of the enhanced portion (MaxTE and MinTE), and long and short diameters of the necrotic portion (LDN and SDN). Univariate analysis of each feature and a decision tree model fed with all the features were performed. Area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of features, and predictive accuracy was used to assess the performance of the model.For single feature, MinTE showed the best performance in differentiating EGFR gene amplification negative (wild-type) (nEGFR) GBM from EGFR gene amplification positive (pEGFR) GBM, and it got an AUC of 0.68 with a cut-off value of 2.6 mm. The decision tree model included 2 features MinTE and SDN, and got an accuracy of 0.83 in validation dataset.Our results suggest that quantitative measurement of the features MinTE and SDN in preoperative cMRI had a high accuracy for predicting EGFR gene amplification status in GBM.

  10. A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

    PubMed

    Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif

    2016-02-13

    Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Quantitative template for subtyping primary progressive aphasia.

    PubMed

    Mesulam, Marsel; Wieneke, Christina; Rogalski, Emily; Cobia, Derin; Thompson, Cynthia; Weintraub, Sandra

    2009-12-01

    The syndrome of primary progressive aphasia (PPA) is diagnosed when a gradual failure of word usage or comprehension emerges as the principal feature of a neurodegenerative disease. To provide a quantitative algorithm for classifying PPA into agrammatic (PPA-G), semantic (PPA-S), and logopenic (PPA-L) variants, each of which is known to have a different probability of association with Alzheimer disease vs frontotemporal lobar degeneration. Prospective study. University medical center. Sixteen consecutively enrolled patients with PPA who underwent neuropsychological testing and magnetic resonance imaging recruited nationally in the United States as part of a longitudinal study. A 2-dimensional template that reflects performance on tests of syntax (Northwestern Anagram Test) and lexical semantics (Peabody Picture Vocabulary Test-Fourth Edition) classified all 16 patients in concordance with a clinical diagnosis that had been made before the administration of quantitative tests. All 3 PPA subtypes had distinctly asymmetrical atrophy of the left perisylvian language network. Each subtype also had distinctive peak atrophy sites: PPA-G in the inferior frontal gyrus (Broca area), PPA-S in the anterior temporal lobe, and PPA-L in Brodmann area 37. Once an accurate root diagnosis of PPA is made, subtyping can be quantitatively guided using a 2-dimensional template based on orthogonal tasks of grammatical competence and word comprehension. Although the choice of tasks and the precise cutoff levels may need to be adjusted to fit linguistic and educational backgrounds, these 16 patients demonstrate the feasibility of using a simple algorithm for clinicoanatomical classification in PPA. Prospective studies will show whether this subtyping can improve clinical prediction of the underlying neuropathologic condition.

  12. Prediction of Emergent Heart Failure Death by Semi-Quantitative Triage Risk Stratification

    PubMed Central

    Van Spall, Harriette G. C.; Atzema, Clare; Schull, Michael J.; Newton, Gary E.; Mak, Susanna; Chong, Alice; Tu, Jack V.; Stukel, Thérèse A.; Lee, Douglas S.

    2011-01-01

    Objectives Generic triage risk assessments are widely used in the emergency department (ED), but have not been validated for prediction of short-term risk among patients with acute heart failure (HF). Our objective was to evaluate the Canadian Triage Acuity Scale (CTAS) for prediction of early death among HF patients. Methods We included patients presenting with HF to an ED in Ontario from Apr 2003 to Mar 2007. We used the National Ambulatory Care Reporting System and vital statistics databases to examine care and outcomes. Results Among 68,380 patients (76±12 years, 49.4% men), early mortality was stratified with death rates of 9.9%, 1.9%, 0.9%, and 0.5% at 1-day, and 17.2%, 5.9%, 3.8%, and 2.5% at 7-days, for CTAS 1, 2, 3, and 4–5, respectively. Compared to lower acuity (CTAS 4–5) patients, adjusted odds ratios (aOR) for 1-day death were 1.32 (95%CI; 0.93–1.88; p = 0.12) for CTAS 3, 2.41 (95%CI; 1.71–3.40; p<0.001) for CTAS 2, and highest for CTAS 1: 9.06 (95%CI; 6.28–13.06; p<0.001). Predictors of triage-critical (CTAS 1) status included oxygen saturation <90% (aOR 5.92, 95%CI; 3.09–11.81; p<0.001), respiratory rate >24 breaths/minute (aOR 1.96, 95%CI; 1.05–3.67; p = 0.034), and arrival by paramedic (aOR 3.52, 95%CI; 1.70–8.02; p = 0.001). While age/sex-adjusted CTAS score provided good discrimination for ED (c-statistic = 0.817) and 1-day (c-statistic = 0.724) death, mortality prediction was improved further after accounting for cardiac and non-cardiac co-morbidities (c-statistics 0.882 and 0.810, respectively; both p<0.001). Conclusions A semi-quantitative triage acuity scale assigned at ED presentation and based largely on respiratory factors predicted emergent death among HF patients. PMID:21853068

  13. Quantitative imaging methods in osteoporosis.

    PubMed

    Oei, Ling; Koromani, Fjorda; Rivadeneira, Fernando; Zillikens, M Carola; Oei, Edwin H G

    2016-12-01

    Osteoporosis is characterized by a decreased bone mass and quality resulting in an increased fracture risk. Quantitative imaging methods are critical in the diagnosis and follow-up of treatment effects in osteoporosis. Prior radiographic vertebral fractures and bone mineral density (BMD) as a quantitative parameter derived from dual-energy X-ray absorptiometry (DXA) are among the strongest known predictors of future osteoporotic fractures. Therefore, current clinical decision making relies heavily on accurate assessment of these imaging features. Further, novel quantitative techniques are being developed to appraise additional characteristics of osteoporosis including three-dimensional bone architecture with quantitative computed tomography (QCT). Dedicated high-resolution (HR) CT equipment is available to enhance image quality. At the other end of the spectrum, by utilizing post-processing techniques such as the trabecular bone score (TBS) information on three-dimensional architecture can be derived from DXA images. Further developments in magnetic resonance imaging (MRI) seem promising to not only capture bone micro-architecture but also characterize processes at the molecular level. This review provides an overview of various quantitative imaging techniques based on different radiological modalities utilized in clinical osteoporosis care and research.

  14. Accurate Prediction of Inducible Transcription Factor Binding Intensities In Vivo

    PubMed Central

    Siepel, Adam; Lis, John T.

    2012-01-01

    DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding followed by high-throughput sequencing (PB–seq), that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin. We applied our methods to the Drosophila Heat Shock Factor (HSF), which inducibly binds a target DNA sequence element (HSE) following heat shock stress. PB–seq involves incubating sheared naked genomic DNA with recombinant HSF, partitioning the HSF–bound and HSF–free DNA, and then detecting HSF–bound DNA by high-throughput sequencing. We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment. We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity. We also investigated the extent to which DNA accessibility, as measured by digital DNase I footprinting data, could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs, and found GAGA element associated factor (GAF), tetra-acetylation of H4, and H4K16 acetylation to be the most predictive covariates. Lastly, we generated an unbiased model of HSF binding sequences, which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE. These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity. PMID:22479205

  15. Accurate determination of reference materials and natural isolates by means of quantitative (1)h NMR spectroscopy.

    PubMed

    Frank, Oliver; Kreissl, Johanna Karoline; Daschner, Andreas; Hofmann, Thomas

    2014-03-26

    A fast and precise proton nuclear magnetic resonance (qHNMR) method for the quantitative determination of low molecular weight target molecules in reference materials and natural isolates has been validated using ERETIC 2 (Electronic REference To access In vivo Concentrations) based on the PULCON (PULse length based CONcentration determination) methodology and compared to the gravimetric results. Using an Avance III NMR spectrometer (400 MHz) equipped with a broad band observe (BBO) probe, the qHNMR method was validated by determining its linearity, range, precision, and accuracy as well as robustness and limit of quantitation. The linearity of the method was assessed by measuring samples of l-tyrosine, caffeine, or benzoic acid in a concentration range between 0.3 and 16.5 mmol/L (r(2) ≥ 0.99), whereas the interday and intraday precisions were found to be ≤2%. The recovery of a range of reference compounds was ≥98.5%, thus demonstrating the qHNMR method as a precise tool for the rapid quantitation (~15 min) of food-related target compounds in reference materials and natural isolates such as nucleotides, polyphenols, or cyclic peptides.

  16. QUESP and QUEST revisited - fast and accurate quantitative CEST experiments.

    PubMed

    Zaiss, Moritz; Angelovski, Goran; Demetriou, Eleni; McMahon, Michael T; Golay, Xavier; Scheffler, Klaus

    2018-03-01

    Chemical exchange saturation transfer (CEST) NMR or MRI experiments allow detection of low concentrated molecules with enhanced sensitivity via their proton exchange with the abundant water pool. Be it endogenous metabolites or exogenous contrast agents, an exact quantification of the actual exchange rate is required to design optimal pulse sequences and/or specific sensitive agents. Refined analytical expressions allow deeper insight and improvement of accuracy for common quantification techniques. The accuracy of standard quantification methodologies, such as quantification of exchange rate using varying saturation power or varying saturation time, is improved especially for the case of nonequilibrium initial conditions and weak labeling conditions, meaning the saturation amplitude is smaller than the exchange rate (γB 1  < k). The improved analytical 'quantification of exchange rate using varying saturation power/time' (QUESP/QUEST) equations allow for more accurate exchange rate determination, and provide clear insights on the general principles to execute the experiments and to perform numerical evaluation. The proposed methodology was evaluated on the large-shift regime of paramagnetic chemical-exchange-saturation-transfer agents using simulated data and data of the paramagnetic Eu(III) complex of DOTA-tetraglycineamide. The refined formulas yield improved exchange rate estimation. General convergence intervals of the methods that would apply for smaller shift agents are also discussed. Magn Reson Med 79:1708-1721, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  17. A link prediction approach to cancer drug sensitivity prediction.

    PubMed

    Turki, Turki; Wei, Zhi

    2017-10-03

    Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

  18. Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

    PubMed

    Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

    2016-08-01

    This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Predicting Fluid Flow in Stressed Fractures: A Quantitative Evaluation of Methods

    NASA Astrophysics Data System (ADS)

    Weihmann, S. A.; Healy, D.

