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Sample records for accurately predict disease

  1. 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

  2. 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. PMID:27260782

  3. Accurate and Robust Genomic Prediction of Celiac Disease Using Statistical Learning

    PubMed Central

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

    2014-01-01

    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. PMID:24550740

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

    PubMed Central

    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

  5. Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes.

    PubMed

    Zhao, Li; Chen, Yiyun; Bajaj, Amol Onkar; Eblimit, Aiden; Xu, Mingchu; Soens, Zachry T; Wang, Feng; Ge, Zhongqi; Jung, Sung Yun; He, Feng; Li, Yumei; Wensel, Theodore G; Qin, Jun; Chen, Rui

    2016-05-01

    Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases. PMID:26912414

  6. Predict amine solution properties accurately

    SciTech Connect

    Cheng, S.; Meisen, A.; Chakma, A.

    1996-02-01

    Improved process design begins with using accurate physical property data. Especially in the preliminary design stage, physical property data such as density viscosity, thermal conductivity and specific heat can affect the overall performance of absorbers, heat exchangers, reboilers and pump. These properties can also influence temperature profiles in heat transfer equipment and thus control or affect the rate of amine breakdown. Aqueous-amine solution physical property data are available in graphical form. However, it is not convenient to use with computer-based calculations. Developed equations allow improved correlations of derived physical property estimates with published data. Expressions are given which can be used to estimate physical properties of methyldiethanolamine (MDEA), monoethanolamine (MEA) and diglycolamine (DGA) solutions.

  7. The Model for End-stage Liver Disease accurately predicts 90-day liver transplant wait-list mortality in Atlantic Canada

    PubMed Central

    Renfrew, Paul Douglas; Quan, Hude; Doig, Christopher James; Dixon, Elijah; Molinari, Michele

    2011-01-01

    OBJECTIVE: To determine the generalizability of the predictions for 90-day mortality generated by Model for End-stage Liver Disease (MELD) and the serum sodium augmented MELD (MELDNa) to Atlantic Canadian adults with end-stage liver disease awaiting liver transplantation (LT). METHODS: The predictive accuracy of the MELD and the MELDNa was evaluated by measurement of the discrimination and calibration of the respective models’ estimates for the occurrence of 90-day mortality in a consecutive cohort of LT candidates accrued over a five-year period. Accuracy of discrimination was measured by the area under the ROC curves. Calibration accuracy was evaluated by comparing the observed and model-estimated incidences of 90-day wait-list failure for the total cohort and within quantiles of risk. RESULTS: The area under the ROC curve for the MELD was 0.887 (95% CI 0.705 to 0.978) – consistent with very good accuracy of discrimination. The area under the ROC curve for the MELDNa was 0.848 (95% CI 0.681 to 0.965). The observed incidence of 90-day wait-list mortality in the validation cohort was 7.9%, which was not significantly different from the MELD estimate of 6.6% (95% CI 4.9% to 8.4%; P=0.177) or the MELDNa estimate of 5.8% (95% CI 3.5% to 8.0%; P=0.065). Global goodness-of-fit testing found no evidence of significant lack of fit for either model (Hosmer-Lemeshow χ2 [df=3] for MELD 2.941, P=0.401; for MELDNa 2.895, P=0.414). CONCLUSION: Both the MELD and the MELDNa accurately predicted the occurrence of 90-day wait-list mortality in the study cohort and, therefore, are generalizable to Atlantic Canadians with end-stage liver disease awaiting LT. PMID:21876856

  8. New model accurately predicts reformate composition

    SciTech Connect

    Ancheyta-Juarez, J.; Aguilar-Rodriguez, E. )

    1994-01-31

    Although naphtha reforming is a well-known process, the evolution of catalyst formulation, as well as new trends in gasoline specifications, have led to rapid evolution of the process, including: reactor design, regeneration mode, and operating conditions. Mathematical modeling of the reforming process is an increasingly important tool. It is fundamental to the proper design of new reactors and revamp of existing ones. Modeling can be used to optimize operating conditions, analyze the effects of process variables, and enhance unit performance. Instituto Mexicano del Petroleo has developed a model of the catalytic reforming process that accurately predicts reformate composition at the higher-severity conditions at which new reformers are being designed. The new AA model is more accurate than previous proposals because it takes into account the effects of temperature and pressure on the rate constants of each chemical reaction.

  9. Accurate Prediction of Docked Protein Structure Similarity.

    PubMed

    Akbal-Delibas, Bahar; Pomplun, Marc; Haspel, Nurit

    2015-09-01

    One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin. PMID:26335807

  10. Race-specific genetic risk score is more accurate than nonrace-specific genetic risk score for predicting prostate cancer and high-grade diseases

    PubMed Central

    Na, Rong; Ye, Dingwei; Qi, Jun; Liu, Fang; Lin, Xiaoling; Helfand, Brian T; Brendler, Charles B; Conran, Carly; Gong, Jian; Wu, Yishuo; Gao, Xu; Chen, Yaqing; Zheng, S Lilly; Mo, Zengnan; Ding, Qiang; Sun, Yinghao; Xu, Jianfeng

    2016-01-01

    Genetic risk score (GRS) based on disease risk-associated single nucleotide polymorphisms (SNPs) is an informative tool that can be used to provide inherited information for specific diseases in addition to family history. However, it is still unknown whether only SNPs that are implicated in a specific racial group should be used when calculating GRSs. The objective of this study is to compare the performance of race-specific GRS and nonrace-specific GRS for predicting prostate cancer (PCa) among 1338 patients underwent prostate biopsy in Shanghai, China. A race-specific GRS was calculated with seven PCa risk-associated SNPs implicated in East Asians (GRS7), and a nonrace-specific GRS was calculated based on 76 PCa risk-associated SNPs implicated in at least one racial group (GRS76). The means of GRS7 and GRS76 were 1.19 and 1.85, respectively, in the study population. Higher GRS7 and GRS76 were independent predictors for PCa and high-grade PCa in univariate and multivariate analyses. GRS7 had a better area under the receiver-operating curve (AUC) than GRS76 for discriminating PCa (0.602 vs 0.573) and high-grade PCa (0.603 vs 0.575) but did not reach statistical significance. GRS7 had a better (up to 13% at different cutoffs) positive predictive value (PPV) than GRS76. In conclusion, a race-specific GRS is more robust and has a better performance when predicting PCa in East Asian men than a GRS calculated using SNPs that are not shown to be associated with East Asians. PMID:27140652

  11. Race-specific genetic risk score is more accurate than nonrace-specific genetic risk score for predicting prostate cancer and high-grade diseases.

    PubMed

    Na, Rong; Ye, Dingwei; Qi, Jun; Liu, Fang; Lin, Xiaoling; Helfand, Brian T; Brendler, Charles B; Conran, Carly; Gong, Jian; Wu, Yishuo; Gao, Xu; Chen, Yaqing; Zheng, S Lilly; Mo, Zengnan; Ding, Qiang; Sun, Yinghao; Xu, Jianfeng

    2016-01-01

    Genetic risk score (GRS) based on disease risk-associated single nucleotide polymorphisms (SNPs) is an informative tool that can be used to provide inherited information for specific diseases in addition to family history. However, it is still unknown whether only SNPs that are implicated in a specific racial group should be used when calculating GRSs. The objective of this study is to compare the performance of race-specific GRS and nonrace-specific GRS for predicting prostate cancer (PCa) among 1338 patients underwent prostate biopsy in Shanghai, China. A race-specific GRS was calculated with seven PCa risk-associated SNPs implicated in East Asians (GRS7), and a nonrace-specific GRS was calculated based on 76 PCa risk-associated SNPs implicated in at least one racial group (GRS76). The means of GRS7 and GRS76 were 1.19 and 1.85, respectively, in the study population. Higher GRS7 and GRS76 were independent predictors for PCa and high-grade PCa in univariate and multivariate analyses. GRS7 had a better area under the receiver-operating curve (AUC) than GRS76 for discriminating PCa (0.602 vs 0.573) and high-grade PCa (0.603 vs 0.575) but did not reach statistical significance. GRS7 had a better (up to 13% at different cutoffs) positive predictive value (PPV) than GRS76. In conclusion, a race-specific GRS is more robust and has a better performance when predicting PCa in East Asian men than a GRS calculated using SNPs that are not shown to be associated with East Asians. PMID:27140652

  12. Predicting accurate probabilities with a ranking loss

    PubMed Central

    Menon, Aditya Krishna; Jiang, Xiaoqian J; Vembu, Shankar; Elkan, Charles; Ohno-Machado, Lucila

    2013-01-01

    In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric technique offers both good ranking and regression performance, and models a richer set of probability distributions than statistical workhorses such as logistic regression. We provide experimental results that show the effectiveness of this technique on real-world applications of probability prediction. PMID:25285328

  13. You Can Accurately Predict Land Acquisition Costs.

    ERIC Educational Resources Information Center

    Garrigan, Richard

    1967-01-01

    Land acquisition costs were tested for predictability based upon the 1962 assessed valuations of privately held land acquired for campus expansion by the University of Wisconsin from 1963-1965. By correlating the land acquisition costs of 108 properties acquired during the 3 year period with--(1) the assessed value of the land, (2) the assessed…

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

    PubMed

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

    2015-01-01

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. PMID:26198229

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

    PubMed Central

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

    2015-01-01

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. PMID:26198229

  16. Predicting the course of disease.

    PubMed

    Krakauer, H; Jacoby, I

    1993-01-01

    The ability to predict the course of disease and the effect of interventions is critical to effective medical practice and health care management. In this analysis, we sought to test whether available clinical data and analytic methodologies can be used to accurately predict the time course of the probability of death after hospital admission and the probability of readmission following discharge for patients with acute myocardial infarction or pulmonary disease. We grouped patients by selected physiologic characteristics and made time-to-event predictions using multiple regression models. These predictions were compared with observed probabilities calculated using the actuarial or life-table method. Predictions made with the Bailey-Makeham model generally replicated observed experience. They accurately accounted for substantial differences in the patterns of death and readmission and accurately delineated the effects of therapies, after adjustment for patient risk. These results were validated by analyses of resampled populations that differed in case mix from the source population. We believe that using such models to project the course of disease and the effects of treatment on that course in defined classes of patients should facilitate the development of practice guidelines for patient care and the management of health care resources. PMID:8314601

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

    NASA Astrophysics Data System (ADS)

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

    2010-10-01

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

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

    DOE PAGESBeta

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; et al

    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

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

    SciTech Connect

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    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 charged 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.

  20. Autonomic disorders predicting Parkinson disease

    PubMed Central

    Palma, Jose-Alberto; Kaufmann, Horacio

    2014-01-01

    It is now well recognized that there is a premotor phase of Parkinson disease with hyposmia and REM sleep behavior disorder caused by degeneration of specific CNS neurons. Most patients with PD describe autonomic symptoms at the time of diagnosis suggesting that these features may have potential sensitivity as clinical biomarkers of the premotor phase. The recognition that damage to peripheral autonomic neurons is present in the early stages of Parkinson disease has led to a search for specific abnormalities in autonomic function that could serve as predictive biomarkers. There is evidence that constipation, urinary and sexual dysfunction and more recently decreased cardiac chronotropic response during exercise, are part of the premotor parkinsonian phenotype. The sensitivity and specificity of these features has yet to be accurately assessed. We briefly review the evidence for autonomic dysfunction as biomarkers of premotor PD. PMID:24262198

  1. Passive samplers accurately predict PAH levels in resident crayfish.

    PubMed

    Paulik, L Blair; Smith, Brian W; Bergmann, Alan J; Sower, Greg J; Forsberg, Norman D; Teeguarden, Justin G; Anderson, Kim A

    2016-02-15

    Contamination of resident aquatic organisms is a major concern for environmental risk assessors. However, collecting organisms to estimate risk is often prohibitively time and resource-intensive. Passive sampling accurately estimates resident organism contamination, and it saves time and resources. This study used low density polyethylene (LDPE) passive water samplers to predict polycyclic aromatic hydrocarbon (PAH) levels in signal crayfish, Pacifastacus leniusculus. Resident crayfish were collected at 5 sites within and outside of the Portland Harbor Superfund Megasite (PHSM) in the Willamette River in Portland, Oregon. LDPE deployment was spatially and temporally paired with crayfish collection. Crayfish visceral and tail tissue, as well as water-deployed LDPE, were extracted and analyzed for 62 PAHs using GC-MS/MS. Freely-dissolved concentrations (Cfree) of PAHs in water were calculated from concentrations in LDPE. Carcinogenic risks were estimated for all crayfish tissues, using benzo[a]pyrene equivalent concentrations (BaPeq). ∑PAH were 5-20 times higher in viscera than in tails, and ∑BaPeq were 6-70 times higher in viscera than in tails. Eating only tail tissue of crayfish would therefore significantly reduce carcinogenic risk compared to also eating viscera. Additionally, PAH levels in crayfish were compared to levels in crayfish collected 10 years earlier. PAH levels in crayfish were higher upriver of the PHSM and unchanged within the PHSM after the 10-year period. Finally, a linear regression model predicted levels of 34 PAHs in crayfish viscera with an associated R-squared value of 0.52 (and a correlation coefficient of 0.72), using only the Cfree PAHs in water. On average, the model predicted PAH concentrations in crayfish tissue within a factor of 2.4 ± 1.8 of measured concentrations. This affirms that passive water sampling accurately estimates PAH contamination in crayfish. Furthermore, the strong predictive ability of this simple model suggests

  2. A new generalized correlation for accurate vapor pressure prediction

    NASA Astrophysics Data System (ADS)

    An, Hui; Yang, Wenming

    2012-08-01

    An accurate knowledge of the vapor pressure of organic liquids is very important for the oil and gas processing operations. In combustion modeling, the accuracy of numerical predictions is also highly dependent on the fuel properties such as vapor pressure. In this Letter, a new generalized correlation is proposed based on the Lee-Kesler's method where a fuel dependent parameter 'A' is introduced. The proposed method only requires the input parameters of critical temperature, normal boiling temperature and the acentric factor of the fluid. With this method, vapor pressures have been calculated and compared with the data reported in data compilation for 42 organic liquids over 1366 data points, and the overall average absolute percentage deviation is only 1.95%.

  3. Mouse models of human AML accurately predict chemotherapy response

    PubMed Central

    Zuber, Johannes; Radtke, Ina; Pardee, Timothy S.; Zhao, Zhen; Rappaport, Amy R.; Luo, Weijun; McCurrach, Mila E.; Yang, Miao-Miao; Dolan, M. Eileen; Kogan, Scott C.; Downing, James R.; Lowe, Scott W.

    2009-01-01

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients. PMID:19339691

  4. Mouse models of human AML accurately predict chemotherapy response.

    PubMed

    Zuber, Johannes; Radtke, Ina; Pardee, Timothy S; Zhao, Zhen; Rappaport, Amy R; Luo, Weijun; McCurrach, Mila E; Yang, Miao-Miao; Dolan, M Eileen; Kogan, Scott C; Downing, James R; Lowe, Scott W

    2009-04-01

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients. PMID:19339691

  5. Turbulence Models for Accurate Aerothermal Prediction in Hypersonic Flows

    NASA Astrophysics Data System (ADS)

    Zhang, Xiang-Hong; Wu, Yi-Zao; Wang, Jiang-Feng

    Accurate description of the aerodynamic and aerothermal environment is crucial to the integrated design and optimization for high performance hypersonic vehicles. In the simulation of aerothermal environment, the effect of viscosity is crucial. The turbulence modeling remains a major source of uncertainty in the computational prediction of aerodynamic forces and heating. In this paper, three turbulent models were studied: the one-equation eddy viscosity transport model of Spalart-Allmaras, the Wilcox k-ω model and the Menter SST model. For the k-ω model and SST model, the compressibility correction, press dilatation and low Reynolds number correction were considered. The influence of these corrections for flow properties were discussed by comparing with the results without corrections. In this paper the emphasis is on the assessment and evaluation of the turbulence models in prediction of heat transfer as applied to a range of hypersonic flows with comparison to experimental data. This will enable establishing factor of safety for the design of thermal protection systems of hypersonic vehicle.

  6. Accurate Prediction of Binding Thermodynamics for DNA on Surfaces

    PubMed Central

    Vainrub, Arnold; Pettitt, B. Montgomery

    2011-01-01

    For DNA mounted on surfaces for microarrays, microbeads and nanoparticles, the nature of the random attachment of oligonucleotide probes to an amorphous surface gives rise to a locally inhomogeneous probe density. These fluctuations of the probe surface density are inherent to all common surface or bead platforms, regardless if they exploit either an attachment of pre-synthesized probes or probes synthesized in situ on the surface. Here, we demonstrate for the first time the crucial role of the probe surface density fluctuations in performance of DNA arrays. We account for the density fluctuations with a disordered two-dimensional surface model and derive the corresponding array hybridization isotherm that includes a counter-ion screened electrostatic repulsion between the assayed DNA and probe array. The calculated melting curves are in excellent agreement with published experimental results for arrays with both pre-synthesized and in-situ synthesized oligonucleotide probes. The approach developed allows one to accurately predict the melting curves of DNA arrays using only the known sequence dependent hybridization enthalpy and entropy in solution and the experimental macroscopic surface density of probes. This opens the way to high precision theoretical design and optimization of probes and primers in widely used DNA array-based high-throughput technologies for gene expression, genotyping, next-generation sequencing, and surface polymerase extension. PMID:21972932

  7. 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

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

    PubMed

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

    2016-05-01

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  10. IRIS: Towards an Accurate and Fast Stage Weight Prediction Method

    NASA Astrophysics Data System (ADS)

    Taponier, V.; Balu, A.

    2002-01-01

    The knowledge of the structural mass fraction (or the mass ratio) of a given stage, which affects the performance of a rocket, is essential for the analysis of new or upgraded launchers or stages, whose need is increased by the quick evolution of the space programs and by the necessity of their adaptation to the market needs. The availability of this highly scattered variable, ranging between 0.05 and 0.15, is of primary importance at the early steps of the preliminary design studies. At the start of the staging and performance studies, the lack of frozen weight data (to be obtained later on from propulsion, trajectory and sizing studies) leads to rely on rough estimates, generally derived from printed sources and adapted. When needed, a consolidation can be acquired trough a specific analysis activity involving several techniques and implying additional effort and time. The present empirical approach allows thus to get approximated values (i.e. not necessarily accurate or consistent), inducing some result inaccuracy as well as, consequently, difficulties of performance ranking for a multiple option analysis, and an increase of the processing duration. This forms a classical harsh fact of the preliminary design system studies, insufficiently discussed to date. It appears therefore highly desirable to have, for all the evaluation activities, a reliable, fast and easy-to-use weight or mass fraction prediction method. Additionally, the latter should allow for a pre selection of the alternative preliminary configurations, making possible a global system approach. For that purpose, an attempt at modeling has been undertaken, whose objective was the determination of a parametric formulation of the mass fraction, to be expressed from a limited number of parameters available at the early steps of the project. It is based on the innovative use of a statistical method applicable to a variable as a function of several independent parameters. A specific polynomial generator

  11. Accurately Predicting Complex Reaction Kinetics from First Principles

    NASA Astrophysics Data System (ADS)

    Green, William

    Many important systems contain a multitude of reactive chemical species, some of which react on a timescale faster than collisional thermalization, i.e. they never achieve a Boltzmann energy distribution. Usually it is impossible to fully elucidate the processes by experiments alone. Here we report recent progress toward predicting the time-evolving composition of these systems a priori: how unexpected reactions can be discovered on the computer, how reaction rates are computed from first principles, and how the many individual reactions are efficiently combined into a predictive simulation for the whole system. Some experimental tests of the a priori predictions are also presented.

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

    PubMed

    Nam, Ki-Uk; Hong, Jongrak

    2015-11-01

    The authors assessed whether virtual surgery, performed with a soft tissue prediction program, could correctly simulate the actual surgical outcome, focusing on soft tissue movement. Preoperative and postoperative computed tomography (CT) data for 29 patients, who had undergone orthognathic surgery, were obtained and analyzed using the Simplant Pro software. The program made a predicted soft tissue image (A) based on presurgical CT data. After the operation, we obtained actual postoperative CT data and an actual soft tissue image (B) was generated. Finally, the 2 images (A and B) were superimposed and analyzed differences between the A and B. Results were grouped in 2 classes: absolute values and vector values. In the absolute values, the left mouth corner was the most significant error point (2.36 mm). The right mouth corner (2.28 mm), labrale inferius (2.08 mm), and the pogonion (2.03 mm) also had significant errors. In vector values, prediction of the right-left side had a left-sided tendency, the superior-inferior had a superior tendency, and the anterior-posterior showed an anterior tendency. As a result, with this program, the position of points tended to be located more left, anterior, and superior than the "real" situation. There is a need to improve the prediction accuracy for soft tissue images. Such software is particularly valuable in predicting craniofacial soft tissues landmarks, such as the pronasale. With this software, landmark positions were most inaccurate in terms of anterior-posterior predictions. PMID:26594988

  13. 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. PMID:24524947

  14. 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.

  15. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    PubMed Central

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

    2016-01-01

    Objective: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. Methods: In this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3–5 until 180 days. Results: Eight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p < 0.001). Specificity and sensitivity were not significantly affected by targeted temperature or sedation. A benign EEG was found in 1% of the patients with a poor outcome. Conclusions: Highly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome. PMID:26865516

  16. 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?

  17. 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

  18. 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. PMID:26998518

  19. Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer's Disease.

    PubMed

    Zheng, Weihao; Yao, Zhijun; Hu, Bin; Gao, Xiang; Cai, Hanshu; Moore, Philip

    2015-01-01

    Brain network occupies an important position in representing abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Currently, most studies only focused on morphological features of regions of interest without exploring the interregional alterations. In order to investigate the potential discriminative power of a morphological network in AD diagnosis and to provide supportive evidence on the feasibility of an individual structural network study, we propose a novel approach of extracting the correlative features from magnetic resonance imaging, which consists of a two-step approach for constructing an individual thickness network with low computational complexity. Firstly, multi-distance combination is utilized for accurate evaluation of between-region dissimilarity; and then the dissimilarity is transformed to connectivity via calculation of correlation function. An evaluation of the proposed approach has been conducted with 189 normal controls, 198 MCI subjects, and 163 AD patients using machine learning techniques. Results show that the observed correlative feature suggests significant promotion in classification performance compared with cortical thickness, with accuracy of 89.88% and area of 0.9588 under receiver operating characteristic curve. We further improved the performance by integrating both thickness and apolipoprotein E ɛ4 allele information with correlative features. New achieved accuracies are 92.11% and 79.37% in separating AD from normal controls and AD converters from non-converters, respectively. Differences between using diverse distance measurements and various correlation transformation functions are also discussed to explore an optimal way for network establishment. PMID:26444768

  20. 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

  1. Accurate predictions for the production of vaporized water

    SciTech Connect

    Morin, E.; Montel, F.

    1995-12-31

    The production of water vaporized in the gas phase is controlled by the local conditions around the wellbore. The pressure gradient applied to the formation creates a sharp increase of the molar water content in the hydrocarbon phase approaching the well; this leads to a drop in the pore water saturation around the wellbore. The extent of the dehydrated zone which is formed is the key controlling the bottom-hole content of vaporized water. The maximum water content in the hydrocarbon phase at a given pressure, temperature and salinity is corrected by capillarity or adsorption phenomena depending on the actual water saturation. Describing the mass transfer of the water between the hydrocarbon phases and the aqueous phase into the tubing gives a clear idea of vaporization effects on the formation of scales. Field example are presented for gas fields with temperatures ranging between 140{degrees}C and 180{degrees}C, where water vaporization effects are significant. Conditions for salt plugging in the tubing are predicted.

  2. Change in BMI Accurately Predicted by Social Exposure to Acquaintances

    PubMed Central

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

    2013-01-01

    Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R2. This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends. PMID

  3. Towards an expert system for accurate diagnosis and progress monitoring of Parkinson's disease.

    PubMed

    Alexiou, Athanasios; Psiha, Maria; Vlamos, Panayiotis

    2015-01-01

    While Parkinson's disease is a chronic and progressive movement disorder, no one can predict which symptoms will affect an individual patient. At the present time there is no cure for Parkinson's disease but instead a variety of alternative treatments provide relief from the symptoms. Due to these unpromising factors, we propose a new multi-scale ontology-based modeling technology for the accurate diagnosis of Parkinson's disease and its progress monitoring. The proposed model will be used to assess the status of the patient with PD corresponding treatments using a multilayer neural network. The proposed tool also aims to identify new associated physical and biological biomarkers from heterogeneous patients' data. The architecture of this expert system and its implementation in Protégé is presented in this paper. PMID:25416985

  4. Abdominal MRI without Enteral Contrast Accurately Detects Intestinal Fibrostenosis in Patients with Inflammatory Bowel Disease.

    PubMed

    Fisher, Jeremy G; Kalb, Bobby; Martin, Diego; Dhere, Tanvi; Perez, Sebastian D; Srinivasan, Jahnavi K

    2015-11-01

    Patients with inflammatory bowel disease (IBD) presenting for surgical evaluation require thorough small bowel surveillance as it improves accuracy of diagnosis (ulcerative colitis versus Crohn's) and differentiates those who may respond to nonoperative therapy, preserving bowel length. MRI has not been validated conclusively against histopathology in IBD. Most protocols require enteral contrast. This study aimed to 1) evaluate the accuracy of MRI for inflammation, fibrosis, and extraluminal complications and 2) compare MRI without enteral contrast to standard magnetic resonance enterography. Adults with Crohn's disease or ulcerative colitis who underwent abdominal MRI and surgery were retrospectively reviewed. Of 65 patients evaluated, 55 met inclusion criteria. Overall sensitivity and specificity of MRI for disease involvement localized by segment were 93 per cent (95% confidence interval = 89.4-95.0) and 95 per cent (95% confidence interval = 92.3-97.0), respectively (positive predictive value was 86%, negative predictive value was 98%). Sensitivity and specificity between MRI with and without oral and rectal contrast were similar (96% vs 91% and 99% vs 94%, P > 0.10). As were positive predictive value and negative predictive value (85% vs 96%, P = 0.16; 97% vs 99%, P = 0.42). Magnetic resonance is highly sensitive and specific for localized disease involvement and extraluminal abdominal sequelae of IBD. It accurately differentiates patients who have chronic transmural (fibrotic) disease and thus may require an operation from those with acute inflammation, whose symptoms may improve with aggressive medical therapy alone. MRI without contrast had comparable diagnostic yield to standard magnetic resonance enterography. PMID:26672581

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

    SciTech Connect

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

    2009-02-01

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

  6. Development and Application of Chronic Disease Risk Prediction Models

    PubMed Central

    Oh, Sun Min; Stefani, Katherine M.

    2014-01-01

    Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea. PMID:24954311

  7. New consensus definition for acute kidney injury accurately predicts 30-day mortality in cirrhosis with infection

    PubMed Central

    Wong, Florence; O’Leary, Jacqueline G; Reddy, K Rajender; Patton, Heather; Kamath, Patrick S; Fallon, Michael B; Garcia-Tsao, Guadalupe; Subramanian, Ram M.; Malik, Raza; Maliakkal, Benedict; Thacker, Leroy R; Bajaj, Jasmohan S

    2015-01-01

    Background & Aims A consensus conference proposed that cirrhosis-associated acute kidney injury (AKI) be defined as an increase in serum creatinine by >50% from the stable baseline value in <6 months or by ≥0.3mg/dL in <48 hrs. We prospectively evaluated the ability of these criteria to predict mortality within 30 days among hospitalized patients with cirrhosis and infection. Methods 337 patients with cirrhosis admitted with or developed an infection in hospital (56% men; 56±10 y old; model for end-stage liver disease score, 20±8) were followed. We compared data on 30-day mortality, hospital length-of-stay, and organ failure between patients with and without AKI. Results 166 (49%) developed AKI during hospitalization, based on the consensus criteria. Patients who developed AKI had higher admission Child-Pugh (11.0±2.1 vs 9.6±2.1; P<.0001), and MELD scores (23±8 vs17±7; P<.0001), and lower mean arterial pressure (81±16mmHg vs 85±15mmHg; P<.01) than those who did not. Also higher amongst patients with AKI were mortality in ≤30 days (34% vs 7%), intensive care unit transfer (46% vs 20%), ventilation requirement (27% vs 6%), and shock (31% vs 8%); AKI patients also had longer hospital stays (17.8±19.8 days vs 13.3±31.8 days) (all P<.001). 56% of AKI episodes were transient, 28% persistent, and 16% resulted in dialysis. Mortality was 80% among those without renal recovery, higher compared to partial (40%) or complete recovery (15%), or AKI-free patients (7%; P<.0001). Conclusions 30-day mortality is 10-fold higher among infected hospitalized cirrhotic patients with irreversible AKI than those without AKI. The consensus definition of AKI accurately predicts 30-day mortality, length of hospital stay, and organ failure. PMID:23999172

  8. Cas9-chromatin binding information enables more accurate CRISPR off-target prediction

    PubMed Central

    Singh, Ritambhara; Kuscu, Cem; Quinlan, Aaron; Qi, Yanjun; Adli, Mazhar

    2015-01-01

    The CRISPR system has become a powerful biological tool with a wide range of applications. However, improving targeting specificity and accurately predicting potential off-targets remains a significant goal. Here, we introduce a web-based CRISPR/Cas9 Off-target Prediction and Identification Tool (CROP-IT) that performs improved off-target binding and cleavage site predictions. Unlike existing prediction programs that solely use DNA sequence information; CROP-IT integrates whole genome level biological information from existing Cas9 binding and cleavage data sets. Utilizing whole-genome chromatin state information from 125 human cell types further enhances its computational prediction power. Comparative analyses on experimentally validated datasets show that CROP-IT outperforms existing computational algorithms in predicting both Cas9 binding as well as cleavage sites. With a user-friendly web-interface, CROP-IT outputs scored and ranked list of potential off-targets that enables improved guide RNA design and more accurate prediction of Cas9 binding or cleavage sites. PMID:26032770

  9. Accurate rotor loads prediction using the FLAP (Force and Loads Analysis Program) dynamics code

    SciTech Connect

    Wright, A.D.; Thresher, R.W.

    1987-10-01

    Accurately predicting wind turbine blade loads and response is very important in predicting the fatigue life of wind turbines. There is a clear need in the wind turbine community for validated and user-friendly structural dynamics codes for predicting blade loads and response. At the Solar Energy Research Institute (SERI), a Force and Loads Analysis Program (FLAP) has been refined and validated and is ready for general use. Currently, FLAP is operational on an IBM-PC compatible computer and can be used to analyze both rigid- and teetering-hub configurations. The results of this paper show that FLAP can be used to accurately predict the deterministic loads for rigid-hub rotors. This paper compares analytical predictions to field test measurements for a three-bladed, upwind turbine with a rigid-hub configuration. The deterministic loads predicted by FLAP are compared with 10-min azimuth averages of blade root flapwise bending moments for different wind speeds. 6 refs., 12 figs., 3 tabs.

  10. 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

  11. An accurate modeling, simulation, and analysis tool for predicting and estimating Raman LIDAR system performance

    NASA Astrophysics Data System (ADS)

    Grasso, Robert J.; Russo, Leonard P.; Barrett, John L.; Odhner, Jefferson E.; Egbert, Paul I.

    2007-09-01

    BAE Systems presents the results of a program to model the performance of Raman LIDAR systems for the remote detection of atmospheric gases, air polluting hydrocarbons, chemical and biological weapons, and other molecular species of interest. Our model, which integrates remote Raman spectroscopy, 2D and 3D LADAR, and USAF atmospheric propagation codes permits accurate determination of the performance of a Raman LIDAR system. The very high predictive performance accuracy of our model is due to the very accurate calculation of the differential scattering cross section for the specie of interest at user selected wavelengths. We show excellent correlation of our calculated cross section data, used in our model, with experimental data obtained from both laboratory measurements and the published literature. In addition, the use of standard USAF atmospheric models provides very accurate determination of the atmospheric extinction at both the excitation and Raman shifted wavelengths.

  12. Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter.

    PubMed

    Samsudin, Firdaus; Parker, Joanne L; Sansom, Mark S P; Newstead, Simon; Fowler, Philip W

    2016-02-18

    Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the ?-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepTSt, and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families. PMID:27028887

  13. Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter

    PubMed Central

    Samsudin, Firdaus; Parker, Joanne L.; Sansom, Mark S.P.; Newstead, Simon; Fowler, Philip W.

    2016-01-01

    Summary Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the β-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepTSt, and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families. PMID:27028887

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

    NASA Astrophysics Data System (ADS)

    McPherron, R. L.; Chu, X.

    2015-12-01

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

  15. A review of the kinetic detail required for accurate predictions of normal shock waves

    NASA Technical Reports Server (NTRS)

    Muntz, E. P.; Erwin, Daniel A.; Pham-Van-diep, Gerald C.

    1991-01-01

    Several aspects of the kinetic models used in the collision phase of Monte Carlo direct simulations have been studied. Accurate molecular velocity distribution function predictions require a significantly increased number of computational cells in one maximum slope shock thickness, compared to predictions of macroscopic properties. The shape of the highly repulsive portion of the interatomic potential for argon is not well modeled by conventional interatomic potentials; this portion of the potential controls high Mach number shock thickness predictions, indicating that the specification of the energetic repulsive portion of interatomic or intermolecular potentials must be chosen with care for correct modeling of nonequilibrium flows at high temperatures. It has been shown for inverse power potentials that the assumption of variable hard sphere scattering provides accurate predictions of the macroscopic properties in shock waves, by comparison with simulations in which differential scattering is employed in the collision phase. On the other hand, velocity distribution functions are not well predicted by the variable hard sphere scattering model for softer potentials at higher Mach numbers.

  16. 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

  17. 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. PMID:27272707

  18. Predicted burden of venous disease.

    PubMed

    Onida, Sarah; Davies, Alun Huw

    2016-03-01

    Chronic venous disease is a common condition with clinical signs and symptoms ranging from spider veins, to varicose veins, to active venous ulceration. Both superficial and deep venous dysfunction may be implicated in the development of this disease. Socio-economic factors are shaping our population, with increasing age and body mass index resulting in significant pressure on healthcare systems worldwide. These risk factors also lead to an increased risk of developing superficial and/or deep venous insufficiency, increasing disease prevalence and morbidity. In this chapter, the authors review the current and future burden of chronic venous disease from an epidemiological, quality of life and economic perspective. PMID:26916773

  19. How many standard area diagram sets are needed for accurate disease severity assessment

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Standard area diagram sets (SADs) are widely used in plant pathology: a rater estimates disease severity by comparing an unknown sample to actual severities in the SADs and interpolates an estimate as accurately as possible (although some SADs have been developed for categorizing disease too). Most ...

  20. Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions

    PubMed Central

    2015-01-01

    Background Biclustering is a popular method for identifying under which experimental conditions biological signatures are co-expressed. However, the general biclustering problem is NP-hard, offering room to focus algorithms on specific biological tasks. We hypothesize that conditional co-regulation of genes is a key factor in determining cell phenotype and that accurately segregating conditions in biclusters will improve such predictions. Thus, we developed a bicluster sampled coherence metric (BSCM) for determining which conditions and signals should be included in a bicluster. Results Our BSCM calculates condition and cluster size specific p-values, and we incorporated these into the popular integrated biclustering algorithm cMonkey. We demonstrate that incorporation of our new algorithm significantly improves bicluster co-regulation scores (p-value = 0.009) and GO annotation scores (p-value = 0.004). Additionally, we used a bicluster based signal to predict whether a given experimental condition will result in yeast peroxisome induction. Using the new algorithm, the classifier accuracy improves from 41.9% to 76.1% correct. Conclusions We demonstrate that the proposed BSCM helps determine which signals ought to be co-clustered, resulting in more accurately assigned bicluster membership. Furthermore, we show that BSCM can be extended to more accurately detect under which experimental conditions the genes are co-clustered. Features derived from this more accurate analysis of conditional regulation results in a dramatic improvement in the ability to predict a cellular phenotype in yeast. The latest cMonkey is available for download at https://github.com/baliga-lab/cmonkey2. The experimental data and source code featured in this paper is available http://AitchisonLab.com/BSCM. BSCM has been incorporated in the official cMonkey release. PMID:25881257

  1. Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism

    PubMed Central

    Kieslich, Chris A.; Tamamis, Phanourios; Guzman, Yannis A.; Onel, Melis; Floudas, Christodoulos A.

    2016-01-01

    HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/. PMID:26859389

  2. Accurate similarity index based on activity and connectivity of node for link prediction

    NASA Astrophysics Data System (ADS)

    Li, Longjie; Qian, Lvjian; Wang, Xiaoping; Luo, Shishun; Chen, Xiaoyun

    2015-05-01

    Recent years have witnessed the increasing of available network data; however, much of those data is incomplete. Link prediction, which can find the missing links of a network, plays an important role in the research and analysis of complex networks. Based on the assumption that two unconnected nodes which are highly similar are very likely to have an interaction, most of the existing algorithms solve the link prediction problem by computing nodes' similarities. The fundamental requirement of those algorithms is accurate and effective similarity indices. In this paper, we propose a new similarity index, namely similarity based on activity and connectivity (SAC), which performs link prediction more accurately. To compute the similarity between two nodes, this index employs the average activity of these two nodes in their common neighborhood and the connectivities between them and their common neighbors. The higher the average activity is and the stronger the connectivities are, the more similar the two nodes are. The proposed index not only commendably distinguishes the contributions of paths but also incorporates the influence of endpoints. Therefore, it can achieve a better predicting result. To verify the performance of SAC, we conduct experiments on 10 real-world networks. Experimental results demonstrate that SAC outperforms the compared baselines.

  3. 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 PMID:27100309

  4. Prediction of Accurate Thermochemistry of Medium and Large Sized Radicals Using Connectivity-Based Hierarchy (CBH).

    PubMed

    Sengupta, Arkajyoti; Raghavachari, Krishnan

    2014-10-14

    Accurate modeling of the chemical reactions in many diverse areas such as combustion, photochemistry, or atmospheric chemistry strongly depends on the availability of thermochemical information of the radicals involved. However, accurate thermochemical investigations of radical systems using state of the art composite methods have mostly been restricted to the study of hydrocarbon radicals of modest size. In an alternative approach, systematic error-canceling thermochemical hierarchy of reaction schemes can be applied to yield accurate results for such systems. In this work, we have extended our connectivity-based hierarchy (CBH) method to the investigation of radical systems. We have calibrated our method using a test set of 30 medium sized radicals to evaluate their heats of formation. The CBH-rad30 test set contains radicals containing diverse functional groups as well as cyclic systems. We demonstrate that the sophisticated error-canceling isoatomic scheme (CBH-2) with modest levels of theory is adequate to provide heats of formation accurate to ∼1.5 kcal/mol. Finally, we predict heats of formation of 19 other large and medium sized radicals for which the accuracy of available heats of formation are less well-known. PMID:26588131

  5. conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure.

    PubMed

    Kieslich, Chris A; Smadbeck, James; Khoury, George A; Floudas, Christodoulos A

    2016-03-28

    Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus secondary structure prediction method, conSSert, which is based on support vector machines (SVM) and provides exceptional accuracy for the prediction of beta-strands with QE accuracy of over 0.82 and a Q2-EH of 0.86. conSSert uses as input probabilities for the three types of secondary structure (helix, strand, and coil) that are predicted by four top performing methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR. conSSert was trained/tested using 4261 protein chains from PDBSelect25, and 8632 chains from PISCES. Further validation was performed using targets from CASP9, CASP10, and CASP11. Our data suggest that poor performance in strand prediction is likely a result of training bias and not solely due to the nonlocal nature of beta-sheet contacts. conSSert is freely available for noncommercial use as a webservice: http://ares.tamu.edu/conSSert/ . PMID:26928531

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

    NASA Astrophysics Data System (ADS)

    Junkin, Gary

    2013-04-01

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

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

    SciTech Connect

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

    2004-12-01

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

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

    DOE PAGESBeta

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

    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

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

    SciTech Connect

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

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

  10. Accurate microRNA target prediction correlates with protein repression levels

    PubMed Central

    Maragkakis, Manolis; Alexiou, Panagiotis; Papadopoulos, Giorgio L; Reczko, Martin; Dalamagas, Theodore; Giannopoulos, George; Goumas, George; Koukis, Evangelos; Kourtis, Kornilios; Simossis, Victor A; Sethupathy, Praveen; Vergoulis, Thanasis; Koziris, Nectarios; Sellis, Timos; Tsanakas, Panagiotis; Hatzigeorgiou, Artemis G

    2009-01-01

    Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at PMID:19765283

  11. 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.

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

    PubMed

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

    2015-07-01

    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 to 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. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded. PMID:25979264

  13. Microstructure-Dependent Gas Adsorption: Accurate Predictions of Methane Uptake in Nanoporous Carbons

    SciTech Connect

    Ihm, Yungok; Cooper, Valentino R; Gallego, Nidia C; Contescu, Cristian I; Morris, James R

    2014-01-01

    We demonstrate a successful, efficient framework for predicting gas adsorption properties in real materials based on first-principles calculations, with a specific comparison of experiment and theory for methane adsorption in activated carbons. These carbon materials have different pore size distributions, leading to a variety of uptake characteristics. Utilizing these distributions, we accurately predict experimental uptakes and heats of adsorption without empirical potentials or lengthy simulations. We demonstrate that materials with smaller pores have higher heats of adsorption, leading to a higher gas density in these pores. This pore-size dependence must be accounted for, in order to predict and understand the adsorption behavior. The theoretical approach combines: (1) ab initio calculations with a van der Waals density functional to determine adsorbent-adsorbate interactions, and (2) a thermodynamic method that predicts equilibrium adsorption densities by directly incorporating the calculated potential energy surface in a slit pore model. The predicted uptake at P=20 bar and T=298 K is in excellent agreement for all five activated carbon materials used. This approach uses only the pore-size distribution as an input, with no fitting parameters or empirical adsorbent-adsorbate interactions, and thus can be easily applied to other adsorbent-adsorbate combinations.

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

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

    SciTech Connect

    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 to 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.

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

    DOE PAGESBeta

    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

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

    NASA Technical Reports Server (NTRS)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1988-01-01

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

  18. Accurate verification of the conserved-vector-current and standard-model predictions

    SciTech Connect

    Sirlin, A.; Zucchini, R.

    1986-10-20

    An approximate analytic calculation of O(Z..cap alpha../sup 2/) corrections to Fermi decays is presented. When the analysis of Koslowsky et al. is modified to take into account the new results, it is found that each of the eight accurately studied scrFt values differs from the average by approx. <1sigma, thus significantly improving the comparison of experiments with conserved-vector-current predictions. The new scrFt values are lower than before, which also brings experiments into very good agreement with the three-generation standard model, at the level of its quantum corrections.

  19. Accurate Structure Prediction and Conformational Analysis of Cyclic Peptides with Residue-Specific Force Fields.

    PubMed

    Geng, Hao; Jiang, Fan; Wu, Yun-Dong

    2016-05-19

    Cyclic peptides (CPs) are promising candidates for drugs, chemical biology tools, and self-assembling nanomaterials. However, the development of reliable and accurate computational methods for their structure prediction has been challenging. Here, 20 all-trans CPs of 5-12 residues selected from Cambridge Structure Database have been simulated using replica-exchange molecular dynamics with four different force fields. Our recently developed residue-specific force fields RSFF1 and RSFF2 can correctly identify the crystal-like conformations of more than half CPs as the most populated conformation. The RSFF2 performs the best, which consistently predicts the crystal structures of 17 out of 20 CPs with rmsd < 1.1 Å. We also compared the backbone (ϕ, ψ) sampling of residues in CPs with those in short linear peptides and in globular proteins. In general, unlike linear peptides, CPs have local conformational free energies and entropies quite similar to globular proteins. PMID:27128113

  20. 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.

  1. Predicting visual outcomes for macular disease using optical coherence tomography

    PubMed Central

    Keane, Pearse A.; Sadda, Srinivas R.

    2011-01-01

    In recent years, the management of macular disease has undergone radical changes, in part because of new therapeutic approaches, but also due to the introduction of a new imaging modality – optical coherence tomography (OCT). The application of OCT imaging has clarified many aspects of chorioretinal disease pathophysiology and elucidated many hitherto unrecognized disease characteristics. From an early stage in its development, OCT has also been revolutionary in attempting to extract clinically useful measurements from image data in an automated fashion. As a result, OCT-derived measurements of retinal thickness have been rapidly embraced in clinical and research settings. However, as knowledge of OCT image analysis has developed, it has become increasingly clear that even accurate measurements of retinal thickness may fail to predict visual outcomes for many diseases. As a result, the focus of much current clinical imaging research is on the identification of other OCT-derived anatomic biomarkers predictive of visual outcomes – such biomarkers could serve as surrogate endpoints in clinical trials and provide prognostic information in clinical practice. In this review, we begin by highlighting the importance of accurate visual function assessment and describing the fundamentals of OCT image evaluation, before describing the current state-of-the-art with regard to predicting visual outcomes, for a variety of macular diseases, using OCT. PMID:23960916

  2. Accurate prediction of helix interactions and residue contacts in membrane proteins.

    PubMed

    Hönigschmid, Peter; Frishman, Dmitrij

    2016-04-01

    Accurate prediction of intra-molecular interactions from amino acid sequence is an important pre-requisite for obtaining high-quality protein models. Over the recent years, remarkable progress in this area has been achieved through the application of novel co-variation algorithms, which eliminate transitive evolutionary connections between residues. In this work we present a new contact prediction method for α-helical transmembrane proteins, MemConP, in which evolutionary couplings are combined with a machine learning approach. MemConP achieves a substantially improved accuracy (precision: 56.0%, recall: 17.5%, MCC: 0.288) compared to the use of either machine learning or co-evolution methods alone. The method also achieves 91.4% precision, 42.1% recall and a MCC of 0.490 in predicting helix-helix interactions based on predicted contacts. The approach was trained and rigorously benchmarked by cross-validation and independent testing on up-to-date non-redundant datasets of 90 and 30 experimental three dimensional structures, respectively. MemConP is a standalone tool that can be downloaded together with the associated training data from http://webclu.bio.wzw.tum.de/MemConP. PMID:26851352

  3. Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

    PubMed Central

    Xu, Tianlei; Li, Ben; Zhao, Meng; Szulwach, Keith E.; Street, R. Craig; Lin, Li; Yao, Bing; Zhang, Feiran; Jin, Peng; Wu, Hao; Qin, Zhaohui S.

    2015-01-01

    Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods. PMID:25722376

  4. 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. PMID:24376713

  5. NMRDSP: An Accurate Prediction of Protein Shape Strings from NMR Chemical Shifts and Sequence Data

    PubMed Central

    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. PMID:24376713

  6. Accurate Prediction of Drug-Induced Liver Injury Using Stem Cell-Derived Populations

    PubMed Central

    Szkolnicka, Dagmara; Farnworth, Sarah L.; Lucendo-Villarin, Baltasar; Storck, Christopher; Zhou, Wenli; Iredale, John P.; Flint, Oliver

    2014-01-01

    Despite major progress in the knowledge and management of human liver injury, there are millions of people suffering from chronic liver disease. Currently, the only cure for end-stage liver disease is orthotopic liver transplantation; however, this approach is severely limited by organ donation. Alternative approaches to restoring liver function have therefore been pursued, including the use of somatic and stem cell populations. Although such approaches are essential in developing scalable treatments, there is also an imperative to develop predictive human systems that more effectively study and/or prevent the onset of liver disease and decompensated organ function. We used a renewable human stem cell resource, from defined genetic backgrounds, and drove them through developmental intermediates to yield highly active, drug-inducible, and predictive human hepatocyte populations. Most importantly, stem cell-derived hepatocytes displayed equivalence to primary adult hepatocytes, following incubation with known hepatotoxins. In summary, we have developed a serum-free, scalable, and shippable cell-based model that faithfully predicts the potential for human liver injury. Such a resource has direct application in human modeling and, in the future, could play an important role in developing renewable cell-based therapies. PMID:24375539

  7. A hierarchical approach to accurate predictions of macroscopic thermodynamic behavior from quantum mechanics and molecular simulations

    NASA Astrophysics Data System (ADS)

    Garrison, Stephen L.

    2005-07-01

    The combination of molecular simulations and potentials obtained from quantum chemistry is shown to be able to provide reasonably accurate thermodynamic property predictions. Gibbs ensemble Monte Carlo simulations are used to understand the effects of small perturbations to various regions of the model Lennard-Jones 12-6 potential. However, when the phase behavior and second virial coefficient are scaled by the critical properties calculated for each potential, the results obey a corresponding states relation suggesting a non-uniqueness problem for interaction potentials fit to experimental phase behavior. Several variations of a procedure collectively referred to as quantum mechanical Hybrid Methods for Interaction Energies (HM-IE) are developed and used to accurately estimate interaction energies from CCSD(T) calculations with a large basis set in a computationally efficient manner for the neon-neon, acetylene-acetylene, and nitrogen-benzene systems. Using these results and methods, an ab initio, pairwise-additive, site-site potential for acetylene is determined and then improved using results from molecular simulations using this initial potential. The initial simulation results also indicate that a limited range of energies important for accurate phase behavior predictions. Second virial coefficients calculated from the improved potential indicate that one set of experimental data in the literature is likely erroneous. This prescription is then applied to methanethiol. Difficulties in modeling the effects of the lone pair electrons suggest that charges on the lone pair sites negatively impact the ability of the intermolecular potential to describe certain orientations, but that the lone pair sites may be necessary to reasonably duplicate the interaction energies for several orientations. Two possible methods for incorporating the effects of three-body interactions into simulations within the pairwise-additivity formulation are also developed. A low density

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

    PubMed

    Shvab, I; Sadus, Richard J

    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(3) for a wide range of temperatures (298-650 K) and pressures (0.1-700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC∕E and TIP4P∕2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys. 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC∕E and TIP4P∕2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K. PMID:24320337

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

    NASA Astrophysics Data System (ADS)

    Shvab, I.; Sadus, Richard J.

    2013-11-01

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

  10. Toward an Accurate Prediction of the Arrival Time of Geomagnetic-Effective Coronal Mass Ejections

    NASA Astrophysics Data System (ADS)

    Shi, T.; Wang, Y.; Wan, L.; Cheng, X.; Ding, M.; Zhang, J.

    2015-12-01

    Accurately predicting the arrival of coronal mass ejections (CMEs) to the Earth based on remote images is of critical significance for the study of space weather. Here we make a statistical study of 21 Earth-directed CMEs, specifically exploring the relationship between CME initial speeds and transit times. The initial speed of a CME is obtained by fitting the CME with the Graduated Cylindrical Shell model and is thus free of projection effects. We then use the drag force model to fit results of the transit time versus the initial speed. By adopting different drag regimes, i.e., the viscous, aerodynamics, and hybrid regimes, we get similar results, with a least mean estimation error of the hybrid model of 12.9 hr. CMEs with a propagation angle (the angle between the propagation direction and the Sun-Earth line) larger than their half-angular widths arrive at the Earth with an angular deviation caused by factors other than the radial solar wind drag. The drag force model cannot be reliably applied to such events. If we exclude these events in the sample, the prediction accuracy can be improved, i.e., the estimation error reduces to 6.8 hr. This work suggests that it is viable to predict the arrival time of CMEs to the Earth based on the initial parameters with fairly good accuracy. Thus, it provides a method of forecasting space weather 1-5 days following the occurrence of CMEs.

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

    NASA Astrophysics Data System (ADS)

    Zeng, Zhenhua; Chan, Maria; Greeley, Jeff

    2015-03-01

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

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

    SciTech Connect

    Shvab, I.; Sadus, Richard J.

    2013-11-21

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

  13. Direct Pressure Monitoring Accurately Predicts Pulmonary Vein Occlusion During Cryoballoon Ablation

    PubMed Central

    Kosmidou, Ioanna; Wooden, Shannnon; Jones, Brian; Deering, Thomas; Wickliffe, Andrew; Dan, Dan

    2013-01-01

    Cryoballoon ablation (CBA) is an established therapy for atrial fibrillation (AF). Pulmonary vein (PV) occlusion is essential for achieving antral contact and PV isolation and is typically assessed by contrast injection. We present a novel method of direct pressure monitoring for assessment of PV occlusion. Transcatheter pressure is monitored during balloon advancement to the PV antrum. Pressure is recorded via a single pressure transducer connected to the inner lumen of the cryoballoon. Pressure curve characteristics are used to assess occlusion in conjunction with fluoroscopic or intracardiac echocardiography (ICE) guidance. PV occlusion is confirmed when loss of typical left atrial (LA) pressure waveform is observed with recordings of PA pressure characteristics (no A wave and rapid V wave upstroke). Complete pulmonary vein occlusion as assessed with this technique has been confirmed with concurrent contrast utilization during the initial testing of the technique and has been shown to be highly accurate and readily reproducible. We evaluated the efficacy of this novel technique in 35 patients. A total of 128 veins were assessed for occlusion with the cryoballoon utilizing the pressure monitoring technique; occlusive pressure was demonstrated in 113 veins with resultant successful pulmonary vein isolation in 111 veins (98.2%). Occlusion was confirmed with subsequent contrast injection during the initial ten procedures, after which contrast utilization was rapidly reduced or eliminated given the highly accurate identification of occlusive pressure waveform with limited initial training. Verification of PV occlusive pressure during CBA is a novel approach to assessing effective PV occlusion and it accurately predicts electrical isolation. Utilization of this method results in significant decrease in fluoroscopy time and volume of contrast. PMID:23485956

  14. Direct pressure monitoring accurately predicts pulmonary vein occlusion during cryoballoon ablation.

    PubMed

    Kosmidou, Ioanna; Wooden, Shannnon; Jones, Brian; Deering, Thomas; Wickliffe, Andrew; Dan, Dan

    2013-01-01

    Cryoballoon ablation (CBA) is an established therapy for atrial fibrillation (AF). Pulmonary vein (PV) occlusion is essential for achieving antral contact and PV isolation and is typically assessed by contrast injection. We present a novel method of direct pressure monitoring for assessment of PV occlusion. Transcatheter pressure is monitored during balloon advancement to the PV antrum. Pressure is recorded via a single pressure transducer connected to the inner lumen of the cryoballoon. Pressure curve characteristics are used to assess occlusion in conjunction with fluoroscopic or intracardiac echocardiography (ICE) guidance. PV occlusion is confirmed when loss of typical left atrial (LA) pressure waveform is observed with recordings of PA pressure characteristics (no A wave and rapid V wave upstroke). Complete pulmonary vein occlusion as assessed with this technique has been confirmed with concurrent contrast utilization during the initial testing of the technique and has been shown to be highly accurate and readily reproducible. We evaluated the efficacy of this novel technique in 35 patients. A total of 128 veins were assessed for occlusion with the cryoballoon utilizing the pressure monitoring technique; occlusive pressure was demonstrated in 113 veins with resultant successful pulmonary vein isolation in 111 veins (98.2%). Occlusion was confirmed with subsequent contrast injection during the initial ten procedures, after which contrast utilization was rapidly reduced or eliminated given the highly accurate identification of occlusive pressure waveform with limited initial training. Verification of PV occlusive pressure during CBA is a novel approach to assessing effective PV occlusion and it accurately predicts electrical isolation. Utilization of this method results in significant decrease in fluoroscopy time and volume of contrast. PMID:23485956

  15. 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.

  16. 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).

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

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

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

    SciTech Connect

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-05-14

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

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

    SciTech Connect

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-09-15

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

  20. An Accurate, Clinically Feasible Multi-Gene Expression Assay for Predicting Metastasis in Uveal Melanoma

    PubMed Central

    Onken, Michael D.; Worley, Lori A.; Tuscan, Meghan D.; Harbour, J. William

    2010-01-01

    Uveal (ocular) melanoma is an aggressive cancer that often forms undetectable micrometastases before diagnosis of the primary tumor. These micrometastases later multiply to generate metastatic tumors that are resistant to therapy and are uniformly fatal. We have previously identified a gene expression profile derived from the primary tumor that is extremely accurate for identifying patients at high risk of metastatic disease. Development of a practical clinically feasible platform for analyzing this expression profile would benefit high-risk patients through intensified metastatic surveillance, earlier intervention for metastasis, and stratification for entry into clinical trials of adjuvant therapy. Here, we migrate the expression profile from a hybridization-based microarray platform to a robust, clinically practical, PCR-based 15-gene assay comprising 12 discriminating genes and three endogenous control genes. We analyze the technical performance of the assay in a prospective study of 609 tumor samples, including 421 samples sent from distant locations. We show that the assay can be performed accurately on fine needle aspirate biopsy samples, even when the quantity of RNA is below detectable limits. Preliminary outcome data from the prospective study affirm the prognostic accuracy of the assay. This prognostic assay provides an important addition to the armamentarium for managing patients with uveal melanoma, and it provides a proof of principle for the development of similar assays for other cancers. PMID:20413675

  1. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Gui, Jie; Nie, Ru

    2016-01-01

    Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research. PMID:27571061

  2. A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines.

    PubMed

    Dal Moro, F; Abate, A; Lanckriet, G R G; Arandjelovic, G; Gasparella, P; Bassi, P; Mancini, M; Pagano, F

    2006-01-01

    The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compared the performances obtained with logistic regression (LR) and accurately trained artificial neural networks (ANN) to those obtained with SVM, that is, the standard SVM, and the linear programming SVM (LP-SVM); the latter techniques show an improved performance. Moreover, we rank the prediction factors according to their importance using Fisher scores and the LP-SVM feature weights. A data set of 1163 patients affected by renal colic has been analyzed and restricted to single out a statistically coherent subset of 402 patients. Nine clinical factors are used as inputs for the classification algorithms, to predict one binary output. The algorithms are cross-validated by training and testing on randomly selected train- and test-set partitions of the data and reporting the average performance on the test sets. The SVM-based approaches obtained a sensitivity of 84.5% and a specificity of 86.9%. The feature ranking based on LP-SVM gives the highest importance to stone size, stone position and symptom duration before check-up. We propose a statistically correct way of employing LR, ANN and SVM for the prediction of spontaneous passage of ureteral stones in patients with renal colic. SVM outperformed ANN, as well as LR. This study will soon be translated into a practical software toolbox for actual clinical usage. PMID:16374437

  3. 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

  4. Can CO2 assimilation in maize leaves be predicted accurately from chlorophyll fluorescence analysis?

    PubMed

    Edwards, G E; Baker, N R

    1993-08-01

    Analysis is made of the energetics of CO2 fixation, the photochemical quantum requirement per CO2 fixed, and sinks for utilising reductive power in the C4 plant maize. CO2 assimilation is the primary sink for energy derived from photochemistry, whereas photorespiration and nitrogen assimilation are relatively small sinks, particularly in developed leaves. Measurement of O2 exchange by mass spectrometry and CO2 exchange by infrared gas analysis under varying levels of CO2 indicate that there is a very close relationship between the true rate of O2 evolution from PS II and the net rate of CO2 fixation. Consideration is given to measurements of the quantum yields of PS II (φ PS II) from fluorescence analysis and of CO2 assimilation ([Formula: see text]) in maize over a wide range of conditions. The[Formula: see text] ratio was found to remain reasonably constant (ca. 12) over a range of physiological conditions in developed leaves, with varying temperature, CO2 concentrations, light intensities (from 5% to 100% of full sunlight), and following photoinhibition under high light and low temperature. A simple model for predicting CO2 assimilation from fluorescence parameters is presented and evaluated. It is concluded that under a wide range of conditions fluorescence parameters can be used to predict accurately and rapidly CO2 assimilation rates in maize. PMID:24317706

  5. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina.