    2015-12-01

    Reliable estimation of fracture stability in the subsurface is crucial to the success of exploration and production in the petroleum industry, and also for wider applications to earthquake mechanics, hydrogeology and waste disposal. Previous work suggests that fracture stability is related to fluid flow in crystalline basement rocks through shear or tensile instabilities of fractures. Our preliminary scoping analysis compares the fracture stability of 60 partly open (apertures 1.5-3 cm) and electrically conductive (low acoustic amplitudes relative to matrix) fractures from a 16 m section of a producing zone in a basement well in Bayoot field, Yemen, to a non-producing zone in the same well (also 16 m). We determine the Critically Stressed Fractures (CSF; Barton et al., 1995) and dilatation tendency (Td; Ferrill et al., 1999). We find that: 1. CSF (Fig. 1) is a poor predictor of high fluid flow in the inflow zone; 88% of the fractures are predicted to be NOT critically stressed and yet they all occur within a zone of high fluid flow rate 2. Td (Fig. 2) is also a poor predictor of high fluid flow in the inflow zone; 67% of the fractures have a LOW Td(< 0.6) 3. For the non-producing zone CSF is a very reliable predictor (100% are not critically stressed) whereas the values of Tdare consistent with their location in non-producing interval (81% are < 0.6) (Fig. 3 & 4). In summary, neither method correlates well with the observed abundance of hydraulically conductive fractures within the producing zone. Within the non-producing zone CSF and Td make reasonably accurate predictions. Fractures may be filled or partially filled with drilling mud or a lower density and electrically conductive fill such as clay in the producing zone and therefore appear (partly) open. In situ stress, fluid pressure, rock properties (friction, strength) and fracture orientation data used as inputs for the CSF and Td calculations are all subject to uncertainty. Our results suggest that scope

  20. Quantitative Analysis of Radar Returns from Insects

    NASA Technical Reports Server (NTRS)

    Riley, J. R.

    1979-01-01

    When a number of flying insects is low enough to permit their resolution as individual radar targets, quantitative estimates of their aerial density are developed. Accurate measurements of heading distribution using a rotating polarization radar to enhance the wingbeat frequency method of identification are presented.

  1. Quantitative measures of meniscus extrusion predict incident radiographic knee osteoarthritis--data from the Osteoarthritis Initiative.

    PubMed

    Emmanuel, K; Quinn, E; Niu, J; Guermazi, A; Roemer, F; Wirth, W; Eckstein, F; Felson, D

    2016-02-01

    To test the hypothesis that quantitative measures of meniscus extrusion predict incident radiographic knee osteoarthritis (KOA), prior to the advent of radiographic disease. 206 knees with incident radiographic KOA (Kellgren Lawrence Grade (KLG) 0 or 1 at baseline, developing KLG 2 or greater with a definite osteophyte and joint space narrowing (JSN) grade ≥1 by year 4) were matched to 232 control knees not developing incident KOA. Manual segmentation of the central five slices of the medial and lateral meniscus was performed on coronal 3T DESS MRI and quantitative meniscus position was determined. Cases and controls were compared using conditional logistic regression adjusting for age, sex, BMI, race and clinical site. Sensitivity analyses of early (year [Y] 1/2) and late (Y3/4) incidence was performed. Mean medial extrusion distance was significantly greater for incident compared to non-incident knees (1.56 mean ± 1.12 mm SD vs 1.29 ± 0.99 mm; +21%, P < 0.01), so was the percent extrusion area of the medial meniscus (25.8 ± 15.8% vs 22.0 ± 13.5%; +17%, P < 0.05). This finding was consistent for knees restricted to medial incidence. No significant differences were observed for the lateral meniscus in incident medial KOA, or for the tibial plateau coverage between incident and non-incident knees. Restricting the analysis to medial incident KOA at Y1/2 differences were attenuated, but reached significance for extrusion distance, whereas no significant differences were observed at incident KOA in Y3/4. Greater medial meniscus extrusion predicts incident radiographic KOA. Early onset KOA showed greater differences for meniscus position between incident and non-incident knees than late onset KOA. Copyright © 2015 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

  2. FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately.

    PubMed

    Budowski-Tal, Inbal; Nov, Yuval; Kolodny, Rachel

    2010-02-23

    Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

  3. Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction

    PubMed Central

    Murphy, Kevin G.; Jones, Nick S.

    2018-01-01

    Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity. PMID:29367240

  4. Accurate interatomic force fields via machine learning with covariant kernels

    NASA Astrophysics Data System (ADS)

    Glielmo, Aldo; Sollich, Peter; De Vita, Alessandro

    2017-06-01

    We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group SO (d ) for the relevant dimensionality d . Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

  5. Protein asparagine deamidation prediction based on structures with machine learning methods.

    PubMed

    Jia, Lei; Sun, Yaxiong

    2017-01-01

    Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.

  6. Knotty: Efficient and Accurate Prediction of Complex RNA Pseudoknot Structures.

    PubMed

    Jabbari, Hosna; Wark, Ian; Montemagno, Carlo; Will, Sebastian

    2018-06-01

    The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots (Rivas and Eddy, 1999) has O(n6) time and O(n4) space complexity. The algorithm CCJ (Chen et al., 2009) dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (∼200 bases already require 256GB space). We present a CCJ-type algorithm, Knotty, that handles the same comprehensive pseudoknot class of structures as CCJ with improved space complexity of Θ(n3 + Z)-due to the applied technique of sparsification, the number of "candidates", Z, appears to grow significantly slower than n4 on our benchmark set (which include pseudoknotted RNAs up to 400 nucleotides). In terms of run time over this benchmark, Knotty clearly outperforms Pknots and the original CCJ implementation, CCJ 1.0; Knotty's space consumption fundamentally improves over CCJ 1.0, being on a par with the space-economic Pknots. By comparing to CCJ 2.0, our unsparsified Knotty variant, we demonstrate the isolated effect of sparsification. Moreover, Knotty employs the state-of-the-art energy model of "HotKnots DP09", which results in superior prediction accuracy over Pknots. Our software is available at https://github.com/HosnaJabbari/Knotty. will@tbi.unvie.ac.at. Supplementary data are available at Bioinformatics online.

  7. Accurate Prediction of Protein Contact Maps by Coupling Residual Two-Dimensional Bidirectional Long Short-Term Memory with Convolutional Neural Networks.

    PubMed

    Hanson, Jack; Paliwal, Kuldip; Litfin, Thomas; Yang, Yuedong; Zhou, Yaoqi

    2018-06-19

    Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12, (Schaarschmidt et al., 2018)) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC)>0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary data is available at Bioinformatics online.

  8. [Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review].

    PubMed

    Wu, Chang-Guang; Li, Sheng; Ren, Hua-Dong; Yao, Xiao-Hua; Huang, Zi-Jie

    2012-06-01

    Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.

  9. Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions

    NASA Astrophysics Data System (ADS)

    Qiao, T.; Ren, J.; Craigie, C.; Zabalza, J.; Maltin, Ch.; Marshall, S.

    2015-03-01

    It is well known that the eating quality of beef has a significant influence on the repurchase behavior of consumers. There are several key factors that affect the perception of quality, including color, tenderness, juiciness, and flavor. To support consumer repurchase choices, there is a need for an objective measurement of quality that could be applied to meat prior to its sale. Objective approaches such as offered by spectral technologies may be useful, but the analytical algorithms used remain to be optimized. For visible and near infrared (VISNIR) spectroscopy, Partial Least Squares Regression (PLSR) is a widely used technique for meat related quality modeling and prediction. In this paper, a Support Vector Machine (SVM) based machine learning approach is presented to predict beef eating quality traits. Although SVM has been successfully used in various disciplines, it has not been applied extensively to the analysis of meat quality parameters. To this end, the performance of PLSR and SVM as tools for the analysis of meat tenderness is evaluated, using a large dataset acquired under industrial conditions. The spectral dataset was collected using VISNIR spectroscopy with the wavelength ranging from 350 to 1800 nm on 234 beef M. longissimus thoracis steaks from heifers, steers, and young bulls. As the dimensionality with the VISNIR data is very high (over 1600 spectral bands), the Principal Component Analysis (PCA) technique was applied for feature extraction and data reduction. The extracted principal components (less than 100) were then used for data modeling and prediction. The prediction results showed that SVM has a greater potential to predict beef eating quality than PLSR, especially for the prediction of tenderness. The infl uence of animal gender on beef quality prediction was also investigated, and it was found that beef quality traits were predicted most accurately in beef from young bulls.

  10. Local Debonding and Fiber Breakage in Composite Materials Modeled Accurately

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2001-01-01

    A prerequisite for full utilization of composite materials in aerospace components is accurate design and life prediction tools that enable the assessment of component performance and reliability. Such tools assist both structural analysts, who design and optimize structures composed of composite materials, and materials scientists who design and optimize the composite materials themselves. NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) software package (http://www.grc.nasa.gov/WWW/LPB/mac) addresses this need for composite design and life prediction tools by providing a widely applicable and accurate approach to modeling composite materials. Furthermore, MAC/GMC serves as a platform for incorporating new local models and capabilities that are under development at NASA, thus enabling these new capabilities to progress rapidly to a stage in which they can be employed by the code's end users.

  11. Evaluation of a quantitative structure-property relationship (QSPR) for predicting mid-visible refractive index of secondary organic aerosol (SOA).

    PubMed

    Redmond, Haley; Thompson, Jonathan E

    2011-04-21

    In this work we describe and evaluate a simple scheme by which the refractive index (λ = 589 nm) of non-absorbing components common to secondary organic aerosols (SOA) may be predicted from molecular formula and density (g cm(-3)). The QSPR approach described is based on three parameters linked to refractive index-molecular polarizability, the ratio of mass density to molecular weight, and degree of unsaturation. After computing these quantities for a training set of 111 compounds common to atmospheric aerosols, multi-linear regression analysis was conducted to establish a quantitative relationship between the parameters and accepted value of refractive index. The resulting quantitative relationship can often estimate refractive index to ±0.01 when averaged across a variety of compound classes. A notable exception is for alcohols for which the model consistently underestimates refractive index. Homogenous internal mixtures can conceivably be addressed through use of either the volume or mole fraction mixing rules commonly used in the aerosol community. Predicted refractive indices reconstructed from chemical composition data presented in the literature generally agree with previous reports of SOA refractive index. Additionally, the predicted refractive indices lie near measured values we report for λ = 532 nm for SOA generated from vapors of α-pinene (R.I. 1.49-1.51) and toluene (R.I. 1.49-1.50). We envision the QSPR method may find use in reconstructing optical scattering of organic aerosols if mass composition data is known. Alternatively, the method described could be incorporated into in models of organic aerosol formation/phase partitioning to better constrain organic aerosol optical properties.