    PubMed

    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

    2016-04-01

    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

  6. 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

  7. Predicting Parkinson's disease - why, when, and how?

    PubMed

    Postuma, R B; Montplaisir, J

    2009-12-01

    Parkinson's disease (PD) is a progressive disorder with a presymptomatic interval; that is, there is a period during which the pathologic process has begun, but motor signs required for the clinical diagnosis are absent. There is considerable interest in discovering markers to diagnose this preclinical stage. Current predictive marker development stems mainly from two principles; first, that pathologic processes occur in lower brainstem regions before substantia nigra involvement and second, that redundancy and compensatory responses cause symptoms to emerge only after advanced degeneration. Decreased olfaction has recently been demonstrated to predict PD in prospective pathologic studies, although the lead time may be relatively short and the positive predictive value and specificity are low. Screening patients for depression and personality changes, autonomic symptoms, subtle motor dysfunction on quantitative testing, sleepiness and insomnia are other potential simple markers. More invasive measures such as detailed autonomic testing, cardiac MIBG-scintigraphy, transcranial ultrasound, and dopaminergic functional imaging may be especially useful in those at high risk or for further defining risk in those identified through primary screening. Despite intriguing leads, direct testing of preclinical markers has been limited, mainly because there is no reliable way to identify preclinical disease. Idiopathic RBD is characterized by loss of normal atonia with REM sleep. Approximately 50% of affected individuals will develop PD or dementia within 10 years. This provides an unprecedented opportunity to test potential predictive markers before clinical disease onset. The results of marker testing in idiopathic RBD with its implications for disease prediction will be detailed. PMID:20082967

  8. New technologies in predicting, preventing and controlling emerging infectious diseases

    PubMed Central

    Christaki, Eirini

    2015-01-01

    Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats. PMID:26068569

  9. Energy expenditure during level human walking: seeking a simple and accurate predictive solution.

    PubMed

    Ludlow, Lindsay W; Weyand, Peter G

    2016-03-01

    Accurate prediction of the metabolic energy that walking requires can inform numerous health, bodily status, and fitness outcomes. We adopted a two-step approach to identifying a concise, generalized equation for predicting level human walking metabolism. Using literature-aggregated values we compared 1) the predictive accuracy of three literature equations: American College of Sports Medicine (ACSM), Pandolf et al., and Height-Weight-Speed (HWS); and 2) the goodness-of-fit possible from one- vs. two-component descriptions of walking metabolism. Literature metabolic rate values (n = 127; speed range = 0.4 to 1.9 m/s) were aggregated from 25 subject populations (n = 5-42) whose means spanned a 1.8-fold range of heights and a 4.2-fold range of weights. Population-specific resting metabolic rates (V̇o2 rest) were determined using standardized equations. Our first finding was that the ACSM and Pandolf et al. equations underpredicted nearly all 127 literature-aggregated values. Consequently, their standard errors of estimate (SEE) were nearly four times greater than those of the HWS equation (4.51 and 4.39 vs. 1.13 ml O2·kg(-1)·min(-1), respectively). For our second comparison, empirical best-fit relationships for walking metabolism were derived from the data set in one- and two-component forms for three V̇o2-speed model types: linear (∝V(1.0)), exponential (∝V(2.0)), and exponential/height (∝V(2.0)/Ht). We found that the proportion of variance (R(2)) accounted for, when averaged across the three model types, was substantially lower for one- vs. two-component versions (0.63 ± 0.1 vs. 0.90 ± 0.03) and the predictive errors were nearly twice as great (SEE = 2.22 vs. 1.21 ml O2·kg(-1)·min(-1)). Our final analysis identified the following concise, generalized equation for predicting level human walking metabolism: V̇o2 total = V̇o2 rest + 3.85 + 5.97·V(2)/Ht (where V is measured in m/s, Ht in meters, and V̇o2 in ml O2·kg(-1)·min(-1)). PMID:26679617

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

    SciTech Connect

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

    2012-07-01

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

  11. TIMP2•IGFBP7 biomarker panel accurately predicts acute kidney injury in high-risk surgical patients

    PubMed Central

    Gunnerson, Kyle J.; Shaw, Andrew D.; Chawla, Lakhmir S.; Bihorac, Azra; Al-Khafaji, Ali; Kashani, Kianoush; Lissauer, Matthew; Shi, Jing; Walker, Michael G.; Kellum, John A.

    2016-01-01

    BACKGROUND Acute kidney injury (AKI) is an important complication in surgical patients. Existing biomarkers and clinical prediction models underestimate the risk for developing AKI. We recently reported data from two trials of 728 and 408 critically ill adult patients in whom urinary TIMP2•IGFBP7 (NephroCheck, Astute Medical) was used to identify patients at risk of developing AKI. Here we report a preplanned analysis of surgical patients from both trials to assess whether urinary tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor–binding protein 7 (IGFBP7) accurately identify surgical patients at risk of developing AKI. STUDY DESIGN We enrolled adult surgical patients at risk for AKI who were admitted to one of 39 intensive care units across Europe and North America. The primary end point was moderate-severe AKI (equivalent to KDIGO [Kidney Disease Improving Global Outcomes] stages 2–3) within 12 hours of enrollment. Biomarker performance was assessed using the area under the receiver operating characteristic curve, integrated discrimination improvement, and category-free net reclassification improvement. RESULTS A total of 375 patients were included in the final analysis of whom 35 (9%) developed moderate-severe AKI within 12 hours. The area under the receiver operating characteristic curve for [TIMP-2]•[IGFBP7] alone was 0.84 (95% confidence interval, 0.76–0.90; p < 0.0001). Biomarker performance was robust in sensitivity analysis across predefined subgroups (urgency and type of surgery). CONCLUSION For postoperative surgical intensive care unit patients, a single urinary TIMP2•IGFBP7 test accurately identified patients at risk for developing AKI within the ensuing 12 hours and its inclusion in clinical risk prediction models significantly enhances their performance. LEVEL OF EVIDENCE Prognostic study, level I. PMID:26816218

  12. A Prediction Model for Chronic Kidney Disease Includes Periodontal Disease

    PubMed Central

    Fisher, Monica A.; Taylor, George W.

    2009-01-01

    Background An estimated 75% of the seven million Americans with moderate-to-severe chronic kidney disease are undiagnosed. Improved prediction models to identify high-risk subgroups for chronic kidney disease enhance the ability of health care providers to prevent or delay serious sequelae, including kidney failure, cardiovascular disease, and premature death. Methods We identified 11,955 adults ≥18 years of age in the Third National Health and Nutrition Examination Survey. Chronic kidney disease was defined as an estimated glomerular filtration rate of 15 to 59 ml/minute/1.73 m2. High-risk subgroups for chronic kidney disease were identified by estimating the individual probability using β coefficients from the model of traditional and non-traditional risk factors. To evaluate this model, we performed standard diagnostic analyses of sensitivity, specificity, positive predictive value, and negative predictive value using 5%, 10%, 15%, and 20% probability cutoff points. Results The estimated probability of chronic kidney disease ranged from virtually no probability (0%) for an individual with none of the 12 risk factors to very high probability (98%) for an older, non-Hispanic white edentulous former smoker, with diabetes ≥10 years, hypertension, macroalbuminuria, high cholesterol, low high-density lipoprotein, high C-reactive protein, lower income, and who was hospitalized in the past year. Evaluation of this model using an estimated 5% probability cutoff point resulted in 86% sensitivity, 85% specificity, 18% positive predictive value, and 99% negative predictive value. Conclusion This United States population–based study suggested the importance of considering multiple risk factors, including periodontal status, because this improves the identification of individuals at high risk for chronic kidney disease and may ultimately reduce its burden. PMID:19228085

  13. 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.

  14. 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). PMID:26430979

  15. How accurately can we predict the melting points of drug-like compounds?

    PubMed

    Tetko, Igor V; Sushko, Yurii; Novotarskyi, Sergii; Patiny, Luc; Kondratov, Ivan; Petrenko, Alexander E; Charochkina, Larisa; Asiri, Abdullah M

    2014-12-22

    This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638. PMID:25489863

  16. How Accurately Can We Predict the Melting Points of Drug-like Compounds?

    PubMed Central

    2014-01-01

    This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638. PMID:25489863

  17. Predicting global variation in infectious disease severity

    PubMed Central

    Jensen, Per M.; De Fine Licht, Henrik H.

    2016-01-01

    Background and objectives: Understanding the underlying causes for the variation in case-fatality-ratios (CFR) is important for assessing the mechanism governing global disparity in the burden of infectious diseases. Variation in CFR is likely to be driven by factors such as population genetics, demography, transmission patterns and general health status. We present data here that support the hypothsis that changes in CFRs for specific diseases may be the result of serial passage through different hosts. For example passage through adults may lead to lower CFR, whereas passage through children may have the opposite effect. Accordingly changes in CFR may occur in parallel with demographic transitions. Methodology: We explored the predictability of CFR using data obtained from the World Health Organization (WHO) disease databases for four human diseases: mumps, malaria, tuberculosis and leptospirosis and assessed these for association with a range of population characteristics, such as crude birth and death rates, median age of the population, mean body mass index, proportion living in urban areas and tuberculosis vaccine coverage. We then tested this predictive model on Danish historical demographic and population data. Results: Birth rates were the best predictor for mumps and malaria CFR. For tuberculosis CFR death rates were the best predictor and for leptospirosis population density was a significant predictor. Conclusions and implications: CFR predictors differed among diseases according to their biology. We suggest that the overall result reflects an interaction between the forces driving demographic change and the virulence of human-to-human transmitted diseases. PMID:26884415

  18. Accurate prediction of V1 location from cortical folds in a surface coordinate system

    PubMed Central

    Hinds, Oliver P.; Rajendran, Niranjini; Polimeni, Jonathan R.; Augustinack, Jean C.; Wiggins, Graham; Wald, Lawrence L.; Rosas, H. Diana; Potthast, Andreas; Schwartz, Eric L.; Fischl, Bruce

    2008-01-01

    Previous studies demonstrated substantial variability of the location of primary visual cortex (V1) in stereotaxic coordinates when linear volume-based registration is used to match volumetric image intensities (Amunts et al., 2000). However, other qualitative reports of V1 location (Smith, 1904; Stensaas et al., 1974; Rademacher et al., 1993) suggested a consistent relationship between V1 and the surrounding cortical folds. Here, the relationship between folds and the location of V1 is quantified using surface-based analysis to generate a probabilistic atlas of human V1. High-resolution (about 200 μm) magnetic resonance imaging (MRI) at 7 T of ex vivo human cerebral hemispheres allowed identification of the full area via the stria of Gennari: a myeloarchitectonic feature specific to V1. Separate, whole-brain scans were acquired using MRI at 1.5 T to allow segmentation and mesh reconstruction of the cortical gray matter. For each individual, V1 was manually identified in the high-resolution volume and projected onto the cortical surface. Surface-based intersubject registration (Fischl et al., 1999b) was performed to align the primary cortical folds of individual hemispheres to those of a reference template representing the average folding pattern. An atlas of V1 location was constructed by computing the probability of V1 inclusion for each cortical location in the template space. This probabilistic atlas of V1 exhibits low prediction error compared to previous V1 probabilistic atlases built in volumetric coordinates. The increased predictability observed under surface-based registration suggests that the location of V1 is more accurately predicted by the cortical folds than by the shape of the brain embedded in the volume of the skull. In addition, the high quality of this atlas provides direct evidence that surface-based intersubject registration methods are superior to volume-based methods at superimposing functional areas of cortex, and therefore are better

  19. An extended set of yeast-based functional assays accurately identifies human disease mutations.

    PubMed

    Sun, Song; Yang, Fan; Tan, Guihong; Costanzo, Michael; Oughtred, Rose; Hirschman, Jodi; Theesfeld, Chandra L; Bansal, Pritpal; Sahni, Nidhi; Yi, Song; Yu, Analyn; Tyagi, Tanya; Tie, Cathy; Hill, David E; Vidal, Marc; Andrews, Brenda J; Boone, Charles; Dolinski, Kara; Roth, Frederick P

    2016-05-01

    We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods. PMID:26975778

  20. An extended set of yeast-based functional assays accurately identifies human disease mutations

    PubMed Central

    Sun, Song; Yang, Fan; Tan, Guihong; Costanzo, Michael; Oughtred, Rose; Hirschman, Jodi; Theesfeld, Chandra L.; Bansal, Pritpal; Sahni, Nidhi; Yi, Song; Yu, Analyn; Tyagi, Tanya; Tie, Cathy; Hill, David E.; Vidal, Marc; Andrews, Brenda J.; Boone, Charles; Dolinski, Kara; Roth, Frederick P.

    2016-01-01

    We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods. PMID:26975778

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

    SciTech Connect

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

    2010-07-01

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

  2. [The determinant role of an accurate medicosocial approach in the prognosis of pediatric blood diseases].

    PubMed

    Toppet, M

    2005-01-01

    The care of infancy and childhood blood diseases implies a comprehensive medicosocial approach. This is a prerequisite for regular follow-up, for satisfactory compliance to treatment and for optimal patient's quality of life. Different modalities of medicosocial approach have been developed in the pediatric department (firstly in the Hospital Saint Pierre and than in the Children's University Hospital HUDERF). The drastic importance of a recent reform of the increased family allowances is briefly presented. The author underlines the determinant role of an accurate global approach, in which the patient and the family are surrounded by a multidisciplinary team, including social workers. PMID:16454232

  3. Classification and disease prediction via mathematical programming

    NASA Astrophysics Data System (ADS)

    Lee, Eva K.; Wu, Tsung-Lin

    2007-11-01

    In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular

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

    PubMed

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

    2016-01-25

    Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in <1h compared to >3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required. PMID:26708965

  5. Structural imaging biomarkers of Alzheimer's disease: predicting disease progression.

    PubMed

    Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir S; Pruessner, Jens C; Collins, D Louis

    2015-01-01

    Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimer's Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice. PMID:25260851

  6. Disease Prediction Models and Operational Readiness

    PubMed Central

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey; Noonan, Christine; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-01-01

    The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness

  7. Disease prediction models and operational readiness.

    PubMed

    Corley, Courtney D; Pullum, Laura L; Hartley, David M; Benedum, Corey; Noonan, Christine; Rabinowitz, Peter M; Lancaster, Mary J

    2014-01-01

    The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness

  8. Behavioural phenotypes predict disease susceptibility and infectiousness.

    PubMed

    Araujo, Alessandra; Kirschman, Lucas; Warne, Robin W

    2016-08-01

    Behavioural phenotypes may provide a means for identifying individuals that disproportionally contribute to disease spread and epizootic outbreaks. For example, bolder phenotypes may experience greater exposure and susceptibility to pathogenic infection because of distinct interactions with conspecifics and their environment. We tested the value of behavioural phenotypes in larval amphibians for predicting ranavirus transmission in experimental trials. We found that behavioural phenotypes characterized by latency-to-food and swimming profiles were predictive of disease susceptibility and infectiousness defined as the capacity of an infected host to transmit an infection by contacts. While viral shedding rates were positively associated with transmission, we also found an inverse relationship between contacts and infections. Together these results suggest intrinsic traits that influence behaviour and the quantity of pathogens shed during conspecific interactions may be an important contributor to ranavirus transmission. These results suggest that behavioural phenotypes provide a means to identify individuals more likely to spread disease and thus give insights into disease outbreaks that threaten wildlife and humans. PMID:27555652

  9. Accurate Quantification of Disease Markers in Human Serum Using Iron Oxide Nanoparticle-linked Immunosorbent Assay

    PubMed Central

    Zhang, Linlin; Tong, Sheng; Zhou, Jun; Bao, Gang

    2016-01-01

    Accurate and reliable quantification of biomarkers in the blood is essential in disease screening and diagnosis. Here we describe an iron oxide nanoparticle (IONP)-linked immunosorbent assay (ILISA) for detecting biomolecules in human serum. Sandwich ILISA was optimized for the detection of four important serological markers, IgA, IgG, IgM, and C-reactive protein (CRP), and assessed with normal sera, simulated disease-state sera and the serum samples from patients infected with West Nile virus (WNV) or human herpes virus (HHV). Our study shows that using the detection assay formulated with 18.8 nm wüstite nanocrystals, ILISA can achieve sub-picomolar detection sensitivity, and all four markers can be accurately quantified over a large dynamic range. In addition, ILISA is not susceptible to variations in operating procedures and shows better linearity and higher stability compared with ELISA, which facilitates its integration into detection methods suitable for point of care. Our results demonstrate that ILISA is a simple and versatile nanoplatform for highly sensitive and reliable detection of serological biomarkers in biomedical research and clinical applications. PMID:27375784

  10. Disease Prediction Models and Operational Readiness

    SciTech Connect

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  11. Accurate prediction model of bead geometry in crimping butt of the laser brazing using generalized regression neural network

    NASA Astrophysics Data System (ADS)

    Rong, Y. M.; Chang, Y.; Huang, Y.; Zhang, G. J.; Shao, X. Y.

    2015-12-01

    There are few researches that concentrate on the prediction of the bead geometry for laser brazing with crimping butt. This paper addressed the accurate prediction of the bead profile by developing a generalized regression neural network (GRNN) algorithm. Firstly GRNN model was developed and trained to decrease the prediction error that may be influenced by the sample size. Then the prediction accuracy was demonstrated by comparing with other articles and back propagation artificial neural network (BPNN) algorithm. Eventually the reliability and stability of GRNN model were discussed from the points of average relative error (ARE), mean square error (MSE) and root mean square error (RMSE), while the maximum ARE and MSE were 6.94% and 0.0303 that were clearly less than those (14.28% and 0.0832) predicted by BPNN. Obviously, it was proved that the prediction accuracy was improved at least 2 times, and the stability was also increased much more.

  12. Advanced tests for early and accurate diagnosis of Creutzfeldt-Jakob disease.

    PubMed

    Zanusso, Gianluigi; Monaco, Salvatore; Pocchiari, Maurizio; Caughey, Byron

    2016-06-01

    Early and accurate diagnosis of Creutzfeldt-Jakob disease (CJD) is a necessary to distinguish this untreatable disease from treatable rapidly progressive dementias, and to prevent iatrogenic transmission. Currently, definitive diagnosis of CJD requires detection of the abnormally folded, CJD-specific form of protease-resistant prion protein (PrP(CJD)) in brain tissue obtained postmortem or via biopsy; therefore, diagnosis of sporadic CJD in clinical practice is often challenging. Supporting investigations, including MRI, EEG and conventional analyses of cerebrospinal fluid (CSF) biomarkers, are helpful in the diagnostic work-up, but do not allow definitive diagnosis. Recently, novel ultrasensitive seeding assays, based on the amplified detection of PrP(CJD), have improved the diagnostic process; for example, real-time quaking-induced conversion (RT-QuIC) is a sensitive method to detect prion-seeding activity in brain homogenate from humans with any subtype of sporadic CJD. RT-QuIC can also be used for in vivo diagnosis of CJD: its diagnostic sensitivity in detecting PrP(CJD) in CSF samples is 96%, and its specificity is 100%. Recently, we provided evidence that RT-QuIC of olfactory mucosa brushings is a 97% sensitive and 100% specific for sporadic CJD. These assays provide a basis for definitive antemortem diagnosis of prion diseases and, in doing so, improve prospects for reducing the risk of prion transmission. Moreover, they can be used to evaluate outcome measures in therapeutic trials for these as yet untreatable infections. PMID:27174240

  13. Type 1 diabetes: A predictable disease

    PubMed Central

    Simmons, Kimber M; Michels, Aaron W

    2015-01-01

    Type 1 diabetes (T1D) is an autoimmune disease characterized by loss of insulin producing beta cells and reliance on exogenous insulin for survival. T1D is one of the most common chronic diseases in childhood and the incidence is increasing, especially in children less than 5 years of age. In individuals with a genetic predisposition, an unidentified trigger initiates an abnormal immune response and the development of islet autoantibodies directed against proteins in insulin producing beta cells. There are currently four biochemical islet autoantibodies measured in the serum directed against insulin, glutamic decarboxylase, islet antigen 2, and zinc transporter 8. Development of islet autoantibodies occurs before clinical diagnosis of T1D, making T1D a predictable disease in an individual with 2 or more autoantibodies. Screening for islet autoantibodies is still predominantly done through research studies, but efforts are underway to screen the general population. The benefits of screening for islet autoantibodies include decreasing the incidence of diabetic ketoacidosis that can be life threatening, initiating insulin therapy sooner in the disease process, and evaluating safe and specific therapies in large randomized clinical intervention trials to delay or prevent progression to diabetes onset. PMID:25897349

  14. Accurate Sample Assignment in a Multiplexed, Ultrasensitive, High-Throughput Sequencing Assay for Minimal Residual Disease.

    PubMed

    Bartram, Jack; Mountjoy, Edward; Brooks, Tony; Hancock, Jeremy; Williamson, Helen; Wright, Gary; Moppett, John; Goulden, Nick; Hubank, Mike

    2016-07-01

    High-throughput sequencing (HTS) (next-generation sequencing) of the rearranged Ig and T-cell receptor genes promises to be less expensive and more sensitive than current methods of monitoring minimal residual disease (MRD) in patients with acute lymphoblastic leukemia. However, the adoption of new approaches by clinical laboratories requires careful evaluation of all potential sources of error and the development of strategies to ensure the highest accuracy. Timely and efficient clinical use of HTS platforms will depend on combining multiple samples (multiplexing) in each sequencing run. Here we examine the Ig heavy-chain gene HTS on the Illumina MiSeq platform for MRD. We identify errors associated with multiplexing that could potentially impact the accuracy of MRD analysis. We optimize a strategy that combines high-purity, sequence-optimized oligonucleotides, dual indexing, and an error-aware demultiplexing approach to minimize errors and maximize sensitivity. We present a probability-based, demultiplexing pipeline Error-Aware Demultiplexer that is suitable for all MiSeq strategies and accurately assigns samples to the correct identifier without excessive loss of data. Finally, using controls quantified by digital PCR, we show that HTS-MRD can accurately detect as few as 1 in 10(6) copies of specific leukemic MRD. PMID:27183494

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    PubMed Central

    Braun, Tatjana; Koehler Leman, Julia; Lange, Oliver F.

    2015-01-01

    Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs. PMID:26713437

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

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

    PubMed

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

    2016-03-21

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

  19. LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach.

    PubMed

    Zimmermann, Olav; Hansmann, Ulrich H E

    2008-09-01

    Constraint generation for 3d structure prediction and structure-based database searches benefit from fine-grained prediction of local structure. In this work, we present LOCUSTRA, a novel scheme for the multiclass prediction of local structure that uses two layers of support vector machines (SVM). Using a 16-letter structural alphabet from de Brevern et al. (Proteins: Struct., Funct., Bioinf. 2000, 41, 271-287), we assess its prediction ability for an independent test set of 222 proteins and compare our method to three-class secondary structure prediction and direct prediction of dihedral angles. The prediction accuracy is Q16=61.0% for the 16 classes of the structural alphabet and Q3=79.2% for a simple mapping to the three secondary classes helix, sheet, and coil. We achieve a mean phi(psi) error of 24.74 degrees (38.35 degrees) and a median RMSDA (root-mean-square deviation of the (dihedral) angles) per protein chain of 52.1 degrees. These results compare favorably with related approaches. The LOCUSTRA web server is freely available to researchers at http://www.fz-juelich.de/nic/cbb/service/service.php. PMID:18763837

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

    PubMed Central

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

    2010-01-01

    Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent. PMID:22163414

  1. DISPLAR: an accurate method for predicting DNA-binding sites on protein surfaces

    PubMed Central

    Tjong, Harianto; Zhou, Huan-Xiang

    2007-01-01

    Structural and physical properties of DNA provide important constraints on the binding sites formed on surfaces of DNA-targeting proteins. Characteristics of such binding sites may form the basis for predicting DNA-binding sites from the structures of proteins alone. Such an approach has been successfully developed for predicting protein–protein interface. Here this approach is adapted for predicting DNA-binding sites. We used a representative set of 264 protein–DNA complexes from the Protein Data Bank to analyze characteristics and to train and test a neural network predictor of DNA-binding sites. The input to the predictor consisted of PSI-blast sequence profiles and solvent accessibilities of each surface residue and 14 of its closest neighboring residues. Predicted DNA-contacting residues cover 60% of actual DNA-contacting residues and have an accuracy of 76%. This method significantly outperforms previous attempts of DNA-binding site predictions. Its application to the prion protein yielded a DNA-binding site that is consistent with recent NMR chemical shift perturbation data, suggesting that it can complement experimental techniques in characterizing protein–DNA interfaces. PMID:17284455

  2. Using complete genome comparisons to identify sequences whose presence accurately predicts clinically important phenotypes.

    PubMed

    Hall, Barry G; Cardenas, Heliodoro; Barlow, Miriam

    2013-01-01

    In clinical settings it is often important to know not just the identity of a microorganism, but also the danger posed by that particular strain. For instance, Escherichia coli can range from being a harmless commensal to being a very dangerous enterohemorrhagic (EHEC) strain. Determining pathogenic phenotypes can be both time consuming and expensive. Here we propose a simple, rapid, and inexpensive method of predicting pathogenic phenotypes on the basis of the presence or absence of short homologous DNA segments in an isolate. Our method compares completely sequenced genomes without the necessity of genome alignments in order to identify the presence or absence of the segments to produce an automatic alignment of the binary string that describes each genome. Analysis of the segment alignment allows identification of those segments whose presence strongly predicts a phenotype. Clinical application of the method requires nothing more that PCR amplification of each of the set of predictive segments. Here we apply the method to identifying EHEC strains of E. coli and to distinguishing E. coli from Shigella. We show in silico that with as few as 8 predictive sequences, if even three of those predictive sequences are amplified the probability of being EHEC or Shigella is >0.99. The method is thus very robust to the occasional amplification failure for spurious reasons. Experimentally, we apply the method to screening a set of 98 isolates to distinguishing E. coli from Shigella, and EHEC from non-EHEC E. coli strains and show that all isolates are correctly identified. PMID:23935901

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

    NASA Astrophysics Data System (ADS)

    Dale, Andy; Stolpovsky, Konstantin; Wallmann, Klaus

    2016-04-01

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

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

    PubMed Central

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

    2016-01-01

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

  5. Accurate and inexpensive prediction of the color optical properties of anthocyanins in solution.

    PubMed

    Ge, Xiaochuan; Timrov, Iurii; Binnie, Simon; Biancardi, Alessandro; Calzolari, Arrigo; Baroni, Stefano

    2015-04-23

    The simulation of the color optical properties of molecular dyes in liquid solution requires the calculation of time evolution of the solute absorption spectra fluctuating in the solvent at finite temperature. Time-averaged spectra can be directly evaluated by combining ab initio Car-Parrinello molecular dynamics and time-dependent density functional theory calculations. The inclusion of hybrid exchange-correlation functionals, necessary for the prediction of the correct transition frequencies, prevents one from using these techniques for the simulation of the optical properties of large realistic systems. Here we present an alternative approach for the prediction of the color of natural dyes in solution with a low computational cost. We applied this approach to representative anthocyanin dyes: the excellent agreement between the simulated and the experimental colors makes this method a straightforward and inexpensive tool for the high-throughput prediction of colors of molecules in liquid solvents. PMID:25830823

  6. Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes

    PubMed Central

    Victora, Andrea; Möller, Heiko M.; Exner, Thomas E.

    2014-01-01

    NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3–0.6 ppm and correlation coefficients (r2) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization. PMID:25404135

  7. Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes.

    PubMed

    Victora, Andrea; Möller, Heiko M; Exner, Thomas E

    2014-12-16

    NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3-0.6 ppm and correlation coefficients (r(2)) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization. PMID:25404135

  8. Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

    PubMed Central

    Luo, Longqiang; Li, Dingfang; Zhang, Wen; Tu, Shikui; Zhu, Xiaopeng; Tian, Gang

    2016-01-01

    Background Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete. Methods In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models. Results We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets. Conclusions Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File. PMID:27074043

  9. Viewing men's faces does not lead to accurate predictions of trustworthiness

    PubMed Central

    Efferson, Charles; Vogt, Sonja

    2013-01-01

    The evolution of cooperation requires some mechanism that reduces the risk of exploitation for cooperative individuals. Recent studies have shown that men with wide faces are anti-social, and they are perceived that way by others. This suggests that people could use facial width to identify anti-social men and thus limit the risk of exploitation. To see if people can make accurate inferences like this, we conducted a two-part experiment. First, males played a sequential social dilemma, and we took photographs of their faces. Second, raters then viewed these photographs and guessed how second movers behaved. Raters achieved significant accuracy by guessing that second movers exhibited reciprocal behaviour. Raters were not able to use the photographs to further improve accuracy. Indeed, some raters used the photographs to their detriment; they could have potentially achieved greater accuracy and earned more money by ignoring the photographs and assuming all second movers reciprocate. PMID:23308340

  10. Accurate prediction of the ammonia probes of a variable proton-to-electron mass ratio

    NASA Astrophysics Data System (ADS)

    Owens, A.; Yurchenko, S. N.; Thiel, W.; Špirko, V.

    2015-07-01

    A comprehensive study of the mass sensitivity of the vibration-rotation-inversion transitions of 14NH3, 15NH3, 14ND3 and 15ND3 is carried out variationally using the TROVE approach. Variational calculations are robust and accurate, offering a new way to compute sensitivity coefficients. Particular attention is paid to the Δk = ±3 transitions between the accidentally coinciding rotation-inversion energy levels of the ν2 = 0+, 0-, 1+ and 1- states, and the inversion transitions in the ν4 = 1 state affected by the `giant' l-type doubling effect. These transitions exhibit highly anomalous sensitivities, thus appearing as promising probes of a possible cosmological variation of the proton-to-electron mass ratio μ. Moreover, a simultaneous comparison of the calculated sensitivities reveals a sizeable isotopic dependence which could aid an exclusive ammonia detection.

  11. Accurate, conformation-dependent predictions of solvent effects on protein ionization constants

    PubMed Central

    Barth, P.; Alber, T.; Harbury, P. B.

    2007-01-01

    Predicting how aqueous solvent modulates the conformational transitions and influences the pKa values that regulate the biological functions of biomolecules remains an unsolved challenge. To address this problem, we developed FDPB_MF, a rotamer repacking method that exhaustively samples side chain conformational space and rigorously calculates multibody protein–solvent interactions. FDPB_MF predicts the effects on pKa values of various solvent exposures, large ionic strength variations, strong energetic couplings, structural reorganizations and sequence mutations. The method achieves high accuracy, with root mean square deviations within 0.3 pH unit of the experimental values measured for turkey ovomucoid third domain, hen lysozyme, Bacillus circulans xylanase, and human and Escherichia coli thioredoxins. FDPB_MF provides a faithful, quantitative assessment of electrostatic interactions in biological macromolecules. PMID:17360348

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

    PubMed

    El-Manzalawy, Yasser; Abbas, Mostafa; Malluhi, Qutaibah; Honavar, Vasant

    2016-01-01

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

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

    PubMed Central

    EL-Manzalawy, Yasser; Abbas, Mostafa; Malluhi, Qutaibah; Honavar, Vasant

    2016-01-01

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

  14. Accurate Fault Prediction of BlueGene/P RAS Logs Via Geometric Reduction

    SciTech Connect

    Jones, Terry R; Kirby, Michael; Ladd, Joshua S; Dreisigmeyer, David; Thompson, Joshua

    2010-01-01

    The authors are building two algorithms for fault prediction using raw system-log data. This work is preliminary, and has only been applied to a limited dataset, however the results seem promising. The conclusions are that: (1) obtaining useful data from RAS-logs is challenging; (2) extracting concentrated information improves efficiency and accuracy; and (3) function evaluation algorithms are fast and lend well to scaling.

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

    PubMed Central

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2009-12-24

    In the 2007 blind test of crystal structure prediction hosted by the Cambridge Crystallographic Data Centre (CCDC), a hybrid DFT/MM method correctly ranked each of the four experimental structures as having the lowest lattice energy of all the crystal structures predicted for each molecule. The work presented here further validates this hybrid method by optimizing the crystal structures (experimental and submitted) of the first three CCDC blind tests held in 1999, 2001, and 2004. Except for the crystal structures of compound IX, all structures were reminimized and ranked according to their lattice energies. The hybrid method computes the lattice energy of a crystal structure as the sum of the DFT total energy and a van der Waals (dispersion) energy correction. Considering all four blind tests, the crystal structure with the lowest lattice energy corresponds to the experimentally observed structure for 12 out of 14 molecules. Moreover, good geometrical agreement is observed between the structures determined by the hybrid method and those measured experimentally. In comparison with the correct submissions made by the blind test participants, all hybrid optimized crystal structures (apart from compound II) have the smallest calculated root mean squared deviations from the experimentally observed structures. It is predicted that a new polymorph of compound V exists under pressure. PMID:19950907

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

    PubMed

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-11-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    SciTech Connect

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

    2013-11-01

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

  1. Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System

    PubMed Central

    Norouzi, Jamshid; Mirbagheri, Seyed Ahmad; Mazdeh, Mitra Mahdavi; Hosseini, Seyed Ahmad

    2016-01-01

    Background. Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. Methods. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m2 of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Results. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Conclusions. Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods. PMID:27022406

  2. 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. PMID:24375512

  3. More accurate predictions with transonic Navier-Stokes methods through improved turbulence modeling

    NASA Technical Reports Server (NTRS)

    Johnson, Dennis A.

    1989-01-01

    Significant improvements in predictive accuracies for off-design conditions are achievable through better turbulence modeling; and, without necessarily adding any significant complication to the numerics. One well established fact about turbulence is it is slow to respond to changes in the mean strain field. With the 'equilibrium' algebraic turbulence models no attempt is made to model this characteristic and as a consequence these turbulence models exaggerate the turbulent boundary layer's ability to produce turbulent Reynolds shear stresses in regions of adverse pressure gradient. As a consequence, too little momentum loss within the boundary layer is predicted in the region of the shock wave and along the aft part of the airfoil where the surface pressure undergoes further increases. Recently, a 'nonequilibrium' algebraic turbulence model was formulated which attempts to capture this important characteristic of turbulence. This 'nonequilibrium' algebraic model employs an ordinary differential equation to model the slow response of the turbulence to changes in local flow conditions. In its original form, there was some question as to whether this 'nonequilibrium' model performed as well as the 'equilibrium' models for weak interaction cases. However, this turbulence model has since been further improved wherein it now appears that this turbulence model performs at least as well as the 'equilibrium' models for weak interaction cases and for strong interaction cases represents a very significant improvement. The performance of this turbulence model relative to popular 'equilibrium' models is illustrated for three airfoil test cases of the 1987 AIAA Viscous Transonic Airfoil Workshop, Reno, Nevada. A form of this 'nonequilibrium' turbulence model is currently being applied to wing flows for which similar improvements in predictive accuracy are being realized.

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

    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.

  5. Accurate prediction of lattice energies and structures of molecular crystals with molecular quantum chemistry methods.

    PubMed

    Fang, Tao; Li, Wei; Gu, Fangwei; Li, Shuhua

    2015-01-13

    We extend the generalized energy-based fragmentation (GEBF) approach to molecular crystals under periodic boundary conditions (PBC), and we demonstrate the performance of the method for a variety of molecular crystals. With this approach, the lattice energy of a molecular crystal can be obtained from the energies of a series of embedded subsystems, which can be computed with existing advanced molecular quantum chemistry methods. The use of the field compensation method allows the method to take long-range electrostatic interaction of the infinite crystal environment into account and make the method almost translationally invariant. The computational cost of the present method scales linearly with the number of molecules in the unit cell. Illustrative applications demonstrate that the PBC-GEBF method with explicitly correlated quantum chemistry methods is capable of providing accurate descriptions on the lattice energies and structures for various types of molecular crystals. In addition, this approach can be employed to quantify the contributions of various intermolecular interactions to the theoretical lattice energy. Such qualitative understanding is very useful for rational design of molecular crystals. PMID:26574207

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

    PubMed Central

    Wang, Zhiheng; Yang, Qianqian; Li, Tonghua; Cong, Peisheng

    2015-01-01

    The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database. Availability The DisoMCS is available at http://cal.tongji.edu.cn/disorder/. PMID:26090958

  7. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons

    SciTech Connect

    Oyeyemi, Victor B.; Krisiloff, David B.; Keith, John A.; Libisch, Florian; Pavone, Michele; Carter, Emily A.

    2014-01-28

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs.

  8. Accurate predictions of dielectrophoretic force and torque on particles with strong mutual field, particle, and wall interactions

    NASA Astrophysics Data System (ADS)

    Liu, Qianlong; Reifsnider, Kenneth

    2012-11-01

    The basis of dielectrophoresis (DEP) is the prediction of the force and torque on particles. The classical approach to the prediction is based on the effective moment method, which, however, is an approximate approach, assumes infinitesimal particles. Therefore, it is well-known that for finite-sized particles, the DEP approximation is inaccurate as the mutual field, particle, wall interactions become strong, a situation presently attracting extensive research for practical significant applications. In the present talk, we provide accurate calculations of the force and torque on the particles from first principles, by directly resolving the local geometry and properties and accurately accounting for the mutual interactions for finite-sized particles with both dielectric polarization and conduction in a sinusoidally steady-state electric field. Since the approach has a significant advantage, compared to other numerical methods, to efficiently simulate many closely packed particles, it provides an important, unique, and accurate technique to investigate complex DEP phenomena, for example heterogeneous mixtures containing particle chains, nanoparticle assembly, biological cells, non-spherical effects, etc. This study was supported by the Department of Energy under funding for an EFRC (the HeteroFoaM Center), grant no. DE-SC0001061.

  9. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons.

    PubMed

    Oyeyemi, Victor B; Krisiloff, David B; Keith, John A; Libisch, Florian; Pavone, Michele; Carter, Emily A

    2014-01-28

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs. PMID:25669533

  10. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons

    NASA Astrophysics Data System (ADS)

    Oyeyemi, Victor B.; Krisiloff, David B.; Keith, John A.; Libisch, Florian; Pavone, Michele; Carter, Emily A.

    2014-01-01

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs.

  11. PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction.

    PubMed

    Liu, Lili; Zhang, Zijun; Mei, Qian; Chen, Ming

    2013-01-01

    Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ~10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at http://bis.zju.edu.cn/psi/. PMID:24194827

  12. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    PubMed

    Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

    2015-01-01

    For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease. PMID:25938675

  13. The Compensatory Reserve For Early and Accurate Prediction Of Hemodynamic Compromise: A Review of the Underlying Physiology.

    PubMed

    Convertino, Victor A; Wirt, Michael D; Glenn, John F; Lein, Brian C

    2016-06-01

    Shock is deadly and unpredictable if it is not recognized and treated in early stages of hemorrhage. Unfortunately, measurements of standard vital signs that are displayed on current medical monitors fail to provide accurate or early indicators of shock because of physiological mechanisms that effectively compensate for blood loss. As a result of new insights provided by the latest research on the physiology of shock using human experimental models of controlled hemorrhage, it is now recognized that measurement of the body's reserve to compensate for reduced circulating blood volume is the single most important indicator for early and accurate assessment of shock. We have called this function the "compensatory reserve," which can be accurately assessed by real-time measurements of changes in the features of the arterial waveform. In this paper, the physiology underlying the development and evaluation of a new noninvasive technology that allows for real-time measurement of the compensatory reserve will be reviewed, with its clinical implications for earlier and more accurate prediction of shock. PMID:26950588

  14. A novel method to predict visual field progression more accurately, using intraocular pressure measurements in glaucoma patients.

    PubMed

    2016-01-01

    Visual field (VF) data were retrospectively obtained from 491 eyes in 317 patients with open angle glaucoma who had undergone ten VF tests (Humphrey Field Analyzer, 24-2, SITA standard). First, mean of total deviation values (mTD) in the tenth VF was predicted using standard linear regression of the first five VFs (VF1-5) through to using all nine preceding VFs (VF1-9). Then an 'intraocular pressure (IOP)-integrated VF trend analysis' was carried out by simply using time multiplied by IOP as the independent term in the linear regression model. Prediction errors (absolute prediction error or root mean squared error: RMSE) for predicting mTD and also point wise TD values of the tenth VF were obtained from both approaches. The mTD absolute prediction errors associated with the IOP-integrated VF trend analysis were significantly smaller than those from the standard trend analysis when VF1-6 through to VF1-8 were used (p < 0.05). The point wise RMSEs from the IOP-integrated trend analysis were significantly smaller than those from the standard trend analysis when VF1-5 through to VF1-9 were used (p < 0.05). This was especially the case when IOP was measured more frequently. Thus a significantly more accurate prediction of VF progression is possible using a simple trend analysis that incorporates IOP measurements. PMID:27562553

  15. A novel method to predict visual field progression more accurately, using intraocular pressure measurements in glaucoma patients

    PubMed Central

    Asaoka, Ryo; Fujino, Yuri; Murata, Hiroshi; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Shoji, Nobuyuki

    2016-01-01

    Visual field (VF) data were retrospectively obtained from 491 eyes in 317 patients with open angle glaucoma who had undergone ten VF tests (Humphrey Field Analyzer, 24-2, SITA standard). First, mean of total deviation values (mTD) in the tenth VF was predicted using standard linear regression of the first five VFs (VF1-5) through to using all nine preceding VFs (VF1-9). Then an ‘intraocular pressure (IOP)-integrated VF trend analysis’ was carried out by simply using time multiplied by IOP as the independent term in the linear regression model. Prediction errors (absolute prediction error or root mean squared error: RMSE) for predicting mTD and also point wise TD values of the tenth VF were obtained from both approaches. The mTD absolute prediction errors associated with the IOP-integrated VF trend analysis were significantly smaller than those from the standard trend analysis when VF1-6 through to VF1-8 were used (p < 0.05). The point wise RMSEs from the IOP-integrated trend analysis were significantly smaller than those from the standard trend analysis when VF1-5 through to VF1-9 were used (p < 0.05). This was especially the case when IOP was measured more frequently. Thus a significantly more accurate prediction of VF progression is possible using a simple trend analysis that incorporates IOP measurements. PMID:27562553

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

    NASA Astrophysics Data System (ADS)

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

    2013-06-01

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

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

    SciTech Connect

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

    2008-10-20

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

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

    SciTech Connect

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

    2009-02-13

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

  19. Sequence features accurately predict genome-wide MeCP2 binding in vivo.

    PubMed

    Rube, H Tomas; Lee, Wooje; Hejna, Miroslav; Chen, Huaiyang; Yasui, Dag H; Hess, John F; LaSalle, Janine M; Song, Jun S; Gong, Qizhi

    2016-01-01

    Methyl-CpG binding protein 2 (MeCP2) is critical for proper brain development and expressed at near-histone levels in neurons, but the mechanism of its genomic localization remains poorly understood. Using high-resolution MeCP2-binding data, we show that DNA sequence features alone can predict binding with 88% accuracy. Integrating MeCP2 binding and DNA methylation in a probabilistic graphical model, we demonstrate that previously reported genome-wide association with methylation is in part due to MeCP2's affinity to GC-rich chromatin, a result replicated using published data. Furthermore, MeCP2 co-localizes with nucleosomes. Finally, MeCP2 binding downstream of promoters correlates with increased expression in Mecp2-deficient neurons. PMID:27008915

  20. nuMap: a web platform for accurate prediction of nucleosome positioning.

    PubMed

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

    2014-10-01

    Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site. PMID:25220945

  1. Sequence features accurately predict genome-wide MeCP2 binding in vivo

    PubMed Central

    Rube, H. Tomas; Lee, Wooje; Hejna, Miroslav; Chen, Huaiyang; Yasui, Dag H.; Hess, John F.; LaSalle, Janine M.; Song, Jun S.; Gong, Qizhi

    2016-01-01

    Methyl-CpG binding protein 2 (MeCP2) is critical for proper brain development and expressed at near-histone levels in neurons, but the mechanism of its genomic localization remains poorly understood. Using high-resolution MeCP2-binding data, we show that DNA sequence features alone can predict binding with 88% accuracy. Integrating MeCP2 binding and DNA methylation in a probabilistic graphical model, we demonstrate that previously reported genome-wide association with methylation is in part due to MeCP2's affinity to GC-rich chromatin, a result replicated using published data. Furthermore, MeCP2 co-localizes with nucleosomes. Finally, MeCP2 binding downstream of promoters correlates with increased expression in Mecp2-deficient neurons. PMID:27008915

  2. An accurate and efficient method for prediction of the long-term evolution of space debris in the geosynchronous region

    NASA Astrophysics Data System (ADS)

    McNamara, Roger P.; Eagle, C. D.

    1992-08-01

    Planetary Observer High Accuracy Orbit Prediction Program (POHOP), an existing numerical integrator, was modified with the solar and lunar formulae developed by T.C. Van Flandern and K.F. Pulkkinen to provide the accuracy required to evaluate long-term orbit characteristics of objects on the geosynchronous region. The orbit of a 1000 kg class spacecraft is numerically integrated over 50 years using both the original and the more accurate solar and lunar ephemerides methods. Results of this study demonstrate that, over the long term, for an object located in the geosynchronous region, the more accurate solar and lunar ephemerides effects on the objects's position are significantly different than using the current POHOP ephemeris.

  3. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

    NASA Astrophysics Data System (ADS)

    Balabin, Roman M.; Lomakina, Ekaterina I.

    2009-08-01

    Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree-Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6±0.2 kcal mol-1. In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

  4. 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.

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

    PubMed

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

    2009-04-01

    The current standard method for predicting contact allergenicity is the murine local lymph node assay (LLNA). Public objection to the use of animals in testing of cosmetics makes the development of a system that does not use sentient animals highly desirable. The chorioallantoic membrane (CAM) of the chick egg has been extensively used for the growth of normal and transformed mammalian tissues. The CAM is not innervated, and embryos are sacrificed before the development of pain perception. The aim of this study was to determine whether the sensitization phase of contact dermatitis to known cosmetic allergens can be quantified using CAM-engrafted human skin and how these results compare with published EC3 data obtained with the LLNA. We studied six common molecules used in allergen testing and quantified migration of epidermal Langerhans cells (LC) as a measure of their allergic potency. All agents with known allergic potential induced statistically significant migration of LC. The data obtained correlated well with published data for these allergens generated using the LLNA test. The human-skin CAM model therefore has great potential as an inexpensive, non-radioactive, in vivo alternative to the LLNA, which does not require the use of sentient animals. In addition, this system has the advantage of testing the allergic response of human, rather than animal skin. PMID:19054059

  6. Accurate prediction of the refractive index of polymers using first principles and data modeling

    NASA Astrophysics Data System (ADS)

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

    Organic polymers with a high refractive index (RI) have recently attracted considerable interest due to their potential application in optical and optoelectronic devices. The ability to tailor the molecular structure of polymers is the key to increasing the accessible RI values. Our work concerns the creation of predictive in silico models for the optical properties of organic polymers, the screening of large-scale candidate libraries, and the mining of the resulting data to extract the underlying design principles that govern their performance. This work was set up to guide our experimentalist partners and allow them to target the most promising candidates. Our model is based on the Lorentz-Lorenz equation and thus includes the polarizability and number density values for each candidate. For the former, we performed a detailed benchmark study of different density functionals, basis sets, and the extrapolation scheme towards the polymer limit. For the number density we devised an exceedingly efficient machine learning approach to correlate the polymer structure and the packing fraction in the bulk material. We validated the proposed RI model against the experimentally known RI values of 112 polymers. We could show that the proposed combination of physical and data modeling is both successful and highly economical to characterize a wide range of organic polymers, which is a prerequisite for virtual high-throughput screening.

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

    SciTech Connect

    Margot Gerritsen

    2008-10-31

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

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

    SciTech Connect

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke

    2015-11-15

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

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

    PubMed Central

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

    2015-01-01

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

  10. Accurate prediction of interference minima in linear molecular harmonic spectra by a modified two-center model

    NASA Astrophysics Data System (ADS)

    Xin, Cui; Di-Yu, Zhang; Gao, Chen; Ji-Gen, Chen; Si-Liang, Zeng; Fu-Ming, Guo; Yu-Jun, Yang

    2016-03-01

    We demonstrate that the interference minima in the linear molecular harmonic spectra can be accurately predicted by a modified two-center model. Based on systematically investigating the interference minima in the linear molecular harmonic spectra by the strong-field approximation (SFA), it is found that the locations of the harmonic minima are related not only to the nuclear distance between the two main atoms contributing to the harmonic generation, but also to the symmetry of the molecular orbital. Therefore, we modify the initial phase difference between the double wave sources in the two-center model, and predict the harmonic minimum positions consistent with those simulated by SFA. Project supported by the National Basic Research Program of China (Grant No. 2013CB922200) and the National Natural Science Foundation of China (Grant Nos. 11274001, 11274141, 11304116, 11247024, and 11034003), and the Jilin Provincial Research Foundation for Basic Research, China (Grant Nos. 20130101012JC and 20140101168JC).

  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. Evaluating Mesoscale Numerical Weather Predictions and Spatially Distributed Meteorologic Forcing Data for Developing Accurate SWE Forecasts over Large Mountain Basins

    NASA Astrophysics Data System (ADS)

    Hedrick, A. R.; Marks, D. G.; Winstral, A. H.; Marshall, H. P.

    2014-12-01

    The ability to forecast snow water equivalent, or SWE, in mountain catchments would benefit many different communities ranging from avalanche hazard mitigation to water resource management. Historical model runs of Isnobal, the physically based energy balance snow model, have been produced over the 2150 km2 Boise River Basin for water years 2012 - 2014 at 100-meter resolution. Spatially distributed forcing parameters such as precipitation, wind, and relative humidity are generated from automated weather stations located throughout the watershed, and are supplied to Isnobal at hourly timesteps. Similarly, the Weather Research & Forecasting (WRF) Model provides hourly predictions of the same forcing parameters from an atmospheric physics perspective. This work aims to quantitatively compare WRF model output to the spatial meteorologic fields developed to force Isnobal, with the hopes of eventually using WRF predictions to create accurate hourly forecasts of SWE over a large mountainous basin.

  13. Accurate First-Principles Spectra Predictions for Ethylene and its Isotopologues from Full 12D AB Initio Surfaces

    NASA Astrophysics Data System (ADS)

    Delahaye, Thibault; Rey, Michael; Tyuterev, Vladimir; Nikitin, Andrei V.; Szalay, Peter

    2015-06-01

    Hydrocarbons such as ethylene (C_2H_4) and methane (CH_4) are of considerable interest for the modeling of planetary atmospheres and other astrophysical applications. Knowledge of rovibrational transitions of hydrocarbons is of primary importance in many fields but remains a formidable challenge for the theory and spectral analysis. Essentially two theoretical approaches for the computation and prediction of spectra exist. The first one is based on empirically-fitted effective spectroscopic models. Several databases aim at collecting the corresponding data but the information about C_2H_4 spectrum present in these databases remains limited, only some spectral ranges around 1000, 3000 and 6000 cm-1 being available. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. Although they do not yet reach the spectroscopic accuracy, they could 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 two necessary ingredients: (i) accurate intramolecular potential energy surface and dipole moment surface components and (ii) efficient computational methods to achieve a good numerical convergence. We report predictions of vibrational and rovibrational energy levels of C_2H_4 using our new ground state potential energy surface obtained from extended ab initio calculations. Additionally we will introduce line positions and line intensities predictions based on a new dipole moment surface for ethylene. These results will be compared with previous works on ethylene and its isotopologues.

  14. Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

    PubMed Central

    2014-01-01

    Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein–ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data. PMID:24528282

  15. A hyperspectral imaging system for an accurate prediction of the above-ground biomass of individual rice plants.

    PubMed

    Feng, Hui; Jiang, Ni; Huang, Chenglong; Fang, Wei; Yang, Wanneng; Chen, Guoxing; Xiong, Lizhong; Liu, Qian

    2013-09-01

    Biomass is an important component of the plant phenomics, and the existing methods for biomass estimation for individual plants are either destructive or lack accuracy. In this study, a hyperspectral imaging system was developed for the accurate prediction of the above-ground biomass of individual rice plants in the visible and near-infrared spectral region. First, the structure of the system and the influence of various parameters on the camera acquisition speed were established. Then the system was used to image 152 rice plants, which selected from the rice mini-core collection, in two stages, the tillering to elongation (T-E) stage and the booting to heading (B-H) stage. Several variables were extracted from the images. Following, linear stepwise regression analysis and 5-fold cross-validation were used to select effective variables for model construction and test the stability of the model, respectively. For the T-E stage, the R(2) value was 0.940 for the fresh weight (FW) and 0.935 for the dry weight (DW). For the B-H stage, the R(2) value was 0.891 for the FW and 0.783 for the DW. Moreover, estimations of the biomass using visible light images were also calculated. These comparisons showed that hyperspectral imaging performed better than the visible light imaging. Therefore, this study provides not only a stable hyperspectral imaging platform but also an accurate and nondestructive method for the prediction of biomass for individual rice plants. PMID:24089866

  16. 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. PMID:26121186

  17. Accurate prediction of unsteady and time-averaged pressure loads using a hybrid Reynolds-Averaged/large-eddy simulation technique

    NASA Astrophysics Data System (ADS)

    Bozinoski, Radoslav

    Significant research has been performed over the last several years on understanding the unsteady aerodynamics of various fluid flows. Much of this work has focused on quantifying the unsteady, three-dimensional flow field effects which have proven vital to the accurate prediction of many fluid and aerodynamic problems. Up until recently, engineers have predominantly relied on steady-state simulations to analyze the inherently three-dimensional ow structures that are prevalent in many of today's "real-world" problems. Increases in computational capacity and the development of efficient numerical methods can change this and allow for the solution of the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations for practical three-dimensional aerodynamic applications. An integral part of this capability has been the performance and accuracy of the turbulence models coupled with advanced parallel computing techniques. This report begins with a brief literature survey of the role fully three-dimensional, unsteady, Navier-Stokes solvers have on the current state of numerical analysis. Next, the process of creating a baseline three-dimensional Multi-Block FLOw procedure called MBFLO3 is presented. Solutions for an inviscid circular arc bump, laminar at plate, laminar cylinder, and turbulent at plate are then presented. Results show good agreement with available experimental, numerical, and theoretical data. Scalability data for the parallel version of MBFLO3 is presented and shows efficiencies of 90% and higher for processes of no less than 100,000 computational grid points. Next, the description and implementation techniques used for several turbulence models are presented. Following the successful implementation of the URANS and DES procedures, the validation data for separated, non-reattaching flows over a NACA 0012 airfoil, wall-mounted hump, and a wing-body junction geometry are presented. Results for the NACA 0012 showed significant improvement in flow predictions

  18. Perceived Vulnerability to Disease Predicts Environmental Attitudes

    ERIC Educational Resources Information Center

    Prokop, Pavol; Kubiatko, Milan

    2014-01-01

    Investigating predictors of environmental attitudes may bring valuable benefits in terms of improving public awareness about biodiversity degradation and increased pro-environmental behaviour. Here we used an evolutionary approach to study environmental attitudes based on disease-threat model. We hypothesized that people vulnerable to diseases may…

  19. Predictive modeling of coral disease distribution within a reef system.

    PubMed

    Williams, Gareth J; Aeby, Greta S; Cowie, Rebecca O M; Davy, Simon K

    2010-01-01

    Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1) coral diseases show distinct associations with multiple environmental factors, 2) incorporating interactions (synergistic collinearities) among environmental variables is important when predicting coral disease spatial patterns, and 3) modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value) will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA), Porites tissue loss (PorTL), Porites trematodiasis (PorTrem), and Montipora white syndrome (MWS), and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT) within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response), led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to particular

  20. 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

    2015-02-01

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G(**), B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol(-1), decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol(-1). PMID:24274986

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

    PubMed

    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. 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

  3. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

    PubMed Central

    Brezovský, Jan

    2016-01-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations

  4. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

    PubMed

    Bendl, Jaroslav; Musil, Miloš; Štourač, Jan; Zendulka, Jaroslav; Damborský, Jiří; Brezovský, Jan

    2016-05-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To

  5. A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans

    PubMed Central

    Bigdeli, T. Bernard; Lee, Donghyung; Webb, Bradley Todd; Riley, Brien P.; Vladimirov, Vladimir I.; Fanous, Ayman H.; Kendler, Kenneth S.; Bacanu, Silviu-Alin

    2016-01-01

    Motivation: For genetic studies, statistically significant variants explain far less trait variance than ‘sub-threshold’ association signals. To dimension follow-up studies, researchers need to accurately estimate ‘true’ effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winner’s curse biases, which are reduced only by laborious winner’s curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities. Results:WCA of Z-scores shrinks these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values toward one, which corresponds to the zero Z-score value. Thus, WCA on Z-scores scale is a proxy for MTA on P-value scale. Therefore, to estimate Z-score noncentralities for all SNPs in genome scans, we propose FDR Inverse Quantile Transformation (FIQT). It (i) performs the simpler MTA of P-values using FDR and (ii) obtains noncentralities by back-transforming MTA P-values on Z-score scale. When compared to competitors, realistic simulations suggest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude. Practical application of FIQT to Psychiatric Genetic Consortium schizophrenia cohort predicts a non-trivial fraction of sub-threshold signals which become significant in much larger supersamples. Conclusions: FIQT is a simple, yet accurate, WCA method for Z-scores (and ORs/RRs, via simple transformations). Availability and Implementation: A 10 lines R function implementation is available at https://github.com/bacanusa/FIQT. Contact: sabacanu@vcu.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27187203

  6. Small-scale field experiments accurately scale up to predict density dependence in reef fish populations at large scales

    PubMed Central

    Steele, Mark A.; Forrester, Graham E.

    2005-01-01

    Field experiments provide rigorous tests of ecological hypotheses but are usually limited to small spatial scales. It is thus unclear whether these findings extrapolate to larger scales relevant to conservation and management. We show that the results of experiments detecting density-dependent mortality of reef fish on small habitat patches scale up to have similar effects on much larger entire reefs that are the size of small marine reserves and approach the scale at which some reef fisheries operate. We suggest that accurate scaling is due to the type of species interaction causing local density dependence and the fact that localized events can be aggregated to describe larger-scale interactions with minimal distortion. Careful extrapolation from small-scale experiments identifying species interactions and their effects should improve our ability to predict the outcomes of alternative management strategies for coral reef fishes and their habitats. PMID:16150721

  7. Small-scale field experiments accurately scale up to predict density dependence in reef fish populations at large scales.