  12. pyQms enables universal and accurate quantification of mass spectrometry data.

    PubMed

    Leufken, Johannes; Niehues, Anna; Sarin, L Peter; Wessel, Florian; Hippler, Michael; Leidel, Sebastian A; Fufezan, Christian

    2017-10-01

    Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present pyQms, an open-source software for accurate quantification of all types of molecules measurable by MS. pyQms uses isotope pattern matching that offers an accurate quality assessment of all quantifications and the ability to directly incorporate mass spectrometer accuracy. pyQms is, due to its universal design, applicable to every research field, labeling strategy, and acquisition technique. This opens ultimate flexibility for researchers to design experiments employing innovative and hitherto unexplored labeling strategies. Importantly, pyQms performs very well to accurately quantify partially labeled proteomes in large scale and high throughput, the most challenging task for a quantification algorithm. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  13. Large-scale structure prediction by improved contact predictions and model quality assessment.

    PubMed

    Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

    2017-07-15

    Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  14. Gaussian process regression for tool wear prediction

    NASA Astrophysics Data System (ADS)

    Kong, Dongdong; Chen, Yongjie; Li, Ning

    2018-05-01

    To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

  15. A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

    PubMed

    Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

    2006-08-01

    A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

  16. Quantitative structure activity relationship (QSAR) of piperine analogs for bacterial NorA efflux pump inhibitors.

    PubMed

    Nargotra, Amit; Sharma, Sujata; Koul, Jawahir Lal; Sangwan, Pyare Lal; Khan, Inshad Ali; Kumar, Ashwani; Taneja, Subhash Chander; Koul, Surrinder

    2009-10-01

    Quantitative structure activity relationship (QSAR) analysis of piperine analogs as inhibitors of efflux pump NorA from Staphylococcus aureus has been performed in order to obtain a highly accurate model enabling prediction of inhibition of S. aureus NorA of new chemical entities from natural sources as well as synthetic ones. Algorithm based on genetic function approximation method of variable selection in Cerius2 was used to generate the model. Among several types of descriptors viz., topological, spatial, thermodynamic, information content and E-state indices that were considered in generating the QSAR model, three descriptors such as partial negative surface area of the compounds, area of the molecular shadow in the XZ plane and heat of formation of the molecules resulted in a statistically significant model with r(2)=0.962 and cross-validation parameter q(2)=0.917. The validation of the QSAR models was done by cross-validation, leave-25%-out and external test set prediction. The theoretical approach indicates that the increase in the exposed partial negative surface area increases the inhibitory activity of the compound against NorA whereas the area of the molecular shadow in the XZ plane is inversely proportional to the inhibitory activity. This model also explains the relationship of the heat of formation of the compound with the inhibitory activity. The model is not only able to predict the activity of new compounds but also explains the important regions in the molecules in quantitative manner.

  17. Accurate and scalable social recommendation using mixed-membership stochastic block models.

    PubMed

    Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

    2016-12-13

    With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

  18. Accurate and scalable social recommendation using mixed-membership stochastic block models

    PubMed Central

    Godoy-Lorite, Antonia; Moore, Cristopher

    2016-01-01

    With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

  19. Raoult's law revisited: accurately predicting equilibrium relative humidity points for humidity control experiments.

    PubMed

    Bowler, Michael G; Bowler, David R; Bowler, Matthew W

    2017-04-01

    The humidity surrounding a sample is an important variable in scientific experiments. Biological samples in particular require not just a humid atmosphere but often a relative humidity (RH) that is in equilibrium with a stabilizing solution required to maintain the sample in the same state during measurements. The controlled dehydration of macromolecular crystals can lead to significant increases in crystal order, leading to higher diffraction quality. Devices that can accurately control the humidity surrounding crystals while monitoring diffraction have led to this technique being increasingly adopted, as the experiments become easier and more reproducible. Matching the RH to the mother liquor is the first step in allowing the stable mounting of a crystal. In previous work [Wheeler, Russi, Bowler & Bowler (2012). Acta Cryst. F 68 , 111-114], the equilibrium RHs were measured for a range of concentrations of the most commonly used precipitants in macromolecular crystallography and it was shown how these related to Raoult's law for the equilibrium vapour pressure of water above a solution. However, a discrepancy between the measured values and those predicted by theory could not be explained. Here, a more precise humidity control device has been used to determine equilibrium RH points. The new results are in agreement with Raoult's law. A simple argument in statistical mechanics is also presented, demonstrating that the equilibrium vapour pressure of a solvent is proportional to its mole fraction in an ideal solution: Raoult's law. The same argument can be extended to the case where the solvent and solute molecules are of different sizes, as is the case with polymers. The results provide a framework for the correct maintenance of the RH surrounding a sample.

  20. Research study demonstrates computer simulation can predict warpage and assist in its elimination

    NASA Astrophysics Data System (ADS)

    Glozer, G.; Post, S.; Ishii, K.

    1994-10-01

    Programs for predicting warpage in injection molded parts are relatively new. Commercial software for simulating the flow and cooling stages of injection molding have steadily gained acceptance; however, warpage software is not yet as readily accepted. This study focused on gaining an understanding of the predictive capabilities of the warpage software. The following aspects of this study were unique. (1) Quantitative results were found using a statistically designed set of experiments. (2) Comparisons between experimental and simulation results were made with parts produced in a well-instrumented and controlled injection molding machine. (3) The experimental parts were accurately measured on a coordinate measuring machine with a non-contact laser probe. (4) The effect of part geometry on warpage was investigated.

  1. Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI.

    PubMed

    Mehranian, Abolfazl; Arabi, Hossein; Zaidi, Habib

    2016-04-15

    In quantitative PET/MR imaging, attenuation correction (AC) of PET data is markedly challenged by the need of deriving accurate attenuation maps from MR images. A number of strategies have been developed for MRI-guided attenuation correction with different degrees of success. In this work, we compare the quantitative performance of three generic AC methods, including standard 3-class MR segmentation-based, advanced atlas-registration-based and emission-based approaches in the context of brain time-of-flight (TOF) PET/MRI. Fourteen patients referred for diagnostic MRI and (18)F-FDG PET/CT brain scans were included in this comparative study. For each study, PET images were reconstructed using four different attenuation maps derived from CT-based AC (CTAC) serving as reference, standard 3-class MR-segmentation, atlas-registration and emission-based AC methods. To generate 3-class attenuation maps, T1-weighted MRI images were segmented into background air, fat and soft-tissue classes followed by assignment of constant linear attenuation coefficients of 0, 0.0864 and 0.0975 cm(-1) to each class, respectively. A robust atlas-registration based AC method was developed for pseudo-CT generation using local weighted fusion of atlases based on their morphological similarity to target MR images. Our recently proposed MRI-guided maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm was employed to estimate the attenuation map from TOF emission data. The performance of the different AC algorithms in terms of prediction of bones and quantification of PET tracer uptake was objectively evaluated with respect to reference CTAC maps and CTAC-PET images. Qualitative evaluation showed that the MLAA-AC method could sparsely estimate bones and accurately differentiate them from air cavities. It was found that the atlas-AC method can accurately predict bones with variable errors in defining air cavities. Quantitative assessment of bone extraction accuracy based on

  2. How Accurately Can We Predict Eclipses for Algol? (Poster abstract)

    NASA Astrophysics Data System (ADS)

    Turner, D.

    2016-06-01

    (Abstract only) beta Persei, or Algol, is a very well known eclipsing binary system consisting of a late B-type dwarf that is regularly eclipsed by a GK subgiant every 2.867 days. Eclipses, which last about 8 hours, are regular enough that predictions for times of minima are published in various places, Sky & Telescope magazine and The Observer's Handbook, for example. But eclipse minimum lasts for less than a half hour, whereas subtle mistakes in the current ephemeris for the star can result in predictions that are off by a few hours or more. The Algol system is fairly complex, with the Algol A and Algol B eclipsing system also orbited by Algol C with an orbital period of nearly 2 years. Added to that are complex long-term O-C variations with a periodicity of almost two centuries that, although suggested by Hoffmeister to be spurious, fit the type of light travel time variations expected for a fourth star also belonging to the system. The AB sub-system also undergoes mass transfer events that add complexities to its O-C behavior. Is it actually possible to predict precise times of eclipse minima for Algol months in advance given such complications, or is it better to encourage ongoing observations of the star so that O-C variations can be tracked in real time?

  3. Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data.

    PubMed

    Artico, Sinara; Nardeli, Sarah M; Brilhante, Osmundo; Grossi-de-Sa, Maria Fátima; Alves-Ferreira, Marcio

    2010-03-21

    Normalizing through reference genes, or housekeeping genes, can make more accurate and reliable results from reverse transcription real-time quantitative polymerase chain reaction (qPCR). Recent studies have shown that no single housekeeping gene is universal for all experiments. Thus, suitable reference genes should be the first step of any qPCR analysis. Only a few studies on the identification of housekeeping gene have been carried on plants. Therefore qPCR studies on important crops such as cotton has been hampered by the lack of suitable reference genes. By the use of two distinct algorithms, implemented by geNorm and NormFinder, we have assessed the gene expression of nine candidate reference genes in cotton: GhACT4, GhEF1alpha5, GhFBX6, GhPP2A1, GhMZA, GhPTB, GhGAPC2, GhbetaTUB3 and GhUBQ14. The candidate reference genes were evaluated in 23 experimental samples consisting of six distinct plant organs, eight stages of flower development, four stages of fruit development and in flower verticils. The expression of GhPP2A1 and GhUBQ14 genes were the most stable across all samples and also when distinct plants organs are examined. GhACT4 and GhUBQ14 present more stable expression during flower development, GhACT4 and GhFBX6 in the floral verticils and GhMZA and GhPTB during fruit development. Our analysis provided the most suitable combination of reference genes for each experimental set tested as internal control for reliable qPCR data normalization. In addition, to illustrate the use of cotton reference genes we checked the expression of two cotton MADS-box genes in distinct plant and floral organs and also during flower development. We have tested the expression stabilities of nine candidate genes in a set of 23 tissue samples from cotton plants divided into five different experimental sets. As a result of this evaluation, we recommend the use of GhUBQ14 and GhPP2A1 housekeeping genes as superior references for normalization of gene expression measures in