    PubMed

    Steele, Mark A; Forrester, Graham E

    2005-09-20

    Field experiments provide rigorous tests of ecological hypotheses but are usually limited to small spatial scales. It is thus unclear whether these findings extrapolate to larger scales relevant to conservation and management. We show that the results of experiments detecting density-dependent mortality of reef fish on small habitat patches scale up to have similar effects on much larger entire reefs that are the size of small marine reserves and approach the scale at which some reef fisheries operate. We suggest that accurate scaling is due to the type of species interaction causing local density dependence and the fact that localized events can be aggregated to describe larger-scale interactions with minimal distortion. Careful extrapolation from small-scale experiments identifying species interactions and their effects should improve our ability to predict the outcomes of alternative management strategies for coral reef fishes and their habitats. PMID:16150721

  8. Effects of the inlet conditions and blood models on accurate prediction of hemodynamics in the stented coronary arteries

    NASA Astrophysics Data System (ADS)

    Jiang, Yongfei; Zhang, Jun; Zhao, Wanhua

    2015-05-01

    Hemodynamics altered by stent implantation is well-known to be closely related to in-stent restenosis. Computational fluid dynamics (CFD) method has been used to investigate the hemodynamics in stented arteries in detail and help to analyze the performances of stents. In this study, blood models with Newtonian or non-Newtonian properties were numerically investigated for the hemodynamics at steady or pulsatile inlet conditions respectively employing CFD based on the finite volume method. The results showed that the blood model with non-Newtonian property decreased the area of low wall shear stress (WSS) compared with the blood model with Newtonian property and the magnitude of WSS varied with the magnitude and waveform of the inlet velocity. The study indicates that the inlet conditions and blood models are all important for accurately predicting the hemodynamics. This will be beneficial to estimate the performances of stents and also help clinicians to select the proper stents for the patients.

  9. The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids.

    PubMed

    Sprenger, K G; Jaeger, Vance W; Pfaendtner, Jim

    2015-05-01

    We have applied molecular dynamics to calculate thermodynamic and transport properties of a set of 19 room-temperature ionic liquids. Since accurately simulating the thermophysical properties of solvents strongly depends upon the force field of choice, we tested the accuracy of the general AMBER force field, without refinement, for the case of ionic liquids. Electrostatic point charges were developed using ab initio calculations and a charge scaling factor of 0.8 to more accurately predict dynamic properties. The density, heat capacity, molar enthalpy of vaporization, self-diffusivity, and shear viscosity of the ionic liquids were computed and compared to experimentally available data, and good agreement across a wide range of cation and anion types was observed. Results show that, for a wide range of ionic liquids, the general AMBER force field, with no tuning of parameters, can reproduce a variety of thermodynamic and transport properties with similar accuracy to that of other published, often IL-specific, force fields. PMID:25853313

  10. Can oncologists predict survival for patients with progressive disease after standard chemotherapies?

    PubMed Central

    Taniyama, T.K.; Hashimoto, K.; Katsumata, N.; Hirakawa, A.; Yonemori, K.; Yunokawa, M.; Shimizu, C.; Tamura, K.; Ando, M.; Fujiwara, Y.

    2014-01-01

    Background Prediction of prognosis is important for patients so that they can make the most of the rest of their lives. Oncologists could predict survival, but the accuracy of such predictions is unclear. Methods In this observational prospective cohort study, 14 oncologists treating 9 major adult solid malignancies were asked to complete questionnaires predicting survival based on performance status, oral intake, and other clinical factors when patients experienced progressive disease after standard chemotherapies. Clinically predicted survival (cps) was calculated by the oncologists from the date of progressive disease to the predicted date of death. Actual survival (as) was compared with cps using Kaplan–Meier survival curves, and factors affecting inaccurate prediction were determined by logistic regression analysis. The prediction of survival time was considered accurate when the cps/as ratio was between 0.67 and 1.33. Results The study cohort consisted of 75 patients. Median cps was 120 days (interquartile range: 60–180 days), and median as was 121 days (interquartile range: 40–234 days). The participating oncologists accurately predicted as within a 33% range 36% of the time; the survival time was overestimated 36% of time and underestimated 28% of the time. The factors affecting the accuracy of the survival estimate were the experience of the oncologist, patient age, and information given about the palliative care unit. Conclusions Prediction of cps was accurate for just slightly more than one third of all patients in this study. Additional investigation of putative prognostic factors with a larger sample size is warranted. PMID:24764697

  11. Periodontal disease activity: a development strategy for its investigation by means of accurate 3-dimensional clinical measurement.

    PubMed

    Watts, T L; Beards, C f; Ewing, P D; Leeman, S

    1995-03-01

    The central problem in all previous approaches to clinical assessment of periodontal disease activity is the use of unidimensional measurement, which implies a number of unjustifiable assumptions. In addition, the use of unidimensional probing measurement has established that there are several distinct problems of validity and reliability in currently available techniques. The present paper begins with an analysis of these matters, leading to an approach to accurate clinical measurement of periodontitis in 3 dimensions, with the possibility of future development of a valid system for assessing the nature of disease activity. PMID:7790525

  12. Accurate prediction for atomic-level protein design and its application in diversifying the near-optimal sequence space.

    PubMed

    Fromer, Menachem; Yanover, Chen

    2009-05-15

    precisely. Examination of the predicted ensembles indicates that, for each structure, the amino acid identity at a majority of positions must be chosen extremely selectively so as to not incur significant energetic penalties. We investigate this high degree of similarity and demonstrate how more diverse near-optimal sequences can be predicted in order to systematically overcome this bottleneck for computational design. Finally, we exploit this in-depth analysis of a collection of the lowest energy sequences to suggest an explanation for previously observed experimental design results. The novel methodologies introduced here accurately portray the sequence space compatible with a protein structure and further supply a scheme to yield heterogeneous low-energy sequences, thus providing a powerful instrument for future work on protein design. PMID:19003998

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

    PubMed

    Harb, Moussab

    2015-10-14

    Using accurate first-principles quantum calculations based on DFT (including the DFPT) with the range-separated hybrid HSE06 exchange-correlation functional, we can predict the essential fundamental properties (such as bandgap, optical absorption co-efficient, dielectric constant, charge carrier effective masses and exciton binding energy) of two stable monoclinic vanadium oxynitride (VON) semiconductor crystals for solar energy conversion applications. In addition to the predicted band gaps in the optimal range for making single-junction solar cells, both polymorphs exhibit a relatively high absorption efficiency in the visible range, high dielectric constant, high charge carrier mobility and much lower exciton binding energy than the thermal energy at room temperature. Moreover, their optical absorption, dielectric and exciton dissociation properties were found to be better than those obtained for semiconductors frequently utilized in photovoltaic devices such as Si, CdTe and GaAs. These novel results offer a great opportunity for this stoichiometric VON material to be properly synthesized and considered as a new good candidate for photovoltaic applications. PMID:26351755

  14. 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.

  15. Metabolic biomarkers for predicting cardiovascular disease

    PubMed Central

    Montgomery, Jana E; Brown, Jeremiah R

    2013-01-01

    Cardiac and peripheral vascular biomarkers are increasingly becoming targets of both research and clinical practice. As of 2008, cardiovascular-related medical care accounts for greater than 20% of all the economic costs of illness in the United States. In the age of burgeoning financial pressures on the entire health care system, never has it been more important to try to understand who is at risk for cardiovascular disease in order to prevent new events. In this paper, we will discuss the cost of cardiovascular disease to society, clarify the definition of and need for biomarkers, offer an example of a current biomarker, namely high-sensitivity C-reactive protein, and finally examine the approval process for utilizing these in clinical practice. PMID:23386789

  16. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

    PubMed Central

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  17. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients.

    PubMed

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0-F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  18. Common polygenic variation enhances risk prediction for Alzheimer's disease.

    PubMed

    Escott-Price, Valentina; Sims, Rebecca; Bannister, Christian; Harold, Denise; Vronskaya, Maria; Majounie, Elisa; Badarinarayan, Nandini; Morgan, Kevin; Passmore, Peter; Holmes, Clive; Powell, John; Brayne, Carol; Gill, Michael; Mead, Simon; Goate, Alison; Cruchaga, Carlos; Lambert, Jean-Charles; van Duijn, Cornelia; Maier, Wolfgang; Ramirez, Alfredo; Holmans, Peter; Jones, Lesley; Hardy, John; Seshadri, Sudha; Schellenberg, Gerard D; Amouyel, Philippe; Williams, Julie

    2015-12-01

    The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. PMID:26490334

  19. 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. PMID:26424887

  20. 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

  1. Network-based prediction and knowledge mining of disease genes

    PubMed Central

    2015-01-01

    Background In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. Methods We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Results Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second

  2. Mathematical models for accurate prediction of atmospheric visibility with particular reference to the seasonal and environmental patterns in Hong Kong.

    PubMed

    Mui, K W; Wong, L T; Chung, L Y

    2009-11-01

    Atmospheric visibility impairment has gained increasing concern as it is associated with the existence of a number of aerosols as well as common air pollutants and produces unfavorable conditions for observation, dispersion, and transportation. This study analyzed the atmospheric visibility data measured in urban and suburban Hong Kong (two selected stations) with respect to time-matched mass concentrations of common air pollutants including nitrogen dioxide (NO(2)), nitrogen monoxide (NO), respirable suspended particulates (PM(10)), sulfur dioxide (SO(2)), carbon monoxide (CO), and meteorological parameters including air temperature, relative humidity, and wind speed. No significant difference in atmospheric visibility was reported between the two measurement locations (p > or = 0.6, t test); and good atmospheric visibility was observed more frequently in summer and autumn than in winter and spring (p < 0.01, t test). It was also found that atmospheric visibility increased with temperature but decreased with the concentrations of SO(2), CO, PM(10), NO, and NO(2). The results showed that atmospheric visibility was season dependent and would have significant correlations with temperature, the mass concentrations of PM(10) and NO(2), and the air pollution index API (correlation coefficients mid R: R mid R: > or = 0.7, p < or = 0.0001, t test). Mathematical expressions catering to the seasonal variations of atmospheric visibility were thus proposed. By comparison, the proposed visibility prediction models were more accurate than some existing regional models. In addition to improving visibility prediction accuracy, this study would be useful for understanding the context of low atmospheric visibility, exploring possible remedial measures, and evaluating the impact of air pollution and atmospheric visibility impairment in this region. PMID:18951139

  3. PROMISE of Coronary CT Angiography: Precise and Accurate Diagnosis and Prognosis in Coronary Artery Disease.

    PubMed

    Thomas, Dustin M; Branch, Kelley R; Cury, Ricardo C

    2016-04-01

    Coronary computed tomography angiography (CCTA) is a rapidly growing and powerful diagnostic test that offers a great deal of precision with respect to diagnosing coronary artery disease (CAD). Guideline statements for patients with stable ischemic heart disease have recommended CCTA for only a limited portion of intermediate-risk patients who have relative or absolute contraindications for exercise or vasodilator stress testing. The publication of two large, prospective randomized clinical trials, the Prospective Multicenter Imaging Study for Evaluation of Chest Pain and the Scottish Computed Tomography of the Heart Trial are likely to expand these indications. These new data from large trials, in addition to other studies, show that CCTA is highly sensitive for the detection of CAD, identifies high-risk patients for cardiac events based on extent or plaque morphology of CAD that would not be identified by other noninvasive means, and provides significantly greater diagnostic certainty for proper treatment, including referral for invasive coronary angiography with revascularization more appropriately. Superior diagnostic accuracy and prognostic data with CCTA, when compared with other functional stress tests, may result in a reduction in unnecessary downstream testing and cost savings. In addition, newer CCTA applications hold the promise of providing a complete evaluation of a patient's coronary anatomy as well as a per-vessel ischemic evaluation. This review focuses on the interval knowledge obtained from newer data on CCTA in patients with stable ischemic heart disease, primarily focusing on the contributions of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain and the Scottish Computed Tomography of the Heart Trial. PMID:27043808

  4. A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Cannon, William R.; Oehmen, Christopher S.; Shah, Anuj R.; Gurumoorthi, Vidhya; Lipton, Mary S.; Waters, Katrina M.

    2008-07-01

    Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares these profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. Availability: http://omics.pnl.gov/software/STEPP.php

  5. Iofetamine I 123 single photon emission computed tomography is accurate in the diagnosis of Alzheimer's disease

    SciTech Connect

    Johnson, K.A.; Holman, B.L.; Rosen, T.J.; Nagel, J.S.; English, R.J.; Growdon, J.H. )

    1990-04-01

    To determine the diagnostic accuracy of iofetamine hydrochloride I 123 (IMP) with single photon emission computed tomography in Alzheimer's disease, we studied 58 patients with AD and 15 age-matched healthy control subjects. We used a qualitative method to assess regional IMP uptake in the entire brain and to rate image data sets as normal or abnormal without knowledge of subjects'clinical classification. The sensitivity and specificity of IMP with single photon emission computed tomography in AD were 88% and 87%, respectively. In 15 patients with mild cognitive deficits (Blessed Dementia Scale score, less than or equal to 10), sensitivity was 80%. With the use of a semiquantitative measure of regional cortical IMP uptake, the parietal lobes were the most functionally impaired in AD and the most strongly associated with the patients' Blessed Dementia Scale scores. These results indicated that IMP with single photon emission computed tomography may be a useful adjunct in the clinical diagnosis of AD in early, mild disease.

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

    PubMed

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

    2016-06-01

    Interferon-induced protein with tetratricopeptide repeat 1 (IFIT1) plays a key role in growth suppression and apoptosis promotion in cancer cells. Interferon was reported to induce the expression of IFIT1 and inhibit the expression of O-6-methylguanine-DNA methyltransferase (MGMT).This study aimed to investigate the expression of IFIT1, the correlation between IFIT1 and MGMT, and their impact on the clinical outcome in newly diagnosed glioblastoma. The expression of IFIT1 and MGMT and their correlation were investigated in the tumor tissues from 70 patients with newly diagnosed glioblastoma. The effects on progression-free survival and overall survival were evaluated. Of 70 cases, 57 (81.4%) tissue samples showed high expression of IFIT1 by immunostaining. The χ(2) test indicated that the expression of IFIT1 and MGMT was negatively correlated (r = -0.288, P = .016). Univariate and multivariate analyses confirmed high IFIT1 expression as a favorable prognostic indicator for progression-free survival (P = .005 and .017) and overall survival (P = .001 and .001), respectively. Patients with 2 favorable factors (high IFIT1 and low MGMT) had an improved prognosis as compared with others. The results demonstrated significantly increased expression of IFIT1 in newly diagnosed glioblastoma tissue. The negative correlation between IFIT1 and MGMT expression may be triggered by interferon. High IFIT1 can be a predictive biomarker of favorable clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma. PMID:26980050

  7. The Crop Disease and Pest Warning and Prediction System

    NASA Astrophysics Data System (ADS)

    Luo, Juhua; Huang, Wenjiang; Wang, Jihua; Wei, Chaoling

    The aim of this study was to establish the warning and prediction system for crop diseases and pests based on SuperMap IS. NET geographic information system (GIS), which was developed by Supermap company. In this system, the author used GIS and remote sensing (RS) technology. The system could transform data information into a geographical information map to show the occurrence degree and distribution on various diseases and pests. This paper described mainly warning flow, database design and the main functions of the system. Finally, the system realized successfully the warning of the wheat stripe rust in Xifeng region of Qingyang city in Gansu province in 2002, and the prediction result was satisfactory. It indicated that we could classify and predict diseases and pests, and select right time and technology to control the diseases and pests by this GIS system.

  8. Stratified neutrophil-to-lymphocyte ratio accurately predict mortality risk in hepatocellular carcinoma patients following curative liver resection

    PubMed Central

    Huang, Gui-Qian; Zhu, Gui-Qi; Liu, Yan-Long; Wang, Li-Ren; Braddock, Martin; Zheng, Ming-Hua; Zhou, Meng-Tao

    2016-01-01

    Objectives Neutrophil lymphocyte ratio (NLR) has been shown to predict prognosis of cancers in several studies. This study was designed to evaluate the impact of stratified NLR in patients who have received curative liver resection (CLR) for hepatocellular carcinoma (HCC). Methods A total of 1659 patients who underwent CLR for suspected HCC between 2007 and 2014 were reviewed. The preoperative NLR was categorized into quartiles based on the quantity of the study population and the distribution of NLR. Hazard ratios (HRs) and 95% confidence intervals (CIs) were significantly associated with overall survival (OS) and derived by Cox proportional hazard regression analyses. Univariate and multivariate Cox proportional hazard regression analyses were evaluated for association of all independent parameters with disease prognosis. Results Multivariable Cox proportional hazards models showed that the level of NLR (HR = 1.031, 95%CI: 1.002-1.060, P = 0.033), number of nodules (HR = 1.679, 95%CI: 1.285-2.194, P<0.001), portal vein thrombosis (HR = 4.329, 95%CI: 1.968-9.521, P<0.001), microvascular invasion (HR = 2.527, 95%CI: 1.726-3.700, P<0.001) and CTP score (HR = 1.675, 95%CI: 1.153-2.433, P = 0.007) were significant predictors of mortality. From the Kaplan-Meier analysis of overall survival (OS), each NLR quartile showed a progressively worse OS and apparent separation (log-rank P=0.008). The highest 5-year OS rate following CLR (60%) in HCC patients was observed in quartile 1. In contrast, the lowest 5-year OS rate (27%) was obtained in quartile 4. Conclusions Stratified NLR may predict significantly improved outcomes and strengthen the predictive power for patient responses to therapeutic intervention. PMID:26716411

  9. Are general practitioners able to accurately diagnose dementia and identify Alzheimer's disease? A comparison with an outpatient memory clinic.

    PubMed Central

    van Hout, H; Vernooij-Dassen, M; Poels, P; Hoefnagels, W; Grol, R

    2000-01-01

    Since the introduction of agents for the treatment of Alzheimer's disease, and in order to increase understanding of a patient's changed behaviour, it has become particularly important that dementia is both diagnosed at an early stage and differentiated into its subtypes. This study aims to ascertain whether GPs were able to diagnose dementia and identify the type of dementia accurately and confidently. GPs were well able to assess the firmness of their own dementia diagnoses, which supposes that they are able to make appropriate selection for referral. Diagnostic support from a specialised team can particularly contribute to identifying the type of dementia. PMID:10897518

  10. Inductive matrix completion for predicting gene–disease associations

    PubMed Central

    Natarajan, Nagarajan; Dhillon, Inderjit S.

    2014-01-01

    Motivation: Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies—for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies—for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene–disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Results: Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better—it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has <15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently

  11. GeneRanker: An Online System for Predicting Gene-Disease Associations for Translational Research

    PubMed Central

    Gonzalez, Graciela; Uribe, Juan C.; Armstrong, Brock; McDonough, Wendy; Berens, Michael E.

    2008-01-01

    With the overwhelming volume of genomic and molecular information available on many databases nowadays, researchers need from bioinformaticians more than encouragement to refine their searches. We present here GeneRanker, an online system that allows researchers to obtain a ranked list of genes potentially related to a specific disease or biological process by combining gene-disease (or genebiological process) associations with protein-protein interactions extracted from the literature, using computational analysis of the protein network topology to more accurately rank the predicted associations. GeneRanker was evaluated in the context of brain cancer research, and is freely available online at http://www.generanker.org. PMID:21347122

  12. Staging of osteonecrosis of the jaw requires computed tomography for accurate definition of the extent of bony disease.

    PubMed

    Bedogni, Alberto; Fedele, Stefano; Bedogni, Giorgio; Scoletta, Matteo; Favia, Gianfranco; Colella, Giuseppe; Agrillo, Alessandro; Bettini, Giordana; Di Fede, Olga; Oteri, Giacomo; Fusco, Vittorio; Gabriele, Mario; Ottolenghi, Livia; Valsecchi, Stefano; Porter, Stephen; Petruzzi, Massimo; Arduino, Paolo; D'Amato, Salvatore; Ungari, Claudio; Fung Polly, Pok-Lam; Saia, Giorgia; Campisi, Giuseppina

    2014-09-01

    Management of osteonecrosis of the jaw associated with antiresorptive agents is challenging, and outcomes are unpredictable. The severity of disease is the main guide to management, and can help to predict prognosis. Most available staging systems for osteonecrosis, including the widely-used American Association of Oral and Maxillofacial Surgeons (AAOMS) system, classify severity on the basis of clinical and radiographic findings. However, clinical inspection and radiography are limited in their ability to identify the extent of necrotic bone disease compared with computed tomography (CT). We have organised a large multicentre retrospective study (known as MISSION) to investigate the agreement between the AAOMS staging system and the extent of osteonecrosis of the jaw (focal compared with diffuse involvement of bone) as detected on CT. We studied 799 patients with detailed clinical phenotyping who had CT images taken. Features of diffuse bone disease were identified on CT within all AAOMS stages (20%, 8%, 48%, and 24% of patients in stages 0, 1, 2, and 3, respectively). Of the patients classified as stage 0, 110/192 (57%) had diffuse disease on CT, and about 1 in 3 with CT evidence of diffuse bone disease was misclassified by the AAOMS system as having stages 0 and 1 osteonecrosis. In addition, more than a third of patients with AAOMS stage 2 (142/405, 35%) had focal bone disease on CT. We conclude that the AAOMS staging system does not correctly identify the extent of bony disease in patients with osteonecrosis of the jaw. PMID:24856927

  13. A new hybrid intelligent system for accurate detection of Parkinson's disease.

    PubMed

    Hariharan, M; Polat, Kemal; Sindhu, R

    2014-03-01

    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset. PMID:24485390

  14. Genetic-based prediction of disease traits: prediction is very difficult, especially about the future†

    PubMed Central

    Schrodi, Steven J.; Mukherjee, Shubhabrata; Shan, Ying; Tromp, Gerard; Sninsky, John J.; Callear, Amy P.; Carter, Tonia C.; Ye, Zhan; Haines, Jonathan L.; Brilliant, Murray H.; Crane, Paul K.; Smelser, Diane T.; Elston, Robert C.; Weeks, Daniel E.

    2014-01-01

    Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications. PMID:24917882

  15. ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures

    PubMed Central

    Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey

    2016-01-01

    The advance of next-generation sequencing technologies has made exome sequencing rapid and relatively inexpensive. A major application of exome sequencing is the identification of genetic variations likely to cause Mendelian diseases. This requires processing large amounts of sequence information and therefore computational approaches that can accurately and efficiently identify the subset of disease-associated variations are needed. The accuracy and high false positive rates of existing computational tools leave much room for improvement. Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of protein sequence and structure features. On comparing our method, ENTPRISE, to the state-of-the-art methods SIFT, PolyPhen-2, MUTATIONASSESSOR, MUTATIONTASTER, FATHMM, ENTPRISE exhibits significant improvement. In particular, on a testing dataset consisting of only proteins with balanced disease-associated and neutral variations defined as having the ratio of neutral/disease-associated variations between 0.3 and 3, the Mathews Correlation Coefficient by ENTPRISE is 0.493 as compared to 0.432 by PPH2-HumVar, 0.406 by SIFT, 0.403 by MUTATIONASSESSOR, 0.402 by PPH2-HumDiv, 0.305 by MUTATIONTASTER, and 0.181 by FATHMM. ENTPRISE is then applied to nucleic acid binding proteins in the human proteome. Disease-associated predictions are shown to be highly correlated with the number of protein-protein interactions. Both these predictions and the ENTPRISE server are freely available for academic users as a web service at http://cssb.biology.gatech.edu/entprise/. PMID:26982818

  16. Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

    PubMed Central

    Ramadona, Aditya Lia; Lazuardi, Lutfan; Hii, Yien Ling; Holmner, Åsa; Kusnanto, Hari; Rocklöv, Joacim

    2016-01-01

    Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population. PMID:27031524

  17. A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

    PubMed Central

    Harris, Adam; Harries, Priscilla

    2016-01-01

    overall accuracy being reported. Data were extracted using a standardised tool, by one reviewer, which could have introduced bias. Devising search terms for prognostic studies is challenging. Every attempt was made to devise search terms that were sufficiently sensitive to detect all prognostic studies; however, it remains possible that some studies were not identified. Conclusion Studies of prognostic accuracy in palliative care are heterogeneous, but the evidence suggests that clinicians’ predictions are frequently inaccurate. No sub-group of clinicians was consistently shown to be more accurate than any other. Implications of Key Findings Further research is needed to understand how clinical predictions are formulated and how their accuracy can be improved. PMID:27560380

  18. Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand.

    PubMed

    Reich, Nicholas G; Lauer, Stephen A; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E; Salje, Henrik; Cummings, Derek A T; Lessler, Justin

    2016-06-01

    Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. PMID:27304062

  19. Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand

    PubMed Central

    Lauer, Stephen A.; Sakrejda, Krzysztof; Iamsirithaworn, Sopon; Hinjoy, Soawapak; Suangtho, Paphanij; Suthachana, Suthanun; Clapham, Hannah E.; Salje, Henrik; Cummings, Derek A. T.; Lessler, Justin

    2016-01-01

    Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. PMID:27304062

  20. Epidemic predictions in an imperfect world: modelling disease spread with partial data.

    PubMed

    Dawson, Peter M; Werkman, Marleen; Brooks-Pollock, Ellen; Tildesley, Michael J

    2015-06-01

    'Big-data' epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements. PMID:25948687

  1. High definition i-SCAN endoscopy with water immersion technique accurately reflects histological severity of celiac disease

    PubMed Central

    Iacucci, Marietta; Poon, Tiffany; Gui, X. Sean; Subrata, Ghosh

    2016-01-01

    Background and aims: Severe villous atrophy can be revealed with conventional white light endoscopy (WLE), however, milder grades or patchy villous atrophy are more difficult to detect. Novel endoscopic techniques such as high definition i-SCAN endoscopy with the water immersion technique (i-SCAN-HDWI) may provide the ability to visualize duodenal villi more accurately. We aimed to determine the performance of i-SCAN-HDWI in evaluating the severity of histological damage in the duodenum of patients with celiac disease. Patients and methods: A retrospective cohort study was performed in a single tertiary academic endoscopic center. We studied 58 patients (46 women; median age 36.5 years, range 18 – 72 years) with positive anti-TTG IgA antibody. The villous pattern of the second part of the duodenum was assessed by WLE and i-SCAN-HDWI. The endoscopic grades in both techniques were correlated using Marsh histologic grades by Spearman correlation coefficient. The diagnostic accuracy of i-SCAN-HDWI for detection of patchy or complete atrophy of the villi was evaluated. Results: A significant correlation was demonstrated between endoscopic grade using i-SCAN-HDWI and Marsh histologic grade (r = 0.732; P < 0.00001). The correlation between WLE grade and Marsh histologic grade was inferior to i-SCAN-HDWI (r = 0.31; P = 0.01). The sensitivity of i-SCAN-HDWI was 96 % (95 %CI: 85 – 99 %) and the specificity was 63 % (95 %CI: 26 – 90 %) in diagnosing abnormal biopsy consistent with celiac disease. Conclusion: i-SCAN-HDWI endoscopy can reflect the histological severity of celiac disease more accurately than conventional WLE alone. This novel endoscopic imaging can improve the diagnostic yield of duodenal biopsies in celiac patients, especially for those with a patchy distribution of villous damage. PMID:27227112

  2. Predicting Coronary Heart Disease and Stroke: The FINRISK Calculator.

    PubMed

    Vartiainen, Erkki; Laatikainen, Tiina; Peltonen, Markku; Puska, Pekka

    2016-06-01

    The FINRISK risk calculator predicts 10-year risk for coronary heart disease, stroke incidence, and their combination. The model is based on 10-year cohort follow-up from 3 different cohorts in 1982, 1987, and 1992 from a random population sample in 3 areas in Finland. Coronary heart disease, stroke, and their combination are predicted by smoking, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, diabetes, and family history. The Internet-based calculator is commonly used in Finland in health services to assess the need for hypertension and hypercholesterolemia treatment and is used also in patients' counseling. PMID:27242089

  3. Using Earth Observations to Understand and Predict Infectious Diseases

    NASA Technical Reports Server (NTRS)

    Soebiyanto, Radina P.; Kiang, Richard

    2015-01-01

    This presentation discusses the processes from data collection and processing to analysis involved in unraveling patterns between disease outbreaks and the surrounding environment and meteorological conditions. We used these patterns to estimate when and where disease outbreaks will occur. As a case study, we will present our work on assessing the relationship between meteorological conditions and influenza in Central America. Our work represents the discovery, prescriptive and predictive aspects of data analytics.

  4. Scoring multiple features to predict drug disease associations using information fusion and aggregation.

    PubMed

    Moghadam, H; Rahgozar, M; Gharaghani, S

    2016-08-01

    Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%. PMID:27455069

  5. Development of Regional Models that Use Meteorological Variables for Predicting Stripe Rust Disease on Winter Wheat.

    NASA Astrophysics Data System (ADS)

    Melugin Coakley, Stella; Boyd, William S.; Line, Roland F.

    1984-08-01

    Meteorological variables can be used to predict stripe rust, a disease of wheat caused by Puccinia striiformis West., at Lind, Pullman, and Walla Walla, Washington and Pendleton, Oregon in the Pacific Northwest of the United States. Regional models developed using different methodologies are described and evaluated for accuracy. Disease intensity data, collected from 1968 to 1981, were converted to a 0-9 disease index (DI) and were used as the dependent variable in regression analysis. Meteorological data were expressed as standardized negative degree days (NDDZ) accumulated during December and January, the Julian date of spring (JDS) [defined as the date when 40 or more positive degree days (PDD) accumulated during the subsequent 14 days] and PDD for the 80-day period after the JDS. In one of the regional models, NDDZ was accumulated for adjusted time periods at sites other than Pullman. Mallow's Cp criterion was used to evaluate the regression equations with different numbers of independent variables. The most accurate model uses NDDZ and JDS as the independent variables. The models were cross-validated by randomly removing 2 years' data and reformulating the model based on the remaining data; the new model was then used to compare actual and predicted DI. Predicted DI was within one standard error of the actual DI 60% of the time. Incorrect predictions occurred during years when spring was unusually favorable or unfavorable for disease development. The methodology described is applicable to developing statistical models relating other pest occurrences to meteorological conditions.

  6. Markers predicting progression of human immunodeficiency virus-related disease.

    PubMed Central

    Tsoukas, C M; Bernard, N F

    1994-01-01

    Human immunodeficiency virus (HIV) interacts with the immune system throughout the course of infection. For most of the disease process, HIV activates the immune system, and the degree of activation can be assessed by measuring serum levels of molecules such as beta 2-microglobulin and neopterin, as well as other serum and cell surface phenotype markers. The levels of some of these markers correlate with clinical progression of HIV disease, and these markers may be useful as surrogate markers for development of clinical AIDS. Because the likelihood and timing of development of clinical AIDS following seroconversion, for any particular individual, are not readily predictable, the use of nonclinical disease markers has become critically important to patient management. Surrogate markers of HIV infection are, by definition, measurable traits that correlate with disease progression. An ideal marker should identify patients at highest risk of disease progression, provide information on how long an individual has been infected, help in staging HIV disease, predict development of opportunistic infections associated with AIDS, monitor the therapeutic efficacy of immunomodulating or antiviral treatments, and the easily quantifiable, reliable, clinically available, and affordable. This review examines the current state of knowledge and the role of surrogate markers in the natural history and treatment of HIV infection. The clinical usefulness of each marker is assessed with respect to the criteria outlined for the ideal surrogate marker for HIV disease progression. PMID:8118788

  7. The use and role of predictive systems in disease management

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Disease predictive systems are intended to be management aids. With a few exceptions, these systems typically do not have sustained use directly by growers. Rather, their impact is mostly pedagogic and indirect, improving recommendations from farm advisers and shaping management concepts. The degree...

  8. Predicting Clinical Scores from Magnetic Resonance Scans in Alzheimer's Disease

    PubMed Central

    Stonnington, Cynthia M.; Chu, Carlton; Klöppel, Stefan; Jack, Clifford R; Ashburner, John; Frackowiak, Richard S.J.

    2010-01-01

    Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent datasets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3 months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other dataset; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale—Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P<.0001). Training with one dataset and testing with another demonstrated stability between datasets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome. PMID:20347044

  9. How to make predictions about future infectious disease risks

    PubMed Central

    Woolhouse, Mark

    2011-01-01

    Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

  10. Alternative dietary indices both strongly predict risk of chronic disease.

    PubMed

    Chiuve, Stephanie E; Fung, Teresa T; Rimm, Eric B; Hu, Frank B; McCullough, Marjorie L; Wang, Molin; Stampfer, Meir J; Willett, Walter C

    2012-06-01

    The Healthy Eating Index-2005 (HEI-2005) measures adherence to the 2005 Dietary Guidelines for Americans, but the association between the HEI-2005 and risk of chronic disease is not known. The Alternative Healthy Eating Index (AHEI), which is based on foods and nutrients predictive of chronic disease risk, was associated inversely with chronic disease risk previously. We updated the AHEI, including additional dietary factors involved in the development of chronic disease, and assessed the associations between the AHEI-2010 and the HEI-2005 and risk of major chronic disease prospectively among 71,495 women from the Nurses' Health Study and 41,029 men from the Health Professionals Follow-Up Study who were free of chronic disease at baseline. During ≥24 y of follow-up, we documented 26,759 and 15,558 incident chronic diseases (cardiovascular disease, diabetes, cancer, or nontrauma death) among women and men, respectively. The RR (95% CI) of chronic disease comparing the highest with the lowest quintile was 0.84 (0.81, 0.87) for the HEI-2005 and 0.81 (0.77, 0.85) for the AHEI-2010. The AHEI-2010 and HEI-2005 were most strongly associated with coronary heart disease (CHD) and diabetes, and for both outcomes the AHEI-2010 was more strongly associated with risk than the HEI-2005 (P-difference = 0.002 and <0.001, respectively). The 2 indices were similarly associated with risk of stroke and cancer. These findings suggest that closer adherence to the 2005 Dietary Guidelines may lower risk of major chronic disease. However, the AHEI-2010, which included additional dietary information, was more strongly associated with chronic disease risk, particularly CHD and diabetes. PMID:22513989

  11. How accurate and precise are limited sampling strategies in estimating exposure to mycophenolic acid in people with autoimmune disease?

    PubMed

    Abd Rahman, Azrin N; Tett, Susan E; Staatz, Christine E

    2014-03-01

    Mycophenolic acid (MPA) is a potent immunosuppressant agent, which is increasingly being used in the treatment of patients with various autoimmune diseases. Dosing to achieve a specific target MPA area under the concentration-time curve from 0 to 12 h post-dose (AUC12) is likely to lead to better treatment outcomes in patients with autoimmune disease than a standard fixed-dose strategy. This review summarizes the available published data around concentration monitoring strategies for MPA in patients with autoimmune disease and examines the accuracy and precision of methods reported to date using limited concentration-time points to estimate MPA AUC12. A total of 13 studies were identified that assessed the correlation between single time points and MPA AUC12 and/or examined the predictive performance of limited sampling strategies in estimating MPA AUC12. The majority of studies investigated mycophenolate mofetil (MMF) rather than the enteric-coated mycophenolate sodium (EC-MPS) formulation of MPA. Correlations between MPA trough concentrations and MPA AUC12 estimated by full concentration-time profiling ranged from 0.13 to 0.94 across ten studies, with the highest associations (r (2) = 0.90-0.94) observed in lupus nephritis patients. Correlations were generally higher in autoimmune disease patients compared with renal allograft recipients and higher after MMF compared with EC-MPS intake. Four studies investigated use of a limited sampling strategy to predict MPA AUC12 determined by full concentration-time profiling. Three studies used a limited sampling strategy consisting of a maximum combination of three sampling time points with the latest sample drawn 3-6 h after MMF intake, whereas the remaining study tested all combinations of sampling times. MPA AUC12 was best predicted when three samples were taken at pre-dose and at 1 and 3 h post-dose with a mean bias and imprecision of 0.8 and 22.6 % for multiple linear regression analysis and of -5.5 and 23.0 % for

  12. Kidney Disease and the Nexus of Chronic Kidney Disease and Acute Kidney Injury: The Role of Novel Biomarkers as Early and Accurate Diagnostics.

    PubMed

    Yerramilli, Murthy; Farace, Giosi; Quinn, John; Yerramilli, Maha

    2016-11-01

    Chronic kidney disease (CKD) and acute kidney injury (AKI) are interconnected and the presence of one is a risk for the other. CKD is an important predictor of AKI after exposure to nephrotoxic drugs or major surgery, whereas persistent or repetitive injury could result in the progression of CKD. This brings new perspectives to the diagnosis and monitoring of kidney diseases highlighting the need for a panel of kidney-specific biomarkers that reflect functional as well as structural damage and recovery, predict potential risk and provide prognosis. This article discusses the kidney-specific biomarkers, symmetric dimethylarginine (SDMA), clusterin, cystatin B, and inosine. PMID:27485279

  13. Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

    PubMed Central

    Pollastri, Gianluca; Martin, Alberto JM; Mooney, Catherine; Vullo, Alessandro

    2007-01-01

    Background Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio. Results Here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of PDB templates. We compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. We show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. For sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. Gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available. Conclusion The predictive system are publicly available at the address . PMID:17570843

  14. 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. PMID:24675610

  15. COMBINING HETEROGENOUS DATA FOR PREDICTION OF DISEASE RELATED AND PHARMACOGENES

    PubMed Central

    HUNTER, LAWRENCE E.; COHEN, K. BRETONNEL

    2014-01-01

    Identifying genetic variants that affect drug response or play a role in disease is an important task for clinicians and researchers. Before individual variants can be explored efficiently for effect on drug response or disease relationships, specific candidate genes must be identified. While many methods rank candidate genes through the use of sequence features and network topology, only a few exploit the information contained in the biomedical literature. In this work, we train and test a classifier on known pharmacogenes from PharmGKB and present a classifier that predicts pharmacogenes on a genome-wide scale using only Gene Ontology annotations and simple features mined from the biomedical literature. Performance of F=0.86, AUC=0.860 is achieved. The top 10 predicted genes are analyzed. Additionally, a set of enriched pharmacogenic Gene Ontology concepts is produced. PMID:24297559

  16. Computational prediction of disease microRNAs in domestic animals

    PubMed Central

    2014-01-01

    Background The most important means of identifying diseases before symptoms appear is through the discovery of disease-associated biomarkers. Recently, microRNAs (miRNAs) have become highly useful biomarkers of infectious, genetic and metabolic diseases in human but they have not been well studied in domestic animals. It is probable that many of the animal homologs of human disease-associated miRNAs may be involved in domestic animal diseases. Here we describe a computational biology study in which human disease miRNAs were utilized to predict orthologous miRNAs in cow, chicken, pig, horse, and dog. Results We identified 287 human disease-associated miRNAs which had at least one 100% identical animal homolog. The 287 miRNAs were associated with 359 human diseases referenced in 2,863 Pubmed articles. Multiple sequence analysis indicated that over 60% of known horse mature miRNAs found perfect matches in human disease-associated miRNAs, followed by dog (50%). As expected, chicken had the least number of perfect matches (5%). Phylogenetic analysis of miRNA precursors indicated that 85% of human disease pre-miRNAs were highly conserved in animals, showing less than 5% nucleotide substitution rates over evolutionary time. As an example we demonstrated conservation of human hsa-miR-143-3p which is associated with type 2 diabetes and targets AKT1 gene which is highly conserved in pig, horse and dog. Functional analysis of AKT1 gene using Gene Ontology (GO) showed that it is involved in glucose homeostasis, positive regulation of glucose import, positive regulation of glycogen biosynthetic process, glucose transport and response to food. Conclusions This data provides the animal and veterinary research community with a resource to assist in generating hypothesis-driven research for discovering animal disease-related miRNA from their datasets and expedite development of prophylactic and disease-treatment strategies and also influence research efforts to identify novel

  17. LncDisease: a sequence based bioinformatics tool for predicting lncRNA-disease associations

    PubMed Central

    Wang, Junyi; Ma, Ruixia; Ma, Wei; Chen, Ji; Yang, Jichun; Xi, Yaguang; Cui, Qinghua

    2016-01-01

    LncRNAs represent a large class of noncoding RNA molecules that have important functions and play key roles in a variety of human diseases. There is an urgent need to develop bioinformatics tools as to gain insight into lncRNAs. This study developed a sequence-based bioinformatics method, LncDisease, to predict the lncRNA-disease associations based on the crosstalk between lncRNAs and miRNAs. Using LncDisease, we predicted the lncRNAs associated with breast cancer and hypertension. The breast-cancer-associated lncRNAs were studied in two breast tumor cell lines, MCF-7 and MDA-MB-231. The qRT-PCR results showed that 11 (91.7%) of the 12 predicted lncRNAs could be validated in both breast cancer cell lines. The hypertension-associated lncRNAs were further evaluated in human vascular smooth muscle cells (VSMCs) stimulated with angiotensin II (Ang II). The qRT-PCR results showed that 3 (75.0%) of the 4 predicted lncRNAs could be validated in Ang II-treated human VSMCs. In addition, we predicted 6 diseases associated with the lncRNA GAS5 and validated 4 (66.7%) of them by literature mining. These results greatly support the specificity and efficacy of LncDisease in the study of lncRNAs in human diseases. The LncDisease software is freely available on the Software Page: http://www.cuilab.cn/. PMID:26887819

  18. LncDisease: a sequence based bioinformatics tool for predicting lncRNA-disease associations.

    PubMed

    Wang, Junyi; Ma, Ruixia; Ma, Wei; Chen, Ji; Yang, Jichun; Xi, Yaguang; Cui, Qinghua

    2016-05-19

    LncRNAs represent a large class of noncoding RNA molecules that have important functions and play key roles in a variety of human diseases. There is an urgent need to develop bioinformatics tools as to gain insight into lncRNAs. This study developed a sequence-based bioinformatics method, LncDisease, to predict the lncRNA-disease associations based on the crosstalk between lncRNAs and miRNAs. Using LncDisease, we predicted the lncRNAs associated with breast cancer and hypertension. The breast-cancer-associated lncRNAs were studied in two breast tumor cell lines, MCF-7 and MDA-MB-231. The qRT-PCR results showed that 11 (91.7%) of the 12 predicted lncRNAs could be validated in both breast cancer cell lines. The hypertension-associated lncRNAs were further evaluated in human vascular smooth muscle cells (VSMCs) stimulated with angiotensin II (Ang II). The qRT-PCR results showed that 3 (75.0%) of the 4 predicted lncRNAs could be validated in Ang II-treated human VSMCs. In addition, we predicted 6 diseases associated with the lncRNA GAS5 and validated 4 (66.7%) of them by literature mining. These results greatly support the specificity and efficacy of LncDisease in the study of lncRNAs in human diseases. The LncDisease software is freely available on the Software Page: http://www.cuilab.cn/. PMID:26887819

  19. Entorhinal cortex thickness predicts cognitive decline in Alzheimer's disease.

    PubMed

    Velayudhan, Latha; Proitsi, Petroula; Westman, Eric; Muehlboeck, J-Sebastian; Mecocci, Patrizia; Vellas, Bruno; Tsolaki, Magda; Kłoszewska, Iwona; Soininen, Hilkka; Spenger, Christian; Hodges, Angela; Powell, John; Lovestone, Simon; Simmons, Andrew

    2013-01-01

    Biomarkers for Alzheimer's disease (AD) based on non-invasive methods are highly desirable for diagnosis, disease progression, and monitoring therapeutics. We aimed to study the use of hippocampal volume, entorhinal cortex (ERC) thickness, and whole brain volume (WBV) as predictors of cognitive change in patients with AD. 120 AD subjects, 106 mild cognitive impairment (MCI), and 99 non demented controls (NDC) from the multi-center pan-European AddNeuroMed study underwent MRI scanning at baseline and clinical evaluations at quarterly follow-up up to 1 year. The rate of cognitive decline was estimated using cognitive outcomes, Mini-Mental State Examination (MMSE) and Alzheimer disease assessment scale-cognitive (ADAS-cog) by fitting a random intercept and slope model. AD subjects had smaller ERC thickness and hippocampal and WBV volumes compared to MCI and NDC subjects. Within the AD group, ERC > WBV was significantly associated with baseline cognition (MMSE, ADAS-cog) and disease severity (Clinical Dementia Rating). Baseline ERC thickness was associated with both longitudinal MMSE and ADAS-cog score changes and WBV with ADAS-cog decline. These data indicate that AD subjects with thinner ERC had lower baseline cognitive scores, higher disease severity, and predicted greater subsequent cognitive decline at one year follow up. ERC is a region known to be affected early in the disease. Therefore, the rate of atrophy in this structure is expected to be higher since neurodegeneration begins earlier. Focusing on structural analyses that predict decline can identify those individuals at greatest risk for future cognitive loss. This may have potential for increasing the efficacy of early intervention. PMID:23047370

  20. Conformations of 1,2-dimethoxypropane and 5-methoxy-1,3-dioxane: are ab initio quantum chemistry predictions accurate?

    NASA Astrophysics Data System (ADS)

    Smith, Grant D.; Jaffe, Richard L.; Yoon, Do. Y.

    1998-06-01

    High-level ab initio quantum chemistry calculations are shown to predict conformer populations of 1,2-dimethoxypropane and 5-methoxy-1,3-dioxane that are consistent with gas-phase NMR vicinal coupling constant measurements. The conformational energies of the cyclic ether 5-methoxy-1,3-dioxane are found to be consistent with those predicted by a rotational isomeric state (RIS) model based upon the acyclic analog 1,2-dimethoxypropane. The quantum chemistry and RIS calculations indicate the presence of strong attractive 1,5 C(H 3)⋯O electrostatic interactions in these molecules, similar to those found in 1,2-dimethoxyethane.

  1. Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs).

    PubMed

    Cheng, Feixiong; Li, Weihua; Zhou, Yadi; Li, Jie; Shen, Jie; Lee, Philip W; Tang, Yun

    2013-06-01

    New technologies for systems-level determinants of human exposure to drugs, industrial chemicals, pesticides, and other environmental agents provide an invaluable opportunity to extend the understanding of human health and potential environmental hazards. We report here the development of a new computational-systems toxicology framework, called predictive toxicogenomics-derived models (PTDMs). PTDMs integrate three networks of chemical-gene interactions (CGIs), chemical-disease associations (CDAs) and gene-disease associations (GDAs) to infer chemical hazard profiles, identify exposure data gaps and to incorporate genes and disease networks into chemical safety evaluations. Three comprehensive networks addressing CGI, CDA and GDA extracted from the comparative toxicogenomics database (CTD) were constructed. The areas under the receiver operating characteristics curve ranged from 0.85 to 0.97 and were yielded using our methodology using a 10-fold cross validation by a simulation carried out 100 times. As the illustrated examples show, we predicted new potential target genes and diseases for bisphenol A and aspirin. The molecular hypothesis and experimental evidence from published literature for these predictions were provided. The results demonstrated that our method has potential applications for chemical profiling in human health exposure and environmental hazard assessment. PMID:23455869

  2. Survival outcomes scores (SOFT, BAR, and Pedi-SOFT) are accurate in predicting post-liver transplant survival in adolescents.

    PubMed

    Conjeevaram Selvakumar, Praveen Kumar; Maksimak, Brian; Hanouneh, Ibrahim; Youssef, Dalia H; Lopez, Rocio; Alkhouri, Naim

    2016-09-01

    SOFT and BAR scores utilize recipient, donor, and graft factors to predict the 3-month survival after LT in adults (≥18 years). Recently, Pedi-SOFT score was developed to predict 3-month survival after LT in young children (≤12 years). These scoring systems have not been studied in adolescent patients (13-17 years). We evaluated the accuracy of these scoring systems in predicting the 3-month post-LT survival in adolescents through a retrospective analysis of data from UNOS of patients aged 13-17 years who received LT between 03/01/2002 and 12/31/2012. Recipients of combined organ transplants, donation after cardiac death, or living donor graft were excluded. A total of 711 adolescent LT recipients were included with a mean age of 15.2±1.4 years. A total of 100 patients died post-LT including 33 within 3 months. SOFT, BAR, and Pedi-SOFT scores were all found to be good predictors of 3-month post-transplant survival outcome with areas under the ROC curve of 0.81, 0.80, and 0.81, respectively. All three scores provided good accuracy for predicting 3-month survival post-LT in adolescents and may help clinical decision making to optimize survival rate and organ utilization. PMID:27478012

  3. RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions.

    PubMed

    Issa, Naiem T; Peters, Oakland J; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2015-01-01

    We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing. PMID:26234515

  4. A Maximal Graded Exercise Test to Accurately Predict VO2max in 18-65-Year-Old Adults

    ERIC Educational Resources Information Center

    George, James D.; Bradshaw, Danielle I.; Hyde, Annette; Vehrs, Pat R.; Hager, Ronald L.; Yanowitz, Frank G.

    2007-01-01

    The purpose of this study was to develop an age-generalized regression model to predict maximal oxygen uptake (VO sub 2 max) based on a maximal treadmill graded exercise test (GXT; George, 1996). Participants (N = 100), ages 18-65 years, reached a maximal level of exertion (mean plus or minus standard deviation [SD]; maximal heart rate [HR sub…

  5. Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease.

    PubMed

    Nazeri, Arash; Ganjgahi, Habib; Roostaei, Tina; Nichols, Thomas; Zarei, Mojtaba

    2014-11-15

    Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker. PMID:25173418

  6. Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models

    NASA Astrophysics Data System (ADS)

    Pau, George Shu Heng; Shen, Chaopeng; Riley, William J.; Liu, Yaning

    2016-02-01

    The topography, and the biotic and abiotic parameters are typically upscaled to make watershed-scale hydrologic-biogeochemical models computationally tractable. However, upscaling procedure can produce biases when nonlinear interactions between different processes are not fully captured at coarse resolutions. Here we applied the Proper Orthogonal Decomposition Mapping Method (PODMM) to downscale the field solutions from a coarse (7 km) resolution grid to a fine (220 m) resolution grid. PODMM trains a reduced-order model (ROM) with coarse-resolution and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3-D watershed processes model that has been validated for many temperate watersheds. Subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM. The approximation errors were efficiently quantified using an error estimator. By jointly estimating correlated variables and temporally varying the ROM parameters, we further reduced the approximation errors by up to 20%. We also improved the method's robustness by constructing multiple ROMs using different set of variables, and selecting the best approximation based on the error estimator. The ROMs produced accurate downscaling of soil moisture, latent heat flux, and net primary production with O(1000) reduction in computational cost. The subgrid distributions were also nearly indistinguishable from the ones obtained using the fine-resolution model. Compared to coarse-resolution solutions, biases in upscaled ROM solutions were reduced by up to 80%. This method has the potential to help address the long-standing spatial scaling problem in hydrology and enable long-time integration, parameter estimation, and stochastic uncertainty analysis while accurately representing the heterogeneities.

  7. How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements.

    PubMed

    Grassi, Lorenzo; Väänänen, Sami P; Ristinmaa, Matti; Jurvelin, Jukka S; Isaksson, Hanna

    2016-03-21

    Subject-specific finite element models have been proposed as a tool to improve fracture risk assessment in individuals. A thorough laboratory validation against experimental data is required before introducing such models in clinical practice. Results from digital image correlation can provide full-field strain distribution over the specimen surface during in vitro test, instead of at a few pre-defined locations as with strain gauges. The aim of this study was to validate finite element models of human femora against experimental data from three cadaver femora, both in terms of femoral strength and of the full-field strain distribution collected with digital image correlation. The results showed a high accuracy between predicted and measured principal strains (R(2)=0.93, RMSE=10%, 1600 validated data points per specimen). Femoral strength was predicted using a rate dependent material model with specific strain limit values for yield and failure. This provided an accurate prediction (<2% error) for two out of three specimens. In the third specimen, an accidental change in the boundary conditions occurred during the experiment, which compromised the femoral strength validation. The achieved strain accuracy was comparable to that obtained in state-of-the-art studies which validated their prediction accuracy against 10-16 strain gauge measurements. Fracture force was accurately predicted, with the predicted failure location being very close to the experimental fracture rim. Despite the low sample size and the single loading condition tested, the present combined numerical-experimental method showed that finite element models can predict femoral strength by providing a thorough description of the local bone mechanical response. PMID:26944687

  8. 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

  9. Prediction of occult neck disease in laryngeal cancer by means of a logistic regression statistical model.

    PubMed

    Ghouri, A F; Zamora, R L; Sessions, D G; Spitznagel, E L; Harvey, J E

    1994-10-01

    The ability to accurately predict the presence of subclinical metastatic neck disease in clinically N0 patients with primary epidermoid cancer of the larynx would be of great value in determining whether to perform an elective neck dissection. We describe a statistical approach to estimating the probability of occult neck disease given pretreatment clinical parameters. A retrospective study was performed involving 736 clinically N0 patients with primary laryngeal cancer who were treated surgically with primary resection and ipsilateral neck dissection. Nodal involvement was determined histologically after surgical lymphadenectomy. A logistic regression model was used to derive an equation that calculated the probability of occult neck metastasis based on pretreatment T stage, tumor location, and histologic grade. The model has a sensitivity of 74%, a specificity of 87%, and can be entered into a programmable calculator. PMID:7934602

  10. Can the Gibbs free energy of adsorption be predicted efficiently and accurately: an M05-2X DFT study.

    PubMed

    Michalkova, A; Gorb, L; Hill, F; Leszczynski, J

    2011-03-24

    This study presents new insight into the prediction of partitioning of organic compounds between a carbon surface (soot) and water, and it also sheds light on the sluggish desorption of interacting molecules from activated and nonactivated carbon surfaces. This paper provides details about the structure and interactions of benzene, polycyclic aromatic hydrocarbons, and aromatic nitrocompounds with a carbon surface modeled by coronene using a density functional theory approach along with the M05-2X functional. The adsorption was studied in vacuum and from water solution. The molecules studied are physisorbed on the carbon surface. While the intermolecular interactions of benzene and hydrocarbons are governed by dispersion forces, nitrocompounds are adsorbed also due to quite strong electrostatic interactions with all types of carbon surfaces. On the basis of these results, we conclude that the method of prediction presented in this study allows one to approach the experimental level of accuracy in predicting thermodynamic parameters of adsorption on a carbon surface from the gas phase. The empirical modification of the polarized continuum model leads also to a quantitative agreement with the experimental data for the Gibbs free energy values of the adsorption from water solution. PMID:21361266

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

    PubMed

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

    2015-12-01

    Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets. PMID:26460680

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  14. Comparative study of exchange-correlation functionals for accurate predictions of structural and magnetic properties of multiferroic oxides

    NASA Astrophysics Data System (ADS)

    Chen, Hanghui; Millis, Andrew J.

    2016-05-01

    We systematically compare predictions of various exchange correlation functionals for the structural and magnetic properties of perovskite Sr1 -xBaxMnO3 (0 ≤x ≤1 )—a representative class of multiferroic oxides. The local spin density approximation (LSDA) and spin-dependent generalized gradient approximation with Perdew-Burke-Ernzerhof parametrization (sPBE) make substantial different predictions for ferroelectric atomic distortions, tetragonality, and ground state magnetic ordering. Neither approximation quantitatively reproduces all the measured structural and magnetic properties of perovskite Sr0.5Ba0.5MnO3 . The spin-dependent generalized gradient approximation with Perdew-Burke-Ernzerhof revised for solids parametrization (sPBEsol) and the charge-only Perdew-Burke-Ernzerhof parametrized generalized gradient approximation with Hubbard U and Hund's J extensions both provide overall better agreement with measured structural and magnetic properties of Sr0.5Ba0.5MnO3 , compared to LSDA and sPBE. Using these two methods, we find that different from previous predictions, perovskite BaMnO3 has large Mn off-center displacements and is close to a ferromagnetic-to-antiferromagnetic phase boundary, making it a promising candidate to induce effective giant magnetoelectric effects and to achieve cross-field control of polarization and magnetism.

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

    PubMed

    Kong, Liang; Zhang, Lichao; Lv, Jinfeng

    2014-03-01

    Extracting good representation from protein sequence is fundamental for protein structural classes prediction tasks. In this paper, we propose a novel and powerful method to predict protein structural classes based on the predicted secondary structure information. At the feature extraction stage, a 13-dimensional feature vector is extracted to characterize general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Specially, four segment-level features are designed to elevate discriminative ability for proteins from the α/β and α+β classes. After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction. We report extensive experiments comparing the proposed method to the state-of-the-art in protein structural classes prediction on three widely used low-similarity benchmark datasets: FC699, 1189 and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies which have exceeded the best reported results on all of the three datasets. PMID:24316044

  16. Combined analysis of sMRI and fMRI imaging data provides accurate disease markers for hearing impairment.

    PubMed

    Tan, Lirong; Chen, Ye; Maloney, Thomas C; Caré, Marguerite M; Holland, Scott K; Lu, Long J

    2013-01-01

    In this research, we developed a robust two-layer classifier that can accurately classify normal hearing (NH) from hearing impaired (HI) infants with congenital sensori-neural hearing loss (SNHL) based on their Magnetic Resonance (MR) images. Unlike traditional methods that examine the intensity of each single voxel, we extracted high-level features to characterize the structural MR images (sMRI) and functional MR images (fMRI). The Scale Invariant Feature Transform (SIFT) algorithm was employed to detect and describe the local features in sMRI. For fMRI, we constructed contrast maps and detected the most activated/de-activated regions in each individual. Based on those salient regions occurring across individuals, the bag-of-words strategy was introduced to vectorize the contrast maps. We then used a two-layer model to integrate these two types of features together. With the leave-one-out cross-validation approach, this integrated model achieved an AUC score of 0.90. Additionally, our algorithm highlighted several important brain regions that differentiated between NH and HI children. Some of these regions, e.g. planum temporale and angular gyrus, were well known auditory and visual language association regions. Others, e.g. the anterior cingulate cortex (ACC), were not necessarily expected to play a role in differentiating HI from NH children and provided a new understanding of brain function and of the disorder itself. These important brain regions provided clues about neuroimaging markers that may be relevant to the future use of functional neuroimaging to guide predictions about speech and language outcomes in HI infants who receive a cochlear implant. This type of prognostic information could be extremely useful and is currently not available to clinicians by any other means. PMID:24363991

  17. Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

    PubMed Central

    Angelantonio, Emanuele Di; Gao, Pei; Khan, Hassan; Butterworth, Adam S.; Wormser, David; Kaptoge, Stephen; Kondapally Seshasai, Sreenivasa Rao; Thompson, Alex; Sarwar, Nadeem; Willeit, Peter; Ridker, Paul M; Barr, Elizabeth L.M.; Khaw, Kay-Tee; Psaty, Bruce M.; Brenner, Hermann; Balkau, Beverley; Dekker, Jacqueline M.; Lawlor, Debbie A.; Daimon, Makoto; Willeit, Johann; Njølstad, Inger; Nissinen, Aulikki; Brunner, Eric J.; Kuller, Lewis H.; Price, Jackie F.; Sundström, Johan; Knuiman, Matthew W.; Feskens, Edith J. M.; Verschuren, W. M. M.; Wald, Nicholas; Bakker, Stephan J. L.; Whincup, Peter H.; Ford, Ian; Goldbourt, Uri; Gómez-de-la-Cámara, Agustín; Gallacher, John; Simons, Leon A.; Rosengren, Annika; Sutherland, Susan E.; Björkelund, Cecilia; Blazer, Dan G.; Wassertheil-Smoller, Sylvia; Onat, Altan; Marín Ibañez, Alejandro; Casiglia, Edoardo; Jukema, J. Wouter; Simpson, Lara M.; Giampaoli, Simona; Nordestgaard, Børge G.; Selmer, Randi; Wennberg, Patrik; Kauhanen, Jussi; Salonen, Jukka T.; Dankner, Rachel; Barrett-Connor, Elizabeth; Kavousi, Maryam; Gudnason, Vilmundur; Evans, Denis; Wallace, Robert B.; Cushman, Mary; D’Agostino, Ralph B.; Umans, Jason G.; Kiyohara, Yutaka; Nakagawa, Hidaeki; Sato, Shinichi; Gillum, Richard F.; Folsom, Aaron R.; van der Schouw, Yvonne T.; Moons, Karel G.; Griffin, Simon J.; Sattar, Naveed; Wareham, Nicholas J.; Selvin, Elizabeth; Thompson, Simon G.; Danesh, John

    2015-01-01

    IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS Analysis of individual-participant data available from 73 prospective studies involving 294 998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5%to <7.5%), and high (≥7.5%) risk. RESULTS During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20 840 incident fatal and nonfatal CVD outcomes (13 237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (−0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for

  18. Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer.