  4. Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data

    PubMed Central

    2010-01-01

    Background Normalizing through reference genes, or housekeeping genes, can make more accurate and reliable results from reverse transcription real-time quantitative polymerase chain reaction (qPCR). Recent studies have shown that no single housekeeping gene is universal for all experiments. Thus, suitable reference genes should be the first step of any qPCR analysis. Only a few studies on the identification of housekeeping gene have been carried on plants. Therefore qPCR studies on important crops such as cotton has been hampered by the lack of suitable reference genes. Results By the use of two distinct algorithms, implemented by geNorm and NormFinder, we have assessed the gene expression of nine candidate reference genes in cotton: GhACT4, GhEF1α5, GhFBX6, GhPP2A1, GhMZA, GhPTB, GhGAPC2, GhβTUB3 and GhUBQ14. The candidate reference genes were evaluated in 23 experimental samples consisting of six distinct plant organs, eight stages of flower development, four stages of fruit development and in flower verticils. The expression of GhPP2A1 and GhUBQ14 genes were the most stable across all samples and also when distinct plants organs are examined. GhACT4 and GhUBQ14 present more stable expression during flower development, GhACT4 and GhFBX6 in the floral verticils and GhMZA and GhPTB during fruit development. Our analysis provided the most suitable combination of reference genes for each experimental set tested as internal control for reliable qPCR data normalization. In addition, to illustrate the use of cotton reference genes we checked the expression of two cotton MADS-box genes in distinct plant and floral organs and also during flower development. Conclusion We have tested the expression stabilities of nine candidate genes in a set of 23 tissue samples from cotton plants divided into five different experimental sets. As a result of this evaluation, we recommend the use of GhUBQ14 and GhPP2A1 housekeeping genes as superior references for normalization of gene

  5. Quantitative Phase Imaging in a Volume Holographic Microscope

    NASA Astrophysics Data System (ADS)

    Waller, Laura; Luo, Yuan; Barbastathis, George

    2010-04-01

    We demonstrate a method for quantitative phase imaging in a Volume Holographic Microscope (VHM) from a single exposure, describe the properties of the system and show experimental results. The VHM system uses a multiplexed volume hologram (VH) to laterally separate images from different focal planes. This 3D intensity information is then used to solve the transport of intensity (TIE) equation and recover phase quantitatively. We discuss the modifications to the technique that were made in order to give accurate results.

  6. Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

    PubMed

    Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

    2016-06-07

    RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

  7. A gold nanoparticle-based semi-quantitative and quantitative ultrasensitive paper sensor for the detection of twenty mycotoxins

    NASA Astrophysics Data System (ADS)

    Kong, Dezhao; Liu, Liqiang; Song, Shanshan; Suryoprabowo, Steven; Li, Aike; Kuang, Hua; Wang, Libing; Xu, Chuanlai

    2016-02-01

    A semi-quantitative and quantitative multi-immunochromatographic (ICA) strip detection assay was developed for the simultaneous detection of twenty types of mycotoxins from five classes, including zearalenones (ZEAs), deoxynivalenols (DONs), T-2 toxins (T-2s), aflatoxins (AFs), and fumonisins (FBs), in cereal food samples. Sensitive and specific monoclonal antibodies were selected for this assay. The semi-quantitative results were obtained within 20 min by the naked eye, with visual limits of detection for ZEAs, DONs, T-2s, AFs and FBs of 0.1-0.5, 2.5-250, 0.5-1, 0.25-1 and 2.5-10 μg kg-1, and cut-off values of 0.25-1, 5-500, 1-10, 0.5-2.5 and 5-25 μg kg-1, respectively. The quantitative results were obtained using a hand-held strip scan reader, with the calculated limits of detection for ZEAs, DONs, T-2s, AFs and FBs of 0.04-0.17, 0.06-49, 0.15-0.22, 0.056-0.49 and 0.53-1.05 μg kg-1, respectively. The analytical results of spiked samples were in accordance with the accurate content in the simultaneous detection analysis. This newly developed ICA strip assay is suitable for the on-site detection and rapid initial screening of mycotoxins in cereal samples, facilitating both semi-quantitative and quantitative determination.A semi-quantitative and quantitative multi-immunochromatographic (ICA) strip detection assay was developed for the simultaneous detection of twenty types of mycotoxins from five classes, including zearalenones (ZEAs), deoxynivalenols (DONs), T-2 toxins (T-2s), aflatoxins (AFs), and fumonisins (FBs), in cereal food samples. Sensitive and specific monoclonal antibodies were selected for this assay. The semi-quantitative results were obtained within 20 min by the naked eye, with visual limits of detection for ZEAs, DONs, T-2s, AFs and FBs of 0.1-0.5, 2.5-250, 0.5-1, 0.25-1 and 2.5-10 μg kg-1, and cut-off values of 0.25-1, 5-500, 1-10, 0.5-2.5 and 5-25 μg kg-1, respectively. The quantitative results were obtained using a hand-held strip scan

  8. Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy.

    PubMed

    Taradolsirithitikul, Panchita; Sirisomboon, Panmanas; Dachoupakan Sirisomboon, Cheewanun

    2017-03-01

    Ochratoxin A (OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner. PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient (r) of 0.814, a standard error of prediction (SEP and bias of 1.965 µg kg -1 and 0.358 µg kg -1 , respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non-OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%. The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

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

    NASA Astrophysics Data System (ADS)

    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.

  10. Predicting Next Year's Resources--Short-Term Enrollment Forecasting for Accurate Budget Planning. AIR Forum Paper 1978.

    ERIC Educational Resources Information Center

    Salley, Charles D.

    Accurate enrollment forecasts are a prerequisite for reliable budget projections. This is because tuition payments make up a significant portion of a university's revenue, and anticipated revenue is the immediate constraint on current operating expenditures. Accurate forecasts are even more critical to revenue projections when a university's…

  11. Quantitative Species Measurements In Microgravity Combustion Flames

    NASA Technical Reports Server (NTRS)

    Chen, Shin-Juh; Pilgrim, Jeffrey S.; Silver, Joel A.; Piltch, Nancy D.

    2003-01-01

    The capability of models and theories to accurately predict and describe the behavior of low gravity flames can only be verified by quantitative measurements. Although video imaging, simple temperature measurements, and velocimetry methods have provided useful information in many cases, there is still a need for quantitative species measurements. Over the past decade, we have been developing high sensitivity optical absorption techniques to permit in situ, non-intrusive, absolute concentration measurements for both major and minor flames species using diode lasers. This work has helped to establish wavelength modulation spectroscopy (WMS) as an important method for species detection within the restrictions of microgravity-based measurements. More recently, in collaboration with Prof. Dahm at the University of Michigan, a new methodology combining computed flame libraries with a single experimental measurement has allowed us to determine the concentration profiles for all species in a flame. This method, termed ITAC (Iterative Temperature with Assumed Chemistry) was demonstrated for a simple laminar nonpremixed methane-air flame at both 1-g and at 0-g in a vortex ring flame. In this paper, we report additional normal and microgravity experiments which further confirm the usefulness of this approach. We also present the development of a new type of laser. This is an external cavity diode laser (ECDL) which has the unique capability of high frequency modulation as well as a very wide tuning range. This will permit the detection of multiple species with one laser while using WMS detection.

  12. Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

    PubMed

    Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

    2017-12-04

    Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

  13. In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

    PubMed

    Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi

    2009-11-01

    Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.

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

    DTIC Science & Technology

    2011-09-30

    System via Accurate Light Calculations Curtis D. Mobley Sequoia Scientific, Inc. 2700 Richards Road, Suite 107 Bellevue, WA 98005 phone: 425...7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Sequoia Scientific, Inc,2700 Richards Road, Suite 107,Bellevue,WA,98005 8. PERFORMING...EcoLight-S 1.0 Users’ Guide and Technical Documentation. Sequoia Scientific, Inc., Bellevue, WA, 38 pages. Mobley, C. D., 2011. Fast light calculations

  15. Time-Accurate Numerical Prediction of Free Flight Aerodynamics of a Finned Projectile

    DTIC Science & Technology

    2005-09-01

    develop (with fewer dollars) more lethal and effective munitions. The munitions must stay abreast of the latest technology available to our...consuming. Computer simulations can and have provided an effective means of determining the unsteady aerodynamics and flight mechanics of guided projectile...Recently, the time-accurate technique was used to obtain improved results for Magnus moment and roll damping moment of a spinning projectile at transonic

  16. Using an Educational Electronic Documentation System to Help Nursing Students Accurately Identify Nursing Diagnoses

    ERIC Educational Resources Information Center

    Pobocik, Tamara J.

    2013-01-01

    The use of technology and electronic medical records in healthcare has exponentially increased. This quantitative research project used a pretest/posttest design, and reviewed how an educational electronic documentation system helped nursing students to identify the accurate related to statement of the nursing diagnosis for the patient in the case…

  17. Quantitative Analysis of Defects in Silicon. [to predict energy conversion efficiency of silicon samples for solar cells

    NASA Technical Reports Server (NTRS)

    Natesh, R.; Smith, J. M.; Qidwai, H. A.; Bruce, T.

    1979-01-01

    The evaluation and prediction of the conversion efficiency for a variety of silicon samples with differences in structural defects, such as grain boundaries, twin boundaries, precipitate particles, dislocations, etc. are discussed. Quantitative characterization of these structural defects, which were revealed by etching the surface of silicon samples, is performed by using an image analyzer. Due to different crystal growth and fabrication techniques the various types of silicon contain a variety of trace impurity elements and structural defects. The two most important criteria in evaluating the various silicon types for solar cell applications are cost and conversion efficiency.

  18. Quantitative PET Imaging with Novel HER3 Targeted Peptides Selected by Phage Display to Predict Androgen Independent Prostate Cancer Progression

    DTIC Science & Technology

    2017-08-01

    9 4 1. Introduction The subject of this research is the design and testing of a PET imaging agent for the detection and...AWARD NUMBER: W81XWH-16-1-0447 TITLE: Quantitative PET Imaging with Novel HER3-Targeted Peptides Selected by Phage Display to Predict Androgen...MA 02114 REPORT DATE: August 2017 TYPE OF REPORT: Annual PREPARED FOR: U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland

  19. Streamlined system for purifying and quantifying a diverse library of compounds and the effect of compound concentration measurements on the accurate interpretation of biological assay results.