    PubMed

    Xu, Haoming; Moni, Mohammad Ali; Liò, Pietro

    2015-12-01

    In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease-gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git. PMID:26611766

  19. Neurodegenerative diseases: quantitative predictions of protein-RNA interactions.

    PubMed

    Cirillo, Davide; Agostini, Federico; Klus, Petr; Marchese, Domenica; Rodriguez, Silvia; Bolognesi, Benedetta; Tartaglia, Gian Gaetano

    2013-02-01

    Increasing evidence indicates that RNA plays an active role in a number of neurodegenerative diseases. We recently introduced a theoretical framework, catRAPID, to predict the binding ability of protein and RNA molecules. Here, we use catRAPID to investigate ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer's, and Parkinson's diseases. We specifically focus on (1) RNA interactions with fragile X mental retardation protein FMRP; (2) protein sequestration caused by CGG repeats; (3) noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; (4) autogenous regulation of TDP-43 and FMRP; (5) iron-mediated expression of amyloid precursor protein APP and α-synuclein; (6) interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and misfunction. PMID:23264567

  20. Neurodegenerative diseases: Quantitative predictions of protein–RNA interactions

    PubMed Central

    Cirillo, Davide; Agostini, Federico; Klus, Petr; Marchese, Domenica; Rodriguez, Silvia; Bolognesi, Benedetta; Tartaglia, Gian Gaetano

    2013-01-01

    Increasing evidence indicates that RNA plays an active role in a number of neurodegenerative diseases. We recently introduced a theoretical framework, catRAPID, to predict the binding ability of protein and RNA molecules. Here, we use catRAPID to investigate ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer’s, and Parkinson’s diseases. We specifically focus on (1) RNA interactions with fragile X mental retardation protein FMRP; (2) protein sequestration caused by CGG repeats; (3) noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; (4) autogenous regulation of TDP-43 and FMRP; (5) iron-mediated expression of amyloid precursor protein APP and α-synuclein; (6) interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and misfunction. PMID:23264567

  1. How to predict clinical relapse in inflammatory bowel disease patients

    PubMed Central

    Liverani, Elisa; Scaioli, Eleonora; Digby, Richard John; Bellanova, Matteo; Belluzzi, Andrea

    2016-01-01

    Inflammatory bowel diseases have a natural course characterized by alternating periods of remission and relapse. Disease flares occur in a random way and are currently unpredictable for the most part. Predictors of benign or unfavourable clinical course are required to facilitate treatment decisions and to avoid overtreatment. The present article provides a literature review of the current evidence on the main clinical, genetic, endoscopic, histologic, serologic and fecal markers to predict aggressiveness of inflammatory bowel disease and discuss their prognostic role, both in Crohn’s disease and ulcerative colitis. No single marker seems to be reliable alone as a flare predictor, even in light of promising evidence regarding the role of fecal markers, in particular fecal calprotectin, which has reported good results recently. In order to improve our daily clinical practice, validated prognostic scores should be elaborated, integrating clinical and biological markers of prognosis. Finally, we propose an algorithm considering clinical history and biological markers to intercept patients with high risk of clinical relapse. PMID:26811644

  2. aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events.

    PubMed

    Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B; Ben-Hur, Asa; Boucher, Christina

    2016-01-01

    Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The [Formula: see text] class contains tandem [Formula: see text]-type motif sequences, and the [Formula: see text] class contains alternating [Formula: see text], [Formula: see text] and [Formula: see text] type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a [Formula: see text]-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the [Formula: see text] class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for [Formula: see text]-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

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

    NASA Astrophysics Data System (ADS)

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

    1994-01-01

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

  4. aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events

    PubMed Central

    Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B.; Ben-Hur, Asa; Boucher, Christina

    2016-01-01

    Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The P class contains tandem P-type motif sequences, and the PLS class contains alternating P, L and S type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a PLS-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the PLS class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for PLS-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

  5. Adolescents' Sexually Transmitted Disease Protective Attitudes Predict Sexually Transmitted Disease Acquisition in Early Adulthood

    ERIC Educational Resources Information Center

    Crosby, Richard A.; Danner, Fred

    2008-01-01

    Background: Estimates suggest that about 48% of nearly 19 million cases of sexually transmitted diseases (STDs) occurring annually in the United States are acquired by persons aged 15-24 years. The purpose of this study was to test the hypothesis that adolescents' attitudes about protecting themselves from STDs predict their laboratory-confirmed…

  6. Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach

    PubMed Central

    Ko, Kyung Dae; El-Ghazawi, Tarek; Kim, Dongkyu; Morizono, Hiroki

    2014-01-01

    Motor neuron diseases (MNDs) are a class of progressive neurological diseases that damage the motor neurons. An accurate diagnosis is important for the treatment of patients with MNDs because there is no standard cure for the MNDs. However, the rates of false positive and false negative diagnoses are still very high in this class of diseases. In the case of Amyotrophic Lateral Sclerosis (ALS), current estimates indicate 10% of diagnoses are false-positives, while 44% appear to be false negatives. In this study, we developed a new methodology to profile specific medical information from patient medical records for predicting the progression of motor neuron diseases. We implemented a system using Hbase and the Random forest classifier of Apache Mahout to profile medical records provided by the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) site, and we achieved 66% accuracy in the prediction of ALS progress. PMID:25580472

  7. BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes

    PubMed Central

    2015-01-01

    Background Accurately predicting the binding affinities of large sets of protein-ligand complexes is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's binding site has a significant bearing on the accuracy of virtual screening. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited predictive power has been a major roadblock toward cost-effective drug discovery. Therefore, in this work, we present novel SFs employing a large ensemble of neural networks (NN) in conjunction with a diverse set of physicochemical and geometrical features characterizing protein-ligand complexes to predict binding affinity. Results We assess the scoring accuracies of two new ensemble NN SFs based on bagging (BgN-Score) and boosting (BsN-Score), as well as those of conventional SFs in the context of the 2007 PDBbind benchmark that encompasses a diverse set of high-quality protein families. We find that BgN-Score and BsN-Score have more than 25% better Pearson's correlation coefficient (0.804 and 0.816 vs. 0.644) between predicted and measured binding affinities compared to that achieved by a state-of-the-art conventional SF. In addition, these ensemble NN SFs are also at least 19% more accurate (0.804 and 0.816 vs. 0.675) than SFs based on a single neural network that has been traditionally used in drug discovery applications. We further find that ensemble models based on NNs surpass SFs based on the decision-tree ensemble technique Random Forests. Conclusions Ensemble neural networks SFs, BgN-Score and BsN-Score, are the most accurate in predicting binding affinity of protein-ligand complexes among the considered SFs. Moreover, their accuracies are even higher

  8. Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes.

    PubMed

    Keshavarz, Mohammad Hossein; Gharagheizi, Farhad; Shokrolahi, Arash; Zakinejad, Sajjad

    2012-10-30

    Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD(50) with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes. The first model is based on only the number of carbon and hydrogen atoms, which can be improved by several molecular fragments in the second model. For 57 benzoic compounds, where the computed results of quantitative structure-toxicity relationship (QSTR) were recently reported, the predicted results of two simple models of present method are more reliable than QSTR computations. The present simple method is also tested with further 324 benzoic acid compounds including complex molecular structures, which confirm good forecasting ability of the second model. PMID:22959133

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

    SciTech Connect

    Samudrala, Ram; Heffron, Fred; McDermott, Jason E.

    2009-04-24

    The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates, effector proteins, are not. We have used a machine learning approach to identify new secreted effectors. The method integrates evolutionary measures, such as the pattern of homologs in a range of other organisms, and sequence-based features, such as G+C content, amino acid composition and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from Salmonella typhimurium and validated on a corresponding set of effectors from Pseudomonas syringae, after eliminating effectors with detectable sequence similarity. The method was able to identify all of the known effectors in P. syringae with a specificity of 84% and sensitivity of 82%. The reciprocal validation, training on P. syringae and validating on S. typhimurium, gave similar results with a specificity of 86% when the sensitivity level was 87%. These results show that type III effectors in disparate organisms share common features. We found that maximal performance is attained by including an N-terminal sequence of only 30 residues, which agrees with previous studies indicating that this region contains the secretion signal. We then used the method to define the most important residues in this putative secretion signal. Finally, we present novel predictions of secreted effectors in S. typhimurium, some of which have been experimentally validated, and apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis. This approach is a novel and effective way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

  10. MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

    NASA Astrophysics Data System (ADS)

    Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

    2014-03-01

    An early diagnosis of Alzheimer's disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer's Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

  11. Predicting College Students' First Year Success: Should Soft Skills Be Taken into Consideration to More Accurately Predict the Academic Achievement of College Freshmen?

    ERIC Educational Resources Information Center

    Powell, Erica Dion

    2013-01-01

    This study presents a survey developed to measure the skills of entering college freshmen in the areas of responsibility, motivation, study habits, literacy, and stress management, and explores the predictive power of this survey as a measure of academic performance during the first semester of college. The survey was completed by 334 incoming…

  12. Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

    PubMed Central

    Fortin, Élise; Platt, Robert W.; Fontela, Patricia S.; Buckeridge, David L.; Quach, Caroline

    2015-01-01

    Objective The optimal way to measure antimicrobial use in hospital populations, as a complement to surveillance of resistance is still unclear. Using respiratory isolates and antimicrobial prescriptions of nine intensive care units (ICUs), this study aimed to identify the indicator of antimicrobial use that predicted prevalence and incidence rates of resistance with the best accuracy. Methods Retrospective cohort study including all patients admitted to three neonatal (NICU), two pediatric (PICU) and four adult ICUs between April 2006 and March 2010. Ten different resistance / antimicrobial use combinations were studied. After adjustment for ICU type, indicators of antimicrobial use were successively tested in regression models, to predict resistance prevalence and incidence rates, per 4-week time period, per ICU. Binomial regression and Poisson regression were used to model prevalence and incidence rates, respectively. Multiplicative and additive models were tested, as well as no time lag and a one 4-week-period time lag. For each model, the mean absolute error (MAE) in prediction of resistance was computed. The most accurate indicator was compared to other indicators using t-tests. Results Results for all indicators were equivalent, except for 1/20 scenarios studied. In this scenario, where prevalence of carbapenem-resistant Pseudomonas sp. was predicted with carbapenem use, recommended daily doses per 100 admissions were less accurate than courses per 100 patient-days (p = 0.0006). Conclusions A single best indicator to predict antimicrobial resistance might not exist. Feasibility considerations such as ease of computation or potential external comparisons could be decisive in the choice of an indicator for surveillance of healthcare antimicrobial use. PMID:26710322

  13. 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

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

    PubMed

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

    2015-08-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of NBEs in humans can be successfully obtained early in the drug development process by the use of microdosing in a small group of healthy subjects combined with ultrasensitive accelerator mass spectrometry (AMS). After only minimal preclinical testing, we performed a first-in-human phase 0/phase 1 trial with a human recombinant therapeutic protein (RESCuing Alkaline Phosphatase, human recombinant placental alkaline phosphatase [hRESCAP]) to assess its safety and kinetics. Pharmacokinetic analysis showed dose linearity from microdose (53 μg) [(14) C]-hRESCAP to therapeutic doses (up to 5.3 mg) of the protein in healthy volunteers. This study demonstrates the value of a microdosing approach in a very small cohort for accelerating the clinical development of NBEs. PMID:25869840

  15. A new accurate ground-state potential energy surface of ethylene and predictions for rotational and vibrational energy levels

    NASA Astrophysics Data System (ADS)

    Delahaye, Thibault; Nikitin, Andrei; Rey, Michaël; Szalay, Péter G.; Tyuterev, Vladimir G.

    2014-09-01

    In this paper we report a new ground state potential energy surface for ethylene (ethene) C2H4 obtained from extended ab initio calculations. The coupled-cluster approach with the perturbative inclusion of the connected triple excitations CCSD(T) and correlation consistent polarized valence basis set cc-pVQZ was employed for computations of electronic ground state energies. The fit of the surface included 82 542 nuclear configurations using sixth order expansion in curvilinear symmetry-adapted coordinates involving 2236 parameters. A good convergence for variationally computed vibrational levels of the C2H4 molecule was obtained with a RMS(Obs.-Calc.) deviation of 2.7 cm-1 for fundamental bands centers and 5.9 cm-1 for vibrational bands up to 7800 cm-1. Large scale vibrational and rotational calculations for 12C2H4, 13C2H4, and 12C2D4 isotopologues were performed using this new surface. Energy levels for J = 20 up to 6000 cm-1 are in a good agreement with observations. This represents a considerable improvement with respect to available global predictions of vibrational levels of 13C2H4 and 12C2D4 and rovibrational levels of 12C2H4.

  16. Multiple biomarkers for mortality prediction in peripheral arterial disease.

    PubMed

    Amrock, Stephen M; Weitzman, Michael

    2016-04-01

    Few studies have assessed which biomarkers influence mortality risk among those with peripheral arterial disease (PAD). We analyzed data from 556 individuals identified to have PAD (i.e. ankle-brachial index ⩽0.9) with available measurements of C-reactive protein, the neutrophil-to-lymphocyte ratio (NLR), homocysteine, and the urinary albumin-to-creatinine ratio (UACR) in the 1999-2004 National Health and Nutrition Examination Survey. We investigated whether a combination of these biomarkers improved the prediction of all-cause and cardiovascular mortality beyond conventional risk factors. During follow-up (median, 8.1 years), 277 of 556 participants died; 63 deaths were attributed to cardiovascular disease. After adjusting for conventional risk factors, Cox proportional-hazards models showed the following to be most strongly associated with all-cause mortality (each is followed by the adjusted hazard ratio [HR] per 1 standard deviation increment in the log values): homocysteine (1.31), UACR (1.21), and NLR (1.20). UACR alone significantly predicted cardiovascular mortality (1.53). Persons in the highest quintile of multimarker scores derived from regression coefficients of significant biomarkers had elevated risks of all-cause mortality (adjusted HR, 2.45; 95% CI, 1.66-3.62; p for trend, <0.001) and cardiovascular mortality (adjusted HR, 2.20; 95% CI, 1.02-4.71; p for trend, 0.053) compared to those in the lowest two quintiles. The addition of continuous multimarker scores to conventional risk factors improved risk stratification of all-cause mortality (integrated discrimination improvement [IDI], 0.162; p<0.00001) and cardiovascular mortality (IDI, 0.058; p<0.00001). In conclusion, the addition of a continuous multimarker score to conventional risk factors improved mortality prediction among patients with PAD. PMID:26762418

  17. A Low-Cost Method for Multiple Disease Prediction

    PubMed Central

    Bayati, Mohsen; Bhaskar, Sonia; Montanari, Andrea

    2015-01-01

    Recently, in response to the rising costs of healthcare services, employers that are financially responsible for the healthcare costs of their workforce have been investing in health improvement programs for their employees. A main objective of these so called “wellness programs” is to reduce the incidence of chronic illnesses such as cardiovascular disease, cancer, diabetes, and obesity, with the goal of reducing future medical costs. The majority of these wellness programs include an annual screening to detect individuals with the highest risk of developing chronic disease. Once these individuals are identified, the company can invest in interventions to reduce the risk of those individuals. However, capturing many biomarkers per employee creates a costly screening procedure. We propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons to a statistical benchmark. PMID:26958164

  18. Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence

    PubMed Central

    Ellis, Hayley P.; Greenslade, Mark; Powell, Ben; Spiteri, Inmaculada; Sottoriva, Andrea; Kurian, Kathreena M.

    2015-01-01

    Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB. PMID:26636033

  19. A text mining approach to the prediction of disease status from clinical discharge summaries.

    PubMed

    Yang, Hui; Spasic, Irena; Keane, John A; Nenadic, Goran

    2009-01-01

    OBJECTIVE The authors present a system developed for the Challenge in Natural Language Processing for Clinical Data-the i2b2 obesity challenge, whose aim was to automatically identify the status of obesity and 15 related co-morbidities in patients using their clinical discharge summaries. The challenge consisted of two tasks, textual and intuitive. The textual task was to identify explicit references to the diseases, whereas the intuitive task focused on the prediction of the disease status when the evidence was not explicitly asserted. DESIGN The authors assembled a set of resources to lexically and semantically profile the diseases and their associated symptoms, treatments, etc. These features were explored in a hybrid text mining approach, which combined dictionary look-up, rule-based, and machine-learning methods. MEASUREMENTS The methods were applied on a set of 507 previously unseen discharge summaries, and the predictions were evaluated against a manually prepared gold standard. The overall ranking of the participating teams was primarily based on the macro-averaged F-measure. RESULTS The implemented method achieved the macro-averaged F-measure of 81% for the textual task (which was the highest achieved in the challenge) and 63% for the intuitive task (ranked 7(th) out of 28 teams-the highest was 66%). The micro-averaged F-measure showed an average accuracy of 97% for textual and 96% for intuitive annotations. CONCLUSIONS The performance achieved was in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease statuses from clinical discharge summaries. PMID:19390098

  20. Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

    PubMed Central

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

    Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases. PMID:25013377

  1. Genomic medicine and risk prediction across the disease spectrum.

    PubMed

    Kotze, Maritha J; Lückhoff, Hilmar K; Peeters, Armand V; Baatjes, Karin; Schoeman, Mardelle; van der Merwe, Lize; Grant, Kathleen A; Fisher, Leslie R; van der Merwe, Nicole; Pretorius, Jacobus; van Velden, David P; Myburgh, Ettienne J; Pienaar, Fredrieka M; van Rensburg, Susan J; Yako, Yandiswa Y; September, Alison V; Moremi, Kelebogile E; Cronje, Frans J; Tiffin, Nicki; Bouwens, Christianne S H; Bezuidenhout, Juanita; Apffelstaedt, Justus P; Hough, F Stephen; Erasmus, Rajiv T; Schneider, Johann W

    2015-01-01

    Genomic medicine is based on the knowledge that virtually every medical condition, disease susceptibility or response to treatment is caused, regulated or influenced by genes. Genetic testing may therefore add value across the disease spectrum, ranging from single-gene disorders with a Mendelian inheritance pattern to complex multi-factorial diseases. The critical factors for genomic risk prediction are to determine: (1) where the genomic footprint of a particular susceptibility or dysfunction resides within this continuum, and (2) to what extent the genetic determinants are modified by environmental exposures. Regarding the small subset of highly penetrant monogenic disorders, a positive family history and early disease onset are mostly sufficient to determine the appropriateness of genetic testing in the index case and to inform pre-symptomatic diagnosis in at-risk family members. In more prevalent polygenic non-communicable diseases (NCDs), the use of appropriate eligibility criteria is required to ensure a balance between benefit and risk. An additional screening step may therefore be necessary to identify individuals most likely to benefit from genetic testing. This need provided the stimulus for the development of a pathology-supported genetic testing (PSGT) service as a new model for the translational implementation of genomic medicine in clinical practice. PSGT is linked to the establishment of a research database proven to be an invaluable resource for the validation of novel and previously described gene-disease associations replicated in the South African population for a broad range of NCDs associated with increased cardio-metabolic risk. The clinical importance of inquiry concerning family history in determining eligibility for personalized genotyping was supported beyond its current limited role in diagnosing or screening for monogenic subtypes of NCDs. With the recent introduction of advanced microarray-based breast cancer subtyping, genetic testing

  2. Predicting the effects of dietary manipulation in chronic renal disease

    SciTech Connect

    El Nahas, A.M.; Brady, S.A.; Masters-Thomas, A.; Wilkinson, V.; Hilson, A.J.W.; Moorhead, J.F.

    1984-01-01

    It has been suggested that the progressive fall in renal function in some patients with CRF is due to hyperfusion of the remnant nephrons in response to the relatively high protein diet of modern life. The authors attempted to assess this and to see what was the shortest time in which any effect could be demonstrated. In the first phase, 39 patients with CRF had their renal function followed for 6 months on their normal diet and 6 months on a low-protein diet (LPD). The patients on LPD all showed an improvement in the rate of fall of renal function. This was marked in patients with mainly tubular disease, and poor in those with glomerular and vascular disease. In the second phase, 11 of these patients (and 1 other) were started on a high protein diet (HPD) for two weeks, and then switched back to a LPD for 2 weeks. There was no change in GFR during this period, but there were marked changes in ERPF, which correlated well with the changes in renal function in the first phase (r = 0.76, rho < 0.01); 4/4 patients with tubular disease showed a rise in ERPF on HPD and a fall on LPD, while only 4/8 with glomerular or vascular disease responded. In the third phase, they assessed the effect of a single high-protein meal in normal volunteers. This showed that there are major changes in hemodynamics following a meal, such that it is not possible to make any statement about renal function using the single-shot methods. The authors conclude that a 2-week period of HPD followed by LPD allows prediction of the possible beneficial response to diet in CRF; that this is best monitored by ERPF; and that a single meal may invalidate renal function measurement.

  3. Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

    PubMed Central

    Poil, Simon-Shlomo; de Haan, Willem; van der Flier, Wiesje M.; Mansvelder, Huibert D.; Scheltens, Philip; Linkenkaer-Hansen, Klaus

    2013-01-01

    Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention. PMID:24106478

  4. A new estimate of family disease history providing improved prediction of disease risks

    PubMed Central

    Feng, Rui; McClure, Leslie A.; Tiwari, Hemant K.; Howard, George

    2011-01-01

    SUMMARY Complex diseases often aggregate within families and using the history of family members’ disease can potentially increase the accuracy of the risk assessment and allow clinicians to better target on high risk individuals. However, available family risk scores do not reflect the age of disease onset, gender and family structures simultaneously. In this paper, we propose an alternative approach for a family risk score, the stratified log-rank family score (SLFS), which incorporates the age of disease onset of family members, gender differences and the relationship among family members. Via simulation, we demonstrate that the new SLFS is more closely associated with the true family risk for the disease and more robust to family sizes than two existing methods. We apply our proposed method and the two existing methods to a study of stroke and heart disease. The results show that assessing family history can improve the prediction of disease risks and the SLFS has strongest positive associations with both myocardial infarction and stroke. PMID:19170247

  5. 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. PMID:26865669

  6. Prediction of single versus multivessel disease following myocardial infarction using 201-thallium scintigraphy and electrocardiographic stress testing

    SciTech Connect

    Weiss, R.J.; Morise, A.P.; Raabe, D.S. Jr.; Sbarbaro, J.A.

    1983-11-01

    Fifty patients were evaluated who suffered a single myocardial infarction with graded electrocardiographic stress testing, 201-thallium myocardial perfusion imaging and coronary angiography to assess the role of noninvasive indices as predictors of single versus multivessel coronary artery disease. Multivessel involvement was defined angiographically as the presence of two or more major coronary arteries with at least a 70% intraluminal diameter narrowing. Multivessel disease was defined scintigraphically as the presence of stress and/or redistribution perfusion defects in the distribution of more than one coronary artery. The results of stress electrocardiography were not useful in differentiating patients with single (9/16 positive) versus multivessel (22/34 positive) disease. The degree of exercise-induced ST-segment depression was also not helpful. Stress 201-thallium imaging did offer limited additional information with correct predictions of multivessel disease in 21 of 26 patients. Predictions of single-vessel disease were accurate in 11 of 24 patients. Eleven of these 13 incorrect predictions of single-vessel disease were due to the relative insensitivity of the thallium stress image to perceive defect in the anterior wall when the left anterior descending artery had significant obstruction at catheterization. Further refinements of stress perfusion imaging are needed before this method can be used to reliably separate patients with single and multivessel disease after myocardial infarction.

  7. 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

  8. 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

    2016-01-01

    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

  9. Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets

    PubMed Central

    Volpato, Viola; Alshomrani, Badr; Pollastri, Gianluca

    2015-01-01

    Intrinsically-disordered regions lack a well-defined 3D structure, but play key roles in determining the function of many proteins. Although predictors of disorder have been shown to achieve relatively high rates of correct classification of these segments, improvements over the the years have been slow, and accurate methods are needed that are capable of accommodating the ever-increasing amount of structurally-determined protein sequences to try to boost predictive performances. In this paper, we propose a predictor for short disordered regions based on bidirectional recurrent neural networks and tested by rigorous five-fold cross-validation on a large, non-redundant dataset collected from MobiDB, a new comprehensive source of protein disorder annotations. The system exploits sequence and structural information in the forms of frequency profiles, predicted secondary structure and solvent accessibility and direct disorder annotations from homologous protein structures (templates) deposited in the Protein Data Bank. The contributions of sequence, structure and homology information result in large improvements in predictive accuracy. Additionally, the large scale of the training set leads to low false positive rates, making our systems a robust and efficient way to address high-throughput disorder prediction. PMID:26307973

  10. Lipid-Related Markers and Cardiovascular Disease Prediction

    PubMed Central

    2014-01-01

    Context The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated. Objective To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction. Design, Setting, and Participants Individual records were available for 165 544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968–2007) with up to 15 126 incident fatal or nonfatal CVD outcomes (10 132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6–14 years). Main Outcome Measures Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10%), intermediate (10%–<20%), and high (≥20%) risk. Results The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model’s discrimination: C-index change, 0.0006 (95% CI, 0.0002–0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009–0.0023) for lipoprotein(a); and 0.0018 (95% CI, 0.0010–0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100 000 adults aged 40 years or older, 15 436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A2 mass, 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III

  11. Predictive genetic testing for complex diseases: a public health perspective

    PubMed Central

    Marzuillo, C.; De Vito, C.; D’Andrea, E.; Rosso, A.

    2014-01-01

    From a public health perspective, systematic, evidence-based technology assessments and economic evaluations are needed to guide the incorporation of genomics into clinical and public health practice. However, scientific evidence on the effectiveness of predictive genetic tests is difficult to obtain. This review first highlights the similarities and differences between traditional screening tests and predictive genetic testing for complex diseases and goes on to describe frameworks for the evaluation of genetic testing that have been developed in recent years providing some evidence that currently genetic tests are not used in an appropriate way. Nevertheless, evidence-based recommendations are already available for some genomic applications that can reduce morbidity and mortality and many more are expected to emerge over the next decade. The time is now ripe for the introduction of a range of genetic tests into healthcare practice, but this will require the development of specific health policies, proper public health evaluations, organizational changes within the healthcare systems, capacity building among the healthcare workforce and the education of the public. PMID:24049051

  12. Risk reversals in predictive testing for Huntington disease.

    PubMed Central

    Almqvist, E; Adam, S; Bloch, M; Fuller, A; Welch, P; Eisenberg, D; Whelan, D; Macgregor, D; Meschino, W; Hayden, M R

    1997-01-01

    The first predictive testing for Huntington disease (HD) was based on analysis of linked polymorphic DNA markers to estimate the likelihood of inheriting the mutation for HD. Limits to accuracy included recombination between the DNA markers and the mutation, pedigree structure, and whether DNA samples were available from family members. With direct tests for the HD mutation, we have assessed the accuracy of results obtained by linkage approaches when requested to do so by the test individuals. For six such individuals, there was significant disparity between the tests. Three went from a decreased risk to an increased risk, while in another three the risk was decreased. Knowledge of the potential reasons for these changes in results and impact of these risk reversals on both patients and the counseling team can assist in the development of strategies for the prevention and, where necessary, management of a risk reversal in any predictive testing program. Images Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 PMID:9382108

  13. New machine-learning algorithms for prediction of Parkinson's disease

    NASA Astrophysics Data System (ADS)

    Mandal, Indrajit; Sairam, N.