    PubMed

    Popa-Burke, Ioana G; Issakova, Olga; Arroway, James D; Bernasconi, Paul; Chen, Min; Coudurier, Louis; Galasinski, Scott; Jadhav, Ajit P; Janzen, William P; Lagasca, Dennis; Liu, Darren; Lewis, Roderic S; Mohney, Robert P; Sepetov, Nikolai; Sparkman, Darren A; Hodge, C Nicholas

    2004-12-15

    As part of an overall systems approach to generating highly accurate screening data across large numbers of compounds and biological targets, we have developed and implemented streamlined methods for purifying and quantitating compounds at various stages of the screening process, coupled with automated "traditional" storage methods (DMSO, -20 degrees C). Specifically, all of the compounds in our druglike library are purified by LC/MS/UV and are then controlled for identity and concentration in their respective DMSO stock solutions by chemiluminescent nitrogen detection (CLND)/evaporative light scattering detection (ELSD) and MS/UV. In addition, the compound-buffer solutions used in the various biological assays are quantitated by LC/UV/CLND to determine the concentration of compound actually present during screening. Our results show that LC/UV/CLND/ELSD/MS is a widely applicable method that can be used to purify, quantitate, and identify most small organic molecules from compound libraries. The LC/UV/CLND technique is a simple and sensitive method that can be easily and cost-effectively employed to rapidly determine the concentrations of even small amounts of any N-containing compound in aqueous solution. We present data to establish error limits for concentration determination that are well within the overall variability of the screening process. This study demonstrates that there is a significant difference between the predicted amount of soluble compound from stock DMSO solutions following dilution into assay buffer and the actual amount present in assay buffer solutions, even at the low concentrations employed for the assays. We also demonstrate that knowledge of the concentrations of compounds to which the biological target is exposed is critical for accurate potency determinations. Accurate potency values are in turn particularly important for drug discovery, for understanding structure-activity relationships, and for building useful empirical models of

  20. Ultra-fast quantitative imaging using ptychographic iterative engine based digital micro-mirror device

    NASA Astrophysics Data System (ADS)

    Sun, Aihui; Tian, Xiaolin; Kong, Yan; Jiang, Zhilong; Liu, Fei; Xue, Liang; Wang, Shouyu; Liu, Cheng

    2018-01-01

    As a lensfree imaging technique, ptychographic iterative engine (PIE) method can provide both quantitative sample amplitude and phase distributions avoiding aberration. However, it requires field of view (FoV) scanning often relying on mechanical translation, which not only slows down measuring speed, but also introduces mechanical errors decreasing both resolution and accuracy in retrieved information. In order to achieve high-accurate quantitative imaging with fast speed, digital micromirror device (DMD) is adopted in PIE for large FoV scanning controlled by on/off state coding by DMD. Measurements were implemented using biological samples as well as USAF resolution target, proving high resolution in quantitative imaging using the proposed system. Considering its fast and accurate imaging capability, it is believed the DMD based PIE technique provides a potential solution for medical observation and measurements.

  1. Quantitative measures of meniscus extrusion predict incident radiographic knee osteoarthritis – data from the Osteoarthritis Initiative

    PubMed Central

    Emmanuel, K.; Quinn, E.; Niu, J.; Guermazi, A.; Roemer, F.; Wirth, W.; Eckstein, F.; Felson, D.

    2017-01-01

    SUMMARY Objective To test the hypothesis that quantitative measures of meniscus extrusion predict incident radiographic knee osteoarthritis (KOA), prior to the advent of radiographic disease. Methods 206 knees with incident radiographic KOA (Kellgren Lawrence Grade (KLG) 0 or 1 at baseline, developing KLG 2 or greater with a definite osteophyte and joint space narrowing (JSN) grade ≥1 by year 4) were matched to 232 control knees not developing incident KOA. Manual segmentation of the central five slices of the medial and lateral meniscus was performed on coronal 3T DESS MRI and quantitative meniscus position was determined. Cases and controls were compared using conditional logistic regression adjusting for age, sex, BMI, race and clinical site. Sensitivity analyses of early (year [Y] 1/2) and late (Y3/4) incidence was performed. Results Mean medial extrusion distance was significantly greater for incident compared to non-incident knees (1.56 mean ± 1.12 mm SD vs 1.29 ± 0.99 mm; +21%, P < 0.01), so was the percent extrusion area of the medial meniscus (25.8 ± 15.8% vs 22.0 ± 13.5%; +17%, P < 0.05). This finding was consistent for knees restricted to medial incidence. No significant differences were observed for the lateral meniscus in incident medial KOA, or for the tibial plateau coverage between incident and non-incident knees. Restricting the analysis to medial incident KOA at Y1/2 differences were attenuated, but reached significance for extrusion distance, whereas no significant differences were observed at incident KOA in Y3/4. Conclusion Greater medial meniscus extrusion predicts incident radiographic KOA. Early onset KOA showed greater differences for meniscus position between incident and non-incident knees than late onset KOA. PMID:26318658

  2. Highly accurate quantitative spectroscopy of massive stars in the Galaxy

    NASA Astrophysics Data System (ADS)

    Nieva, María-Fernanda; Przybilla, Norbert

    2017-11-01

    Achieving high accuracy and precision in stellar parameter and chemical composition determinations is challenging in massive star spectroscopy. On one hand, the target selection for an unbiased sample build-up is complicated by several types of peculiarities that can occur in individual objects. On the other hand, composite spectra are often not recognized as such even at medium-high spectral resolution and typical signal-to-noise ratios, despite multiplicity among massive stars is widespread. In particular, surveys that produce large amounts of automatically reduced data are prone to oversight of details that turn hazardous for the analysis with techniques that have been developed for a set of standard assumptions applicable to a spectrum of a single star. Much larger systematic errors than anticipated may therefore result because of the unrecognized true nature of the investigated objects, or much smaller sample sizes of objects for the analysis than initially planned, if recognized. More factors to be taken care of are the multiple steps from the choice of instrument over the details of the data reduction chain to the choice of modelling code, input data, analysis technique and the selection of the spectral lines to be analyzed. Only when avoiding all the possible pitfalls, a precise and accurate characterization of the stars in terms of fundamental parameters and chemical fingerprints can be achieved that form the basis for further investigations regarding e.g. stellar structure and evolution or the chemical evolution of the Galaxy. The scope of the present work is to provide the massive star and also other astrophysical communities with criteria to evaluate the quality of spectroscopic investigations of massive stars before interpreting them in a broader context. The discussion is guided by our experiences made in the course of over a decade of studies of massive star spectroscopy ranging from the simplest single objects to multiple systems.

  3. Applications of Microfluidics in Quantitative Biology.

    PubMed

    Bai, Yang; Gao, Meng; Wen, Lingling; He, Caiyun; Chen, Yuan; Liu, Chenli; Fu, Xiongfei; Huang, Shuqiang

    2018-05-01

    Quantitative biology is dedicated to taking advantage of quantitative reasoning and advanced engineering technologies to make biology more predictable. Microfluidics, as an emerging technique, provides new approaches to precisely control fluidic conditions on small scales and collect data in high-throughput and quantitative manners. In this review, the authors present the relevant applications of microfluidics to quantitative biology based on two major categories (channel-based microfluidics and droplet-based microfluidics), and their typical features. We also envision some other microfluidic techniques that may not be employed in quantitative biology right now, but have great potential in the near future. © 2017 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. Biotechnology Journal Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  4. Detection and quantitation of trace phenolphthalein (in pharmaceutical preparations and in forensic exhibits) by liquid chromatography-tandem mass spectrometry, a sensitive and accurate method.

    PubMed

    Sharma, Kakali; Sharma, Shiba P; Lahiri, Sujit C

    2013-01-01

    Phenolphthalein, an acid-base indicator and laxative, is important as a constituent of widely used weight-reducing multicomponent food formulations. Phenolphthalein is an useful reagent in forensic science for the identification of blood stains of suspected victims and for apprehending erring officials accepting bribes in graft or trap cases. The pink-colored alkaline hand washes originating from the phenolphthalein-smeared notes can easily be determined spectrophotometrically. But in many cases, colored solution turns colorless with time, which renders the genuineness of bribe cases doubtful to the judiciary. No method is known till now for the detection and identification of phenolphthalein in colorless forensic exhibits with positive proof. Liquid chromatography-tandem mass spectrometry had been found to be most sensitive, accurate method capable of detection and quantitation of trace phenolphthalein in commercial formulations and colorless forensic exhibits with positive proof. The detection limit of phenolphthalein was found to be 1.66 pg/L or ng/mL, and the calibration curve shows good linearity (r(2) = 0.9974). © 2012 American Academy of Forensic Sciences.

  5. Genotypic tropism testing by massively parallel sequencing: qualitative and quantitative analysis.

    PubMed

    Däumer, Martin; Kaiser, Rolf; Klein, Rolf; Lengauer, Thomas; Thiele, Bernhard; Thielen, Alexander

    2011-05-13

    Inferring viral tropism from genotype is a fast and inexpensive alternative to phenotypic testing. While being highly predictive when performed on clonal samples, sensitivity of predicting CXCR4-using (X4) variants drops substantially in clinical isolates. This is mainly attributed to minor variants not detected by standard bulk-sequencing. Massively parallel sequencing (MPS) detects single clones thereby being much more sensitive. Using this technology we wanted to improve genotypic prediction of coreceptor usage. Plasma samples from 55 antiretroviral-treated patients tested for coreceptor usage with the Monogram Trofile Assay were sequenced with standard population-based approaches. Fourteen of these samples were selected for further analysis with MPS. Tropism was predicted from each sequence with geno2pheno[coreceptor]. Prediction based on bulk-sequencing yielded 59.1% sensitivity and 90.9% specificity compared to the trofile assay. With MPS, 7600 reads were generated on average per isolate. Minorities of sequences with high confidence in CXCR4-usage were found in all samples, irrespective of phenotype. When using the default false-positive-rate of geno2pheno[coreceptor] (10%), and defining a minority cutoff of 5%, the results were concordant in all but one isolate. The combination of MPS and coreceptor usage prediction results in a fast and accurate alternative to phenotypic assays. The detection of X4-viruses in all isolates suggests that coreceptor usage as well as fitness of minorities is important for therapy outcome. The high sensitivity of this technology in combination with a quantitative description of the viral population may allow implementing meaningful cutoffs for predicting response to CCR5-antagonists in the presence of X4-minorities.

  6. Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

    PubMed Central

    2014-01-01

    Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved

  7. Sparse feature selection for classification and prediction of metastasis in endometrial cancer.

    PubMed

    Ahsen, Mehmet Eren; Boren, Todd P; Singh, Nitin K; Misganaw, Burook; Mutch, David G; Moore, Kathleen N; Backes, Floor J; McCourt, Carolyn K; Lea, Jayanthi S; Miller, David S; White, Michael A; Vidyasagar, Mathukumalli

    2017-03-27

    Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.

  8. Predicting the size of individual and group differences on speeded cognitive tasks.

    PubMed

    Chen, Jing; Hale, Sandra; Myerson, Joel

    2007-06-01

    An a priori test of the difference engine model (Myerson, Hale, Zheng, Jenkins, & Widaman, 2003) was conducted using a large, diverse sample of individuals who performed three speeded verbal tasks and three speeded visuospatial tasks. Results demonstrated that, as predicted by the model, the group standard deviation (SD) on any task was proportional to the amount of processing required by that task. Both individual performances as well as those of fast and slow subgroups could be accurately predicted by the model using no free parameters, just an individual or subgroup's mean z-score and the values of theoretical constructs estimated from fits to the group SDs. Taken together, these results are consistent with post hoc analyses reported by Myerson et al. and provide even stronger supporting evidence. In particular, the ability to make quantitative predictions without using any free parameters provides the clearest demonstration to date of the power of an analytic approach on the basis of the difference engine.

  9. Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement.

    PubMed

    Shen, Qijun; Shan, Yanna; Hu, Zhengyu; Chen, Wenhui; Yang, Bing; Han, Jing; Huang, Yanfang; Xu, Wen; Feng, Zhan

    2018-04-30

    To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement. We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set. Significant differences were found between the two groups of patients within variance at V 1.0 and in uniformity at U 1.0 , U 1.8 and U 2.5 . The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85. NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement. • Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods.

  10. Methodological uncertainty in quantitative prediction of human hepatic clearance from in vitro experimental systems.

    PubMed

    Hallifax, D; Houston, J B

    2009-03-01

    Mechanistic prediction of unbound drug clearance from human hepatic microsomes and hepatocytes correlates with in vivo clearance but is both systematically low (10 - 20 % of in vivo clearance) and highly variable, based on detailed assessments of published studies. Metabolic capacity (Vmax) of commercially available human hepatic microsomes and cryopreserved hepatocytes is log-normally distributed within wide (30 - 150-fold) ranges; Km is also log-normally distributed and effectively independent of Vmax, implying considerable variability in intrinsic clearance. Despite wide overlap, average capacity is 2 - 20-fold (dependent on P450 enzyme) greater in microsomes than hepatocytes, when both are normalised (scaled to whole liver). The in vitro ranges contrast with relatively narrow ranges of clearance among clinical studies. The high in vitro variation probably reflects unresolved phenotypical variability among liver donors and practicalities in processing of human liver into in vitro systems. A significant contribution from the latter is supported by evidence of low reproducibility (several fold) of activity in cryopreserved hepatocytes and microsomes prepared from the same cells, between separate occasions of thawing of cells from the same liver. The large uncertainty which exists in human hepatic in vitro systems appears to dominate the overall uncertainty of in vitro-in vivo extrapolation, including uncertainties within scaling, modelling and drug dependent effects. As such, any notion of quantitative prediction of clearance appears severely challenged.

  11. Predictive equation of state method for heavy materials based on the Dirac equation and density functional theory

    NASA Astrophysics Data System (ADS)

    Wills, John M.; Mattsson, Ann E.

    2012-02-01

    Density functional theory (DFT) provides a formally predictive base for equation of state properties. Available approximations to the exchange/correlation functional provide accurate predictions for many materials in the periodic table. For heavy materials however, DFT calculations, using available functionals, fail to provide quantitative predictions, and often fail to be even qualitative. This deficiency is due both to the lack of the appropriate confinement physics in the exchange/correlation functional and to approximations used to evaluate the underlying equations. In order to assess and develop accurate functionals, it is essential to eliminate all other sources of error. In this talk we describe an efficient first-principles electronic structure method based on the Dirac equation and compare the results obtained with this method with other methods generally used. Implications for high-pressure equation of state of relativistic materials are demonstrated in application to Ce and the light actinides. Sandia National Laboratories is a multi-program laboratory managed andoperated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  12. Design and prediction of new anticoagulants as a selective Factor IXa inhibitor via three-dimensional quantitative structure-property relationships of amidinobenzothiophene derivatives.

    PubMed

    Gao, Jia-Suo; Tong, Xu-Peng; Chang, Yi-Qun; He, Yu-Xuan; Mei, Yu-Dan; Tan, Pei-Hong; Guo, Jia-Liang; Liao, Guo-Chao; Xiao, Gao-Keng; Chen, Wei-Min; Zhou, Shu-Feng; Sun, Ping-Hua

    2015-01-01

    Factor IXa (FIXa), a blood coagulation factor, is specifically inhibited at the initiation stage of the coagulation cascade, promising an excellent approach for developing selective and safe anticoagulants. Eighty-four amidinobenzothiophene antithrombotic derivatives targeting FIXa were selected to establish three-dimensional quantitative structure-activity relationship (3D-QSAR) and three-dimensional quantitative structure-selectivity relationship (3D-QSSR) models using comparative molecular field analysis and comparative similarity indices analysis methods. Internal and external cross-validation techniques were investigated as well as region focusing and bootstrapping. The satisfactory q (2) values of 0.753 and 0.770, and r (2) values of 0.940 and 0.965 for 3D-QSAR and 3D-QSSR, respectively, indicated that the models are available to predict both the inhibitory activity and selectivity on FIXa against Factor Xa, the activated status of Factor X. This work revealed that the steric, hydrophobic, and H-bond factors should appropriately be taken into account in future rational design, especially the modifications at the 2'-position of the benzene and the 6-position of the benzothiophene in the R group, providing helpful clues to design more active and selective FIXa inhibitors for the treatment of thrombosis. On the basis of the three-dimensional quantitative structure-property relationships, 16 new potent molecules have been designed and are predicted to be more active and selective than Compound 33, which has the best activity as reported in the literature.

  13. Design and prediction of new anticoagulants as a selective Factor IXa inhibitor via three-dimensional quantitative structure-property relationships of amidinobenzothiophene derivatives

    PubMed Central

    Gao, Jia-Suo; Tong, Xu-Peng; Chang, Yi-Qun; He, Yu-Xuan; Mei, Yu-Dan; Tan, Pei-Hong; Guo, Jia-Liang; Liao, Guo-Chao; Xiao, Gao-Keng; Chen, Wei-Min; Zhou, Shu-Feng; Sun, Ping-Hua

    2015-01-01

    Factor IXa (FIXa), a blood coagulation factor, is specifically inhibited at the initiation stage of the coagulation cascade, promising an excellent approach for developing selective and safe anticoagulants. Eighty-four amidinobenzothiophene antithrombotic derivatives targeting FIXa were selected to establish three-dimensional quantitative structure–activity relationship (3D-QSAR) and three-dimensional quantitative structure–selectivity relationship (3D-QSSR) models using comparative molecular field analysis and comparative similarity indices analysis methods. Internal and external cross-validation techniques were investigated as well as region focusing and bootstrapping. The satisfactory q2 values of 0.753 and 0.770, and r2 values of 0.940 and 0.965 for 3D-QSAR and 3D-QSSR, respectively, indicated that the models are available to predict both the inhibitory activity and selectivity on FIXa against Factor Xa, the activated status of Factor X. This work revealed that the steric, hydrophobic, and H-bond factors should appropriately be taken into account in future rational design, especially the modifications at the 2′-position of the benzene and the 6-position of the benzothiophene in the R group, providing helpful clues to design more active and selective FIXa inhibitors for the treatment of thrombosis. On the basis of the three-dimensional quantitative structure–property relationships, 16 new potent molecules have been designed and are predicted to be more active and selective than Compound 33, which has the best activity as reported in the literature. PMID:25848211

  14. Evaluation of New Zealand's high-seas bottom trawl closures using predictive habitat models and quantitative risk assessment.

    PubMed

    Penney, Andrew J; Guinotte, John M

    2013-01-01

    United Nations General Assembly Resolution 61/105 on sustainable fisheries (UNGA 2007) establishes three difficult questions for participants in high-seas bottom fisheries to answer: 1) Where are vulnerable marine systems (VMEs) likely to occur?; 2) What is the likelihood of fisheries interaction with these VMEs?; and 3) What might qualify as adequate conservation and management measures to prevent significant adverse impacts? This paper develops an approach to answering these questions for bottom trawling activities in the Convention Area of the South Pacific Regional Fisheries Management Organisation (SPRFMO) within a quantitative risk assessment and cost : benefit analysis framework. The predicted distribution of deep-sea corals from habitat suitability models is used to answer the first question. Distribution of historical bottom trawl effort is used to answer the second, with estimates of seabed areas swept by bottom trawlers being used to develop discounting factors for reduced biodiversity in previously fished areas. These are used in a quantitative ecological risk assessment approach to guide spatial protection planning to address the third question. The coral VME likelihood (average, discounted, predicted coral habitat suitability) of existing spatial closures implemented by New Zealand within the SPRFMO area is evaluated. Historical catch is used as a measure of cost to industry in a cost : benefit analysis of alternative spatial closure scenarios. Results indicate that current closures within the New Zealand SPRFMO area bottom trawl footprint are suboptimal for protection of VMEs. Examples of alternative trawl closure scenarios are provided to illustrate how the approach could be used to optimise protection of VMEs under chosen management objectives, balancing protection of VMEs against economic loss to commercial fishers from closure of historically fished areas.

  15. Probabilistic prediction of barrier-island response to hurricanes

    USGS Publications Warehouse

    Plant, Nathaniel G.; Stockdon, Hilary F.

    2012-01-01

    Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

  16. Quantitative structure-activity relationships for predicting potential ecological hazard of organic chemicals for use in regulatory risk assessments.

    PubMed

    Comber, Mike H I; Walker, John D; Watts, Chris; Hermens, Joop

    2003-08-01

    The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed.

  17. A NOVEL TECHNIQUE FOR QUANTITATIVE ESTIMATION OF UPTAKE OF DIESEL EXHAUST PARTICLES BY LUNG CELLS

    EPA Science Inventory

    While airborne particulates like diesel exhaust particulates (DEP) exert significant toxicological effects on lungs, quantitative estimation of accumulation of DEP inside lung cells has not been reported due to a lack of an accurate and quantitative technique for this purpose. I...

  18. Quantitative assessment of hematopoietic chimerism by quantitative real-time polymerase chain reaction of sequence polymorphism systems after hematopoietic stem cell transplantation.

    PubMed

    Qin, Xiao-ying; Li, Guo-xuan; Qin, Ya-zhen; Wang, Yu; Wang, Feng-rong; Liu, Dai-hong; Xu, Lan-ping; Chen, Huan; Han, Wei; Wang, Jing-zhi; Zhang, Xiao-hui; Li, Jin-lan; Li, Ling-di; Liu, Kai-yan; Huang, Xiao-jun

    2011-08-01

    Analysis of changes in recipient and donor hematopoietic cell origin is extremely useful to monitor the effect of hematopoietic stem cell transplantation (HSCT) and sequential adoptive immunotherapy by donor lymphocyte infusions. We developed a sensitive, reliable and rapid real-time PCR method based on sequence polymorphism systems to quantitatively assess the hematopoietic chimerism after HSCT. A panel of 29 selected sequence polymorphism (SP) markers was screened by real-time PCR in 101 HSCT patients with leukemia and other hematological diseases. The chimerism kinetics of bone marrow samples of 8 HSCT patients in remission and relapse situations were followed longitudinally. Recipient genotype discrimination was possible in 97.0% (98 of 101) with a mean number of 2.5 (1-7) informative markers per recipient/donor pair. Using serial dilutions of plasmids containing specific SP markers, the linear correlation (r) of 0.99, the slope between -3.2 and -3.7 and the sensitivity of 0.1% were proved reproducible. By this method, it was possible to very accurately detect autologous signals in the range from 0.1% to 30%. The accuracy of the method in the very important range of autologous signals below 5% was extraordinarily high (standard deviation <1.85%), which might significantly improve detection accuracy of changes in autologous signals early in the post-transplantation course of follow-up. The main advantage of the real-time PCR method over short tandem repeat PCR chimerism assays is the absence of PCR competition and plateau biases, with demonstrated greater sensitivity and linearity. Finally, we prospectively analyzed bone marrow samples of 8 patients who received allografts and presented the chimerism kinetics of remission and relapse situations that illustrated the sensitivity level and the promising clinical application of this method. This SP-based real-time PCR assay provides a rapid, sensitive, and accurate quantitative assessment of mixed chimerism that can

  19. Accurate lithography simulation model based on convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

    2017-07-01

    Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

  20. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

    PubMed Central

    Draper, John; Enot, David P; Parker, David; Beckmann, Manfred; Snowdon, Stuart; Lin, Wanchang; Zubair, Hassan

    2009-01-01

    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 < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI). 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

  1. Towards Accurate Ab Initio Predictions of the Spectrum of Methane

    NASA Technical Reports Server (NTRS)

    Schwenke, David W.; Kwak, Dochan (Technical Monitor)

    2001-01-01

    We have carried out extensive ab initio calculations of the electronic structure of methane, and these results are used to compute vibrational energy levels. We include basis set extrapolations, core-valence correlation, relativistic effects, and Born- Oppenheimer breakdown terms in our calculations. Our ab initio predictions of the lowest lying levels are superb.

  2. Prediction using patient comparison vs. modeling: a case study for mortality prediction.

    PubMed

    Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter

    2016-08-01

    Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.

  3. Is ozone model bias driven by errors in cloud predictions? A quantitative assessment using satellite cloud retrievals in WRF-Chem

    NASA Astrophysics Data System (ADS)

    Ryu, Y. H.; Hodzic, A.; Barré, J.; Descombes, G.; Minnis, P.

    2017-12-01

    Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much of the bias in O3 predictions is caused by inaccurate cloud predictions. This study quantifies the errors in surface O3 predictions associated with clouds in summertime over CONUS using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Cloud fields used for photochemistry are corrected based on satellite cloud retrievals in sensitivity simulations. It is found that the WRF-Chem model is able to detect about 60% of clouds in the right locations and generally underpredicts cloud optical depths. The errors in hourly O3 due to the errors in cloud predictions can be up to 60 ppb. On average in summertime over CONUS, the errors in 8-h average O3 of 1-6 ppb are found to be attributable to those in cloud predictions under cloudy sky conditions. The contribution of changes in photolysis rates due to clouds is found to be larger ( 80 % on average) than that of light-dependent BVOC emissions. The effects of cloud corrections on O­3 are about 2 times larger in VOC-limited than NOx-limited regimes, suggesting that the benefits of accurate cloud predictions would be greater in VOC-limited than NOx-limited regimes.

  4. A Novel Two-Step Hierarchial Quantitative Structure-Activity ...

    EPA Pesticide Factsheets

    Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET ; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments) . The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models t

  5. Quantitative fibrosis parameters highly predict esophageal-gastro varices in primary biliary cirrhosis.

    PubMed

    Wu, Q-M; Zhao, X-Y; You, H

    2016-01-01

    Esophageal-gastro Varices (EGV) may develop in any histological stages of primary biliary cirrhosis (PBC). We aim to establish and validate quantitative fibrosis (qFibrosis) parameters in portal, septal and fibrillar areas as ideal predictors of EGV in PBC patients. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. Among the forty-nine PBC patients with qFibrosis images, twenty-nine PBC patients with both esophagogastroscopy data and qFibrosis data were selected out for EGV prognosis analysis and 44.8% (13/29) of them had EGV. The qFibrosis parameters of collagen percentage and number of crosslink in fibrillar area, short/long/thin strings number and length/width of the strings in septa area were associated with EGV (p < 0.05). Multivariate logistic analysis showed that the collagen percentage in fibrillar area ≥ 3.6% was an independent factor to predict EGV (odds ratio 6.9; 95% confidence interval 1.6-27.4). The area under receiver operating characteristic (ROC), diagnostic sensitivity and specificity was 0.9, 100% and 75% respectively. Collagen percentage in Collagen percentage in the fibrillar area as an independent predictor can highly predict EGV in PBC patients.

  6. Application of quantitative structure activity relationship (QSAR) models to predict ozone toxicity in the lung.

    PubMed

    Kafoury, Ramzi M; Huang, Ming-Ju

    2005-08-01

    The sequence of events leading to ozone-induced airway inflammation is not well known. To elucidate the molecular and cellular events underlying ozone toxicity in the lung, we hypothesized that lipid ozonation products (LOPs) generated by the reaction of ozone with unsaturated fatty acids in the epithelial lining fluid and cell membranes play a key role in mediating ozone-induced airway inflammation. To test our hypothesis, we ozonized 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) and generated LOPs. Confluent human bronchial epithelial cells were exposed to the derivatives of ozonized POPC-9-oxononanoyl, 9-hydroxy-9-hydroperoxynonanoyl, and 8-(5-octyl-1,2,4-trioxolan-3-yl-)octanoyl-at a concentration of 10 muM, and the activity of phospholipases A2 (PLA2), C (PLC), and D (PLD) was measured (1, 0.5, and 1 h, respectively). Quantitative structure-activity relationship (QSAR) models were utilized to predict the biological activity of LOPs in airway epithelial cells. The QSAR results showed a strong correlation between experimental and computed activity (r = 0.97, 0.98, 0.99, for PLA2, PLC, and PLD, respectively). The results indicate that QSAR models can be utilized to predict the biological activity of the various ozone-derived LOP species in the lung. Copyright 2005 Wiley Periodicals, Inc.

  7. Quantitative analysis and prediction of G-quadruplex forming sequences in double-stranded DNA

    PubMed Central

    Kim, Minji; Kreig, Alex; Lee, Chun-Ying; Rube, H. Tomas; Calvert, Jacob; Song, Jun S.; Myong, Sua

    2016-01-01

    Abstract G-quadruplex (GQ) is a four-stranded DNA structure that can be formed in guanine-rich sequences. GQ structures have been proposed to regulate diverse biological processes including transcription, replication, translation and telomere maintenance. Recent studies have demonstrated the existence of GQ DNA in live mammalian cells and a significant number of potential GQ forming sequences in the human genome. We present a systematic and quantitative analysis of GQ folding propensity on a large set of 438 GQ forming sequences in double-stranded DNA by integrating fluorescence measurement, single-molecule imaging and computational modeling. We find that short minimum loop length and the thymine base are two main factors that lead to high GQ folding propensity. Linear and Gaussian process regression models further validate that the GQ folding potential can be predicted with high accuracy based on the loop length distribution and the nucleotide content of the loop sequences. Our study provides important new parameters that can inform the evaluation and classification of putative GQ sequences in the human genome. PMID:27095201

  8. Quantitative magnetic resonance imaging in traumatic brain injury.

    PubMed

    Bigler, E D

    2001-04-01

    Quantitative neuroimaging has now become a well-established method for analyzing magnetic resonance imaging in traumatic brain injury (TBI). A general review of studies that have examined quantitative changes following TBI is presented. The consensus of quantitative neuroimaging studies is that most brain structures demonstrate changes in volume or surface area after injury. The patterns of atrophy are consistent with the generalized nature of brain injury and diffuse axonal injury. Various clinical caveats are provided including how quantitative neuroimaging findings can be used clinically and in predicting rehabilitation outcome. The future of quantitative neuroimaging also is discussed.

  9. New and Accurate Predictive Model for the Efficacy of Extracorporeal Shock Wave Therapy in Managing Patients With Chronic Plantar Fasciitis.

    PubMed

    Yin, Mengchen; Chen, Ni; Huang, Quan; Marla, Anastasia Sulindro; Ma, Junming; Ye, Jie; Mo, Wen

    2017-12-01

    Youden index was .4243, .3003, and .7189, respectively. The Hosmer-Lemeshow test showed a good fitting of the predictive model, with an overall accuracy of 89.6%. This study establishes a new and accurate predictive model for the efficacy of ESWT in managing patients with chronic plantar fasciitis. The use of these parameters, in the form of a predictive model for ESWT efficacy, has the potential to improve decision-making in the application of ESWT. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  10. Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

    PubMed Central

    2016-01-01

    Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

  11. Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships.

    PubMed

    Liu, Zhichao; Kelly, Reagan; Fang, Hong; Ding, Don; Tong, Weida

    2011-07-18

    The primary testing strategy to identify nongenotoxic carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. A slightly more expensive approach based on short-term animal studies with toxicogenomics (TGx) represents another attractive option for this application. Thus, the primary questions are how much better predictive performance using short-term TGx models can be achieved compared to that of QSAR models, and what length of exposure is sufficient for high quality prediction based on TGx. In this study, we developed predictive models for rodent liver carcinogenicity using gene expression data generated from short-term animal models at different time points and QSAR. The study was focused on the prediction of nongenotoxic carcinogenicity since the genotoxic chemicals can be inexpensively removed from further development using various in vitro assays individually or in combination. We identified 62 chemicals whose hepatocarcinogenic potential was available from the National Center for Toxicological Research liver cancer database (NCTRlcdb). The gene expression profiles of liver tissue obtained from rats treated with these chemicals at different time points (1 day, 3 days, and 5 days) are available from the Gene Expression Omnibus (GEO) database. Both TGx and QSAR models were developed on the basis of the same set of chemicals using the same modeling approach, a nearest-centroid method with a minimum redundancy and maximum relevancy-based feature selection with performance assessed using compound-based 5-fold cross-validation. We found that the TGx models outperformed QSAR in every aspect of modeling. For example, the

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

    NASA Astrophysics Data System (ADS)

    Shavalikul, Akamol

    accurate flow characteristics in the NGV domain and the rotor domain with less computational time and computer memory requirements. In contrast, the time accurate flow simulation can predict all unsteady flow characteristics occurring in the turbine stage, but with high computational resource requirements. (Abstract shortened by UMI.)

  13. An instrument for rapid, accurate, determination of fuel moisture content

    Treesearch

    Stephen S. Sackett

    1980-01-01

    Moisture contents of dead and living fuels are key variables in fire behavior. Accurate, real-time fuel moisture data are required for prescribed burning and wildfire behavior predictions. The convection oven method has become the standard for direct fuel moisture content determination. Efforts to quantify fuel moisture through indirect methods have not been...

  14. Predicting Multicomponent Adsorption Isotherms in Open-Metal Site Materials Using Force Field Calculations Based on Energy Decomposed Density Functional Theory.

    PubMed

    Heinen, Jurn; Burtch, Nicholas C; Walton, Krista S; Fonseca Guerra, Célia; Dubbeldam, David

    2016-12-12

    For the design of adsorptive-separation units, knowledge is required of the multicomponent adsorption behavior. Ideal adsorbed solution theory (IAST) breaks down for olefin adsorption in open-metal site (OMS) materials due to non-ideal donor-acceptor interactions. Using a density-function-theory-based energy decomposition scheme, we develop a physically justifiable classical force field that incorporates the missing orbital interactions using an appropriate functional form. Our first-principles derived force field shows greatly improved quantitative agreement with the inflection points, initial uptake, saturation capacity, and enthalpies of adsorption obtained from our in-house adsorption experiments. While IAST fails to make accurate predictions, our improved force field model is able to correctly predict the multicomponent behavior. Our approach is also transferable to other OMS structures, allowing the accurate study of their separation performances for olefins/paraffins and further mixtures involving complex donor-acceptor interactions. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Quantitative X-ray mapping, scatter diagrams and the generation of correction maps to obtain more information about your material

    NASA Astrophysics Data System (ADS)

    Wuhrer, R.; Moran, K.

    2014-03-01

    Quantitative X-ray mapping with silicon drift detectors and multi-EDS detector systems have become an invaluable analysis technique and one of the most useful methods of X-ray microanalysis today. The time to perform an X-ray map has reduced considerably with the ability to map minor and trace elements very accurately due to the larger detector area and higher count rate detectors. Live X-ray imaging can now be performed with a significant amount of data collected in a matter of minutes. A great deal of information can be obtained from X-ray maps. This includes; elemental relationship or scatter diagram creation, elemental ratio mapping, chemical phase mapping (CPM) and quantitative X-ray maps. In obtaining quantitative x-ray maps, we are able to easily generate atomic number (Z), absorption (A), fluorescence (F), theoretical back scatter coefficient (η), and quantitative total maps from each pixel in the image. This allows us to generate an image corresponding to each factor (for each element present). These images allow the user to predict and verify where they are likely to have problems in our images, and are especially helpful to look at possible interface artefacts. The post-processing techniques to improve the quantitation of X-ray map data and the development of post processing techniques for improved characterisation are covered in this paper.

  16. Can We Predict Functional Outcome in Neonates with Hypoxic Ischemic Encephalopathy by the Combination of Neuroimaging and Electroencephalography?

    PubMed Central

    Nanavati, Tania; Seemaladinne, Nirupama; Regier, Michael; Yossuck, Panitan; Pergami, Paola

    2015-01-01

    Background Neonatal hypoxic ischemic encephalopathy (HIE) is a major cause of mortality, morbidity, and long-term neurological deficits. Despite the availability of neuroimaging and neurophysiological testing, tools for accurate early diagnosis and prediction of developmental outcome are still lacking. The goal of this study was to determine if combined use of magnetic resonance imaging (MRI) and electroencephalography (EEG) findings could support outcome prediction. Methods We retrospectively reviewed records of 17 HIE neonates, classified brain MRI and EEG findings based on severity, and assessed clinical outcome up to 48 months. We determined the relation between MRI/EEG findings and clinical outcome. Results We demonstrated a significant relationship between MRI findings and clinical outcome (Fisher’s exact test, p = 0.017). EEG provided no additional information about the outcome beyond that contained in the MRI score. The statistical model for outcome prediction based on random forests suggested that EEG readings at 24 hours and 72 hours could be important variables for outcome prediction, but this needs to be investigated further. Conclusion Caution should be used when discussing prognosis for neonates with mild-to-moderate HIE based on early MR imaging and EEG findings. A robust, quantitative marker of HIE severity that allows for accurate prediction of long-term outcome, particularly for mild-to-moderate cases, is still needed. PMID:25862075

  17. Quantitative, spectrally-resolved intraoperative fluorescence imaging

    PubMed Central

    Valdés, Pablo A.; Leblond, Frederic; Jacobs, Valerie L.; Wilson, Brian C.; Paulsen, Keith D.; Roberts, David W.

    2012-01-01

    Intraoperative visual fluorescence imaging (vFI) has emerged as a promising aid to surgical guidance, but does not fully exploit the potential of the fluorescent agents that are currently available. Here, we introduce a quantitative fluorescence imaging (qFI) approach that converts spectrally-resolved data into images of absolute fluorophore concentration pixel-by-pixel across the surgical field of view (FOV). The resulting estimates are linear, accurate, and precise relative to true values, and spectral decomposition of multiple fluorophores is also achieved. Experiments with protoporphyrin IX in a glioma rodent model demonstrate in vivo quantitative and spectrally-resolved fluorescence imaging of infiltrating tumor margins for the first time. Moreover, we present images from human surgery which detect residual tumor not evident with state-of-the-art vFI. The wide-field qFI technique has broad implications for intraoperative surgical guidance because it provides near real-time quantitative assessment of multiple fluorescent biomarkers across the operative field. PMID:23152935

  18. An Accurate ab initio Quartic Force Field and Vibrational Frequencies for CH4 and Isotopomers

    NASA Technical Reports Server (NTRS)

    Lee, Timothy J.; Martin, Jan M. L.; Taylor, Peter R.

    1995-01-01

    A very accurate ab initio quartic force field for CH4 and its isotopomers is presented. The quartic force field was determined with the singles and doubles coupled-cluster procedure that includes a quasiperturbative estimate of the effects of connected triple excitations, CCSD(T), using the correlation consistent polarized valence triple zeta, cc-pVTZ, basis set. Improved quadratic force constants were evaluated with the correlation consistent polarized valence quadruple zeta, cc-pVQZ, basis set. Fundamental vibrational frequencies are determined using second-order perturbation theory anharmonic analyses. All fundamentals of CH4 and isotopomers for which accurate experimental values exist and for which there is not a large Fermi resonance, are predicted to within +/- 6 cm(exp -1). It is thus concluded that our predictions for the harmonic frequencies and the anharmonic constants are the most accurate estimates available. It is also shown that using cubic and quartic force constants determined with the correlation consistent polarized double zeta, cc-pVDZ, basis set in conjunction with the cc-pVQZ quadratic force constants and equilibrium geometry leads to accurate predictions for the fundamental vibrational frequencies of methane, suggesting that this approach may be a viable alternative for larger molecules. Using CCSD(T), core correlation is found to reduce the CH4 r(e), by 0.0015 A. Our best estimate for r, is 1.0862 +/- 0.0005 A.

  19. Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

    PubMed Central

    Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

    2017-01-01

    Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

  20. Biochemical interpretation of quantitative structure-activity relationships (QSAR) for biodegradation of N-heterocycles: a complementary approach to predict biodegradability.

    PubMed

    Philipp, Bodo; Hoff, Malte; Germa, Florence; Schink, Bernhard; Beimborn, Dieter; Mersch-Sundermann, Volker

    2007-02-15

    Prediction of the biodegradability of organic compounds is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. We combined quantitative structure-activity relationships (QSAR) with the systematic collection of biochemical knowledge to establish rules for the prediction of aerobic biodegradation of N-heterocycles. Validated biodegradation data of 194 N-heterocyclic compounds were analyzed using the MULTICASE-method which delivered two QSAR models based on 17 activating (OSAR 1) and on 16 inactivating molecular fragments (GSAR 2), which were statistically significantly linked to efficient or poor biodegradability, respectively. The percentages of correct classifications were over 99% for both models, and cross-validation resulted in 67.9% (GSAR 1) and 70.4% (OSAR 2) correct predictions. Biochemical interpretation of the activating and inactivating characteristics of the molecular fragments delivered plausible mechanistic interpretations and enabled us to establish the following biodegradation rules: (1) Target sites for amidohydrolases and for cytochrome P450 monooxygenases enhance biodegradation of nonaromatic N-heterocycles. (2) Target sites for molybdenum hydroxylases enhance biodegradation of aromatic N-heterocycles. (3) Target sites for hydratation by an urocanase-like mechanism enhance biodegradation of imidazoles. Our complementary approach represents a feasible strategy for generating concrete rules for the prediction of biodegradability of organic compounds.