    2014-03-01

    This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

  14. Predictive Value of Auricular Diagnosis on Coronary Heart Disease

    PubMed Central

    Kwai-Ping Suen, Lorna; Lau, Yuk-kong; Ma, Hok-cheung; Lai, Kam-wai; Holroyd, Eleanor

    2012-01-01

    The ear has a reflexive property; therefore, various physical attributes may appear on the auricle when disorders of the internal organs or other parts of the body exist. Auricular diagnostics is an objective, painless, and noninvasive method that provides rapid access to information. Thus, the association between auricular signals and coronary heart disease (CHD) should be further investigated. A case control study was conducted to determine the predictive value of auricular signals on 100 cases of CHD (CHD+ve = 50; CHD−ve = 50) via visual inspection, electrical skin resistance measurement, and tenderness testing. The results showed that the presence of an ear lobe crease (ELC) was significantly associated with coronary heart disease. The “heart” zone of the CHD+ve group significantly exhibited higher conductivity on both ears than that of the controls. The CHD+ve group experienced significant tenderness in the “heart” region compared with those in the CHD−ve group in both acute and chronic conditions. Further studies that take into consideration the impact of age, race, and earlobe shape on ELC prevalence in a larger sample should be done. PMID:23326290

  15. Improved apparatus for predictive diagnosis of rotator cuff disease

    NASA Astrophysics Data System (ADS)

    Pillai, Anup; Hall, Brittany N.; Thigpen, Charles A.; Kwartowitz, David M.

    2014-03-01

    Rotator cuff disease impacts over 50% of the population over 60, with reports of incidence being as high as 90% within this population, causing pain and possible loss of function. The rotator cuff is composed of muscles and tendons that work in tandem to support the shoulder. Heavy use of these muscles can lead to rotator cuff tear, with the most common causes is age-related degeneration or sport injuries, both being a function of overuse. Tears ranges in severity from partial thickness tear to total rupture. Diagnostic techniques are based on physical assessment, detailed patient history, and medical imaging; primarily X-ray, MRI and ultrasonography are the chosen modalities for assessment. The final treatment technique and imaging modality; however, is chosen by the clinician is at their discretion. Ultrasound has been shown to have good accuracy for identification and measurement of full-thickness and partial-thickness rotator cuff tears. In this study, we report on the progress and improvement of our method of transduction and analysis of in situ measurement of rotator cuff biomechanics. We have improved the ability of the clinician to apply a uniform force to the underlying musculotendentious tissues while simultaneously obtaining the ultrasound image. This measurement protocol combined with region of interest (ROI) based image processing will help in developing a predictive diagnostic model for treatment of rotator cuff disease and help the clinicians choose the best treatment technique.

  16. Voxelwise spectral diffusional connectivity and its applications to Alzheimer's disease and intelligence prediction.

    PubMed

    Li, Junning; Jin, Yan; Shi, Yonggang; Dinov, Ivo D; Wang, Danny J; Toga, Arthur W; Thompson, Paul M

    2013-01-01

    Human brain connectivity can be studied using graph theory. Many connectivity studies parcellate the brain into regions and count fibres extracted between them. The resulting network analyses require validation of the tractography, as well as region and parameter selection. Here we investigate whole brain connectivity from a different perspective. We propose a mathematical formulation based on studying the eigenvalues of the Laplacian matrix of the diffusion tensor field at the voxel level. This voxelwise matrix has over a million parameters, but we derive the Kirchhoff complexity and eigen-spectrum through elegant mathematical theorems, without heavy computation. We use these novel measures to accurately estimate the voxelwise connectivity in multiple biomedical applications such as Alzheimer's disease and intelligence prediction. PMID:24505723

  17. Early noninvasive measurement of the indocyanine green plasma disappearance rate accurately predicts early graft dysfunction and mortality after deceased donor liver transplantation.

    PubMed

    Olmedilla, Luis; Pérez-Peña, José María; Ripoll, Cristina; Garutti, Ignacio; de Diego, Roberto; Salcedo, Magdalena; Jiménez, Consuelo; Bañares, Rafael

    2009-10-01

    Early diagnosis of graft dysfunction in liver transplantation is essential for taking appropriate action. Indocyanine green clearance is closely related to liver function and can be measured noninvasively by spectrophotometry. The objectives of this study were to prospectively analyze the relationship between the indocyanine green plasma disappearance rate (ICGPDR) and early graft function after liver transplantation and to evaluate the role of ICGPDR in the prediction of severe graft dysfunction (SGD). One hundred seventy-two liver transplants from deceased donors were analyzed. Ten patients had SGD: 6 were retransplanted, and 4 died while waiting for a new graft. The plasma disappearance rate was measured 1 hour (PDRr60) and within the first 24 hours (PDR1) after reperfusion, and it was significantly lower in the SGD group. PDRr60 and PDR1 were excellent predictors of SGD. A threshold PDRr60 value of 10.8%/minute and a PDR1 value of 10%/minute accurately predicted SGD with areas under the receiver operating curve of 0.94 (95% confidence interval, 0.89-0.97) and 0.96 (95% confidence interval, 0.92-0.98), respectively. In addition, survival was significantly lower in patients with PDRr60 values below 10.8%/minute (53%, 47%, and 47% versus 95%, 94%, and 90% at 3, 6, and 12 months, respectively) and with PDR1 values below 10%/minute (62%, 62%, and 62% versus 94%, 92%, and 88%). In conclusion, very early noninvasive measurement of ICGPDR can accurately predict early severe graft dysfunction and mortality after liver transplantation. PMID:19790138

  18. The Patterns, Risk Factors, and Prediction of Progression in Chronic Kidney Disease: A Narrative Review.

    PubMed

    Collister, David; Ferguson, Thomas; Komenda, Paul; Tangri, Navdeep

    2016-07-01

    Chronic kidney disease (CKD) is a global public health problem that is associated with excess morbidity, mortality, and health resource utilization. The progression of CKD is defined by a decrease in glomerular filtration rate and leads to a variety of metabolic abnormalities including acidosis, hypertension, anemia, and mineral bone disorder. Lower glomerular filtration rate also bears a strong relationship with an increased risk of cardiovascular events, end-stage renal disease, and death. Patterns of CKD progression include linear and nonlinear trajectories, but kidney function can remain stable for years in some individuals. Addressing modifiable risk factors for the progression of CKD is needed to attenuate its associated morbidity and mortality. Developing effective risk prediction models for CKD progression is critical to identify patients who are more likely to benefit from interventions and more intensive monitoring. Accurate risk-prediction algorithms permit systems to best align health care resources with risk to maximize their effects and efficiency while guiding overall decision making. PMID:27475658

  19. Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants

    PubMed Central

    Romanos, Jihane; Rosén, Anna; Kumar, Vinod; Trynka, Gosia; Franke, Lude; Szperl, Agata; Gutierrez-Achury, Javier; van Diemen, Cleo C; Kanninga, Roan; Jankipersadsing, Soesma A; Steck, Andrea; Eisenbarth, Georges; van Heel, David A; Cukrowska, Bozena; Bruno, Valentina; Mazzilli, Maria Cristina; Núñez, Concepcion; Bilbao, Jose Ramon; Mearin, M Luisa; Barisani, Donatella; Rewers, Marian; Norris, Jill M; Ivarsson, Anneli; Boezen, H Marieke; Liu, Edwin; Wijmenga, Cisca

    2014-01-01

    Background The majority of coeliac disease (CD) patients are not being properly diagnosed and therefore remain untreated, leading to a greater risk of developing CD-associated complications. The major genetic risk heterodimer, HLA-DQ2 and DQ8, is already used clinically to help exclude disease. However, approximately 40% of the population carry these alleles and the majority never develop CD. Objective We explored whether CD risk prediction can be improved by adding non-HLA-susceptible variants to common HLA testing. Design We developed an average weighted genetic risk score with 10, 26 and 57 single nucleotide polymorphisms (SNP) in 2675 cases and 2815 controls and assessed the improvement in risk prediction provided by the non-HLA SNP. Moreover, we assessed the transferability of the genetic risk model with 26 non-HLA variants to a nested case–control population (n=1709) and a prospective cohort (n=1245) and then tested how well this model predicted CD outcome for 985 independent individuals. Results Adding 57 non-HLA variants to HLA testing showed a statistically significant improvement compared to scores from models based on HLA only, HLA plus 10 SNP and HLA plus 26 SNP. With 57 non-HLA variants, the area under the receiver operator characteristic curve reached 0.854 compared to 0.823 for HLA only, and 11.1% of individuals were reclassified to a more accurate risk group. We show that the risk model with HLA plus 26 SNP is useful in independent populations. Conclusions Predicting risk with 57 additional non-HLA variants improved the identification of potential CD patients. This demonstrates a possible role for combined HLA and non-HLA genetic testing in diagnostic work for CD. PMID:23704318

  20. Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles

    PubMed Central

    2010-01-01

    Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is available to date for the study of HIV disease progression. In this study, we propose a novel machine-learning based framework for the prediction of AIDS disease progression in three stages (RP, SP, and LTNP) using the HIV structural gp120 profile. This new intelligent framework proves to be accurate and provides an important benchmark for predicting AIDS disease progression computationally. The model is trained using a novel HIV gp120 glycosylation structural profile to detect possible stages of AIDS disease progression for the target sequences of HIV+ individuals. The performance of the proposed model was compared to seven existing different machine-learning models on newly proposed gp120-Benchmark_1 dataset in terms of error-rate (MSE), accuracy (CCI), stability (STD), and complexity (TBM). The novel framework showed better predictive performance with 67.82% CCI, 30.21 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS disease progression of 50 HIV+ individuals. This framework is an invaluable bioinformatics tool that will be useful to the clinical assessment of viral pathogenesis. PMID:21143806

  1. 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. PMID:23343036

  2. Prediction of chirality- and size-dependent elastic properties of single-walled boron nitride nanotubes based on an accurate molecular mechanics model

    NASA Astrophysics Data System (ADS)

    Ansari, R.; Mirnezhad, M.; Sahmani, S.

    2015-04-01

    Molecular mechanics theory has been widely used to investigate the mechanical properties of nanostructures analytically. However, there is a limited number of research in which molecular mechanics model is utilized to predict the elastic properties of boron nitride nanotubes (BNNTs). In the current study, the mechanical properties of chiral single-walled BNNTs are predicted analytically based on an accurate molecular mechanics model. For this purpose, based upon the density functional theory (DFT) within the framework of the generalized gradient approximation (GGA), the exchange correlation of Perdew-Burke-Ernzerhof is adopted to evaluate force constants used in the molecular mechanics model. Afterwards, based on the principle of molecular mechanics, explicit expressions are given to calculate surface Young's modulus and Poisson's ratio of the single-walled BNNTs for different values of tube diameter and types of chirality. Moreover, the values of surface Young's modulus, Poisson's ratio and bending stiffness of boron nitride sheets are obtained via the DFT as byproducts. The results predicted by the present model are in reasonable agreement with those reported by other models in the literature.

  3. Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features.

    PubMed

    Wei, Rizhen; Li, Chuhan; Fogelson, Noa; Li, Ling

    2016-01-01

    Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD. PMID:27148045

  4. Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features

    PubMed Central

    Wei, Rizhen; Li, Chuhan; Fogelson, Noa; Li, Ling

    2016-01-01

    Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD. PMID:27148045

  5. Structure-Based Prediction of Unstable Regions in Proteins: Applications to Protein Misfolding Diseases

    NASA Astrophysics Data System (ADS)

    Guest, Will; Cashman, Neil; Plotkin, Steven

    2009-03-01

    Protein misfolding is a necessary step in the pathogenesis of many diseases, including Creutzfeldt-Jakob disease (CJD) and familial amyotrophic lateral sclerosis (fALS). Identifying unstable structural elements in their causative proteins elucidates the early events of misfolding and presents targets for inhibition of the disease process. An algorithm was developed to calculate the Gibbs free energy of unfolding for all sequence-contiguous regions of a protein using three methods to parameterize energy changes: a modified G=o model, changes in solvent-accessible surface area, and solution of the Poisson-Boltzmann equation. The entropic effects of disulfide bonds and post-translational modifications are treated analytically. It incorporates a novel method for finding local dielectric constants inside a protein to accurately handle charge effects. We have predicted the unstable parts of prion protein and superoxide dismutase 1, the proteins involved in CJD and fALS respectively, and have used these regions as epitopes to prepare antibodies that are specific to the misfolded conformation and show promise as therapeutic agents.

  6. Urate predicts rate of clinical decline in Parkinson disease

    PubMed Central

    Ascherio, Alberto; LeWitt, Peter A.; Xu, Kui; Eberly, Shirley; Watts, Arthur; Matson, Wayne R.; Marras, Connie; Kieburtz, Karl; Rudolph, Alice; Bogdanov, Mikhail B.; Schwid, Steven R.; Tennis, Marsha; Tanner, Caroline M.; Beal, M. Flint; Lang, Anthony E.; Oakes, David; Fahn, Stanley; Shoulson, Ira; Schwarzschild, Michael A.

    2009-01-01

    Context The risk of Parkinson disease (PD) and its rate of progression may decline with increasing blood urate, a major antioxidant. Objective To determine whether serum and cerebrospinal fluid (CSF) concentrations of urate predict clinical progression in patients with PD. Design, Setting, and Participants 800 subjects with early PD enrolled in the DATATOP trial. Pre-treatment urate was measured in serum for 774 subjects and in CSF for 713. Main Outcome Measures Treatment-, age- and sex-adjusted hazard ratios (HRs) for clinical disability requiring levodopa therapy, the pre-specified primary endpoint. Results The HR of progressing to endpoint decreased with increasing serum urate (HR for 1 standard deviation increase = 0.82; 95% CI = 0.73 to 0.93). In analyses stratified by α-tocopherol treatment (2,000 IU/day), a decrease in the HR for the primary endpoint was seen only among subjects not treated with α-tocopherol (HR = 0.75; 95% CI = 0.62 to 0.89, versus those treated HR = 0.90; 95% CI = 0.75 to 1.08). Results were similar for the rate of change in the United Parkinson Disease Rating Scale (UPDRS). CSF urate was also inversely related to both the primary endpoint (HR for highest versus lowest quintile = 0.65; 95% CI: 0.54 to 0.96) and to the rate of change in UPDRS. As with serum urate, these associations were present only among subjects not treated with α-tocopherol. Conclusion Higher serum and CSF urate at baseline were associated with slower rates of clinical decline. The findings strengthen the link between urate and PD and the rationale for considering CNS urate elevation as a potential strategy to slow PD progression. PMID:19822770

  7. BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease

    PubMed Central

    Klöppel, Stefan; Koutsouleris, Nikolaos; Sauer, Heinrich

    2013-01-01

    Alzheimer’s disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07–1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options. PMID:23826273

  8. Predicting mortality after acute coronary syndromes in people with chronic obstructive pulmonary disease

    PubMed Central

    Smeeth, Liam; Pearce, Neil; Herrett, Emily; Timmis, Adam; Hemingway, Harry; Wedzicha, Jadwiga; Quint, Jennifer K

    2016-01-01

    Objective To assess the accuracy of Global Registry of Acute Coronary Events (GRACE) scores in predicting mortality at 6 months for people with chronic obstructive pulmonary disease (COPD) and to investigate how it might be improved. Methods Data were obtained on 481 849 patients with acute coronary syndrome admitted to UK hospitals between January 2003 and June 2013 from the Myocardial Ischaemia National Audit Project (MINAP) database. We compared risk of death between patients with COPD and those without COPD at 6 months, adjusting for predicted risk of death. We then assessed whether several modifications improved the accuracy of the GRACE score for people with COPD. Results The risk of death after adjusting for GRACE score predicted that risk of death was higher for patients with COPD than that for other patients (RR 1.29, 95% CI 1.28 to 1.33). Adding smoking into the GRACE score model did not improve accuracy for patients with COPD. Either adding COPD into the model (relative risk (RR) 1.00, 0.94 to 1.02) or multiplying the GRACE score by 1.3 resulted in better performance (RR 0.99, 0.96 to 1.01). Conclusions GRACE scores underestimate risk of death for people with COPD. A more accurate prediction of risk of death can be obtained by adding COPD into the GRACE score equation, or by multiplying the GRACE score predicted risk of death by 1.3 for people with COPD. This means that one third of patients with COPD currently classified as low risk should be classified as moderate risk, and could be considered for more aggressive early treatment after non-ST-segment elevation myocardial infarction or unstable angina. PMID:27177534

  9. Alzheimer's disease pattern of brain atrophy predicts cognitive decline in Parkinson's disease

    PubMed Central

    Dietz, Nicole; Duda, John E.; Wolk, David A.; Doshi, Jimit; Xie, Sharon X.; Davatzikos, Christos; Clark, Christopher M.; Siderowf, Andrew

    2012-01-01

    Research suggests overlap in brain regions undergoing neurodegeneration in Parkinson's and Alzheimer's disease. To assess the clinical significance of this, we applied a validated Alzheimer's disease-spatial pattern of brain atrophy to patients with Parkinson's disease with a range of cognitive abilities to determine its association with cognitive performance and decline. At baseline, 84 subjects received structural magnetic resonance imaging brain scans and completed the Dementia Rating Scale-2, and new robust and expanded Dementia Rating Scale-2 norms were applied to cognitively classify participants. Fifty-nine non-demented subjects were assessed annually with the Dementia Rating Scale-2 for two additional years. Magnetic resonance imaging scans were quantified using both a region of interest approach and voxel-based morphometry analysis, and a method for quantifying the presence of an Alzheimer's disease spatial pattern of brain atrophy was applied to each scan. In multivariate models, higher Alzheimer's disease pattern of atrophy score was associated with worse global cognitive performance (β = −0.31, P = 0.007), including in non-demented patients (β = −0.28, P = 0.05). In linear mixed model analyses, higher baseline Alzheimer's disease pattern of atrophy score predicted long-term global cognitive decline in non-demented patients [F(1, 110) = 9.72, P = 0.002], remarkably even in those with normal cognition at baseline [F(1, 80) = 4.71, P = 0.03]. In contrast, in cross-sectional and longitudinal analyses there was no association between region of interest brain volumes and cognitive performance in patients with Parkinson's disease with normal cognition. These findings support involvement of the hippocampus and parietal–temporal cortex with cognitive impairment and long-term decline in Parkinson's disease. In addition, an Alzheimer's disease pattern of brain atrophy may be a preclinical biomarker of cognitive decline

  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. State-of-the-art in permeability determination from well log data: Part 2- verifiable, accurate permeability predictions, the touch-stone of all models

    SciTech Connect

    Mohaghegh, S.; Balan, B.; Ameri, S.

    1995-12-31

    The ultimate test for any technique that bears the claim of permeability prediction from well log data, is accurate and verifiable prediction of permeability for wells from which only the well log data is available. So far all the available models and techniques have been tried on data that includes both well logs and the corresponding permeability values. This approach at best is nothing more than linear or nonlinear curve fitting. The objective of this paper is to test the capability of the most promising of these techniques in independent (where corresponding permeability values are not available or have not been used in development of the model) prediction of permeability in a heterogeneous formation. These techniques are {open_quotes}Multiple Regression{close_quotes} and {open_quotes}Virtual Measurements using Artificial Neural Networks.{close_quotes} For the purposes of this study several wells from a heterogeneous formation in West Virginia were selected. Well log data and corresponding permeability values for these wells were available. The techniques were applied to the remaining data and a permeability model for the field was developed. The model was then applied to the well that was separated from the rest of the data earlier and the results were compared. This approach will test the generalization power of each technique. The result will show that although Multiple Regression provides acceptable results for wells that were used during model development, (good curve fitting,) it lacks a consistent generalization capability, meaning that it does not perform as well with data it has not been exposed to (the data from well that has been put aside). On the other hand, Virtual Measurement technique provides a steady generalization power. This technique is able to perform the permeability prediction task even for the entire wells with no prior exposure to their permeability profile.

  12. Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection

    PubMed Central

    2013-01-01

    Background Clostridium difficile infection poses a significant healthcare burden. However, the derivation of a simple, evidence based prediction rule to assist patient management has not yet been described. This study aimed to identify such a prediction rule to stratify hospital inpatients according to risk of all-cause mortality, at initial diagnosis of infection. Method Univariate, multivariate and decision tree procedures were used to deduce a prediction rule from over 186 variables; retrospectively collated from clinical data for 213 patients. The resulting prediction rule was validated on independent data from a cohort of 158 patients described by Bhangu et al. (Colorectal Disease, 12(3):241-246, 2010). Results Serum albumin levels (g/L) (P = 0.001), respiratory rate (resps /min) (P = 0.002), C-reactive protein (mg/L) (P = 0.034) and white cell count (mcL) (P = 0.049) were predictors of all-cause mortality. Threshold levels of serum albumin ≤ 24.5 g/L, C- reactive protein >228 mg/L, respiratory rate >17 resps/min and white cell count >12 × 103 mcL were associated with an increased risk of all-cause mortality. A simple four variable prediction rule was devised based on these threshold levels and when tested on the initial data, yield an area under the curve score of 0.754 (P < 0.001) using receiver operating characteristics. The prediction rule was then evaluated using independent data, and yield an area under the curve score of 0.653 (P = 0.001). Conclusions Four easily measurable clinical variables can be used to assess the risk of mortality of patients with Clostridium difficile infection and remains robust with respect to independent data. PMID:23849267

  13. Parkinson's disease prediction using diffusion-based atlas approach

    NASA Astrophysics Data System (ADS)

    Teodorescu, Roxana O.; Racoceanu, Daniel; Smit, Nicolas; Cretu, Vladimir I.; Tan, Eng K.; Chan, Ling L.

    2010-03-01

    We study Parkinson's disease (PD) using an automatic specialized diffusion-based atlas. A total of 47 subjects, among who 22 patients diagnosed clinically with PD and 25 control cases, underwent DTI imaging. The EPIs have lower resolution but provide essential anisotropy information for the fiber tracking process. The two volumes of interest (VOI) represented by the Substantia Nigra and the Putamen are detected on the EPI and FA respectively. We use the VOIs for the geometry-based registration. We fuse the anatomical detail detected on FA image for the putamen volume with the EPI. After 3D fibers growing on the two volumes, we compute the fiber density (FD) and the fiber volume (FV). Furthermore, we compare patients based on the extracted fibers and evaluate them according to Hohen&Yahr (H&Y) scale. This paper introduces the method used for automatic volume detection and evaluates the fiber growing method on these volumes. Our approach is important from the clinical standpoint, providing a new tool for the neurologists to evaluate and predict PD evolution. From the technical point of view, the fusion approach deals with the tensor based information (EPI) and the extraction of the anatomical detail (FA and EPI).

  14. Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease.

    PubMed

    Pena, Michelle J; Mischak, Harald; Heerspink, Hiddo J L

    2016-09-01

    The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice. PMID:27344310

  15. An Integrated Children Disease Prediction Tool within a Special Social Network.

    PubMed

    Apostolova Trpkovska, Marika; Yildirim Yayilgan, Sule; Besimi, Adrian

    2016-01-01

    This paper proposes a social network with an integrated children disease prediction system developed by the use of the specially designed Children General Disease Ontology (CGDO). This ontology consists of children diseases and their relationship with symptoms and Semantic Web Rule Language (SWRL rules) that are specially designed for predicting diseases. The prediction process starts by filling data about the appeared signs and symptoms by the user which are after that mapped with the CGDO ontology. Once the data are mapped, the prediction results are presented. The phase of prediction executes the rules which extract the predicted disease details based on the SWRL rule specified. The motivation behind the development of this system is to spread knowledge about the children diseases and their symptoms in a very simple way using the specialized social networking website www.emama.mk. PMID:27071879

  16. Extent of Resection of Glioblastoma Revisited: Personalized Survival Modeling Facilitates More Accurate Survival Prediction and Supports a Maximum-Safe-Resection Approach to Surgery

    PubMed Central

    Marko, Nicholas F.; Weil, Robert J.; Schroeder, Jason L.; Lang, Frederick F.; Suki, Dima; Sawaya, Raymond E.

    2014-01-01

    Purpose Approximately 12,000 glioblastomas are diagnosed annually in the United States. The median survival rate for this disease is 12 months, but individual survival rates can vary with patient-specific factors, including extent of surgical resection (EOR). The goal of our investigation is to develop a reliable strategy for personalized survival prediction and for quantifying the relationship between survival, EOR, and adjuvant chemoradiotherapy. Patients and Methods We used accelerated failure time (AFT) modeling using data from 721 newly diagnosed patients with glioblastoma (from 1993 to 2010) to model the factors affecting individualized survival after surgical resection, and we used the model to construct probabilistic, patient-specific tools for survival prediction. We validated this model with independent data from 109 patients from a second institution. Results AFT modeling using age, Karnofsky performance score, EOR, and adjuvant chemoradiotherapy produced a continuous, nonlinear, multivariable survival model for glioblastoma. The median personalized predictive error was 4.37 months, representing a more than 20% improvement over current methods. Subsequent model-based calculations yield patient-specific predictions of the incremental effects of EOR and adjuvant therapy on survival. Conclusion Nonlinear, multivariable AFT modeling outperforms current methods for estimating individual survival after glioblastoma resection. The model produces personalized survival curves and quantifies the relationship between variables modulating patient-specific survival. This approach provides comprehensive, personalized, probabilistic, and clinically relevant information regarding the anticipated course of disease, the overall prognosis, and the patient-specific influence of EOR and adjuvant chemoradiotherapy. The continuous, nonlinear relationship identified between expected median survival and EOR argues against a surgical management strategy based on rigid EOR

  17. Discovery of a general method of solving the Schrödinger and dirac equations that opens a way to accurately predictive quantum chemistry.

    PubMed

    Nakatsuji, Hiroshi

    2012-09-18

    Just as Newtonian law governs classical physics, the Schrödinger equation (SE) and the relativistic Dirac equation (DE) rule the world of chemistry. So, if we can solve these equations accurately, we can use computation to predict chemistry precisely. However, for approximately 80 years after the discovery of these equations, chemists believed that they could not solve SE and DE for atoms and molecules that included many electrons. This Account reviews ideas developed over the past decade to further the goal of predictive quantum chemistry. Between 2000 and 2005, I discovered a general method of solving the SE and DE accurately. As a first inspiration, I formulated the structure of the exact wave function of the SE in a compact mathematical form. The explicit inclusion of the exact wave function's structure within the variational space allows for the calculation of the exact wave function as a solution of the variational method. Although this process sounds almost impossible, it is indeed possible, and I have published several formulations and applied them to solve the full configuration interaction (CI) with a very small number of variables. However, when I examined analytical solutions for atoms and molecules, the Hamiltonian integrals in their secular equations diverged. This singularity problem occurred in all atoms and molecules because it originates from the singularity of the Coulomb potential in their Hamiltonians. To overcome this problem, I first introduced the inverse SE and then the scaled SE. The latter simpler idea led to immediate and surprisingly accurate solution for the SEs of the hydrogen atom, helium atom, and hydrogen molecule. The free complement (FC) method, also called the free iterative CI (free ICI) method, was efficient for solving the SEs. In the FC method, the basis functions that span the exact wave function are produced by the Hamiltonian of the system and the zeroth-order wave function. These basis functions are called complement

  18. Prediction of hospitalization due to heart diseases by supervised learning methods

    PubMed Central

    Dai, Wuyang; Brisimi, Theodora S.; Adams, William G.; Mela, Theofanie; Saligrama, Venkatesh; Paschalidis, Ioannis Ch.

    2014-01-01

    Background In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that nearly $30.8 billion in hospital care cost during 2006 was potentially preventable, with heart diseases being responsible for about 31% of that amount. Methods Our goal is to accurately and efficiently predict heart-related hospitalizations based on the available patient-specific medical history. To the best of our knowledge, the approaches we introduce are novel for this problem. The prediction of hospitalization is formulated as a supervised classification problem. We use de-identified Electronic Health Record (EHR) data from a large urban hospital in Boston to identify patients with heart diseases. Patients are labeled and randomly partitioned into a training and a test set. We apply five machine learning algorithms, namely Support Vector Machines (SVM), AdaBoost using trees as the weak learner, Logistic Regression, a naïve Bayes event classifier, and a variation of a Likelihood Ratio Test adapted to the specific problem. Each model is trained on the training set and then tested on the test set. Results All five models show consistent results, which could, to some extent, indicate the limit of the achievable prediction accuracy. Our results show that with under 30% false alarm rate, the detection rate could be as high as 82%. These accuracy rates translate to a considerable amount of potential savings, if used in practice. PMID:25497295

  19. Improved prediction of Alzheimer's disease with longitudinal white matter/gray matter contrast changes.

    PubMed

    Grydeland, Håkon; Westlye, Lars T; Walhovd, Kristine B; Fjell, Anders M

    2013-11-01

    Brain morphometry measures derived from magnetic resonance imaging (MRI) are important biomarkers for Alzheimer's disease (AD). The objective of the present study was to test whether we could improve morphometry-based detection and prediction of disease state by use of white matter/gray matter (WM/GM) signal intensity contrast obtained from conventional MRI scans. We hypothesized that including WM/GM contrast change along with measures of atrophy in the entorhinal cortex and the hippocampi would yield better classification of AD patients, and more accurate prediction of early disease progression. T1 -weighted MRI scans from two sessions approximately 2 years apart from 78 participants with AD (Clinical Dementia Rating (CDR) = 0.5-2) and 71 age-matched controls were used to calculate annual change rates. Results showed that WM/GM contrast decay was larger in AD compared with controls in the medial temporal lobes. For the discrimination between AD and controls, entorhinal WM/GM contrast decay contributed significantly when included together with decrease in entorhinal cortical thickness and hippocampal volume, and increased the area under the curve to 0.79 compared with 0.75 when using the two morphometric variables only. Independent effects of WM/GM contrast decay and improved classification were also observed for the CDR-based subgroups, including participants converting from either a non-AD status to very mild AD, or from very mild to mild AD. Thus, WM/GM contrast decay increased diagnostic accuracy beyond what was obtained by well-validated morphometric measures alone. The findings suggest that signal intensity properties constitute a sensitive biomarker for cerebral degeneration in AD. PMID:22674625

  20. Tuning of Strouhal number for high propulsive efficiency accurately predicts how wingbeat frequency and stroke amplitude relate and scale with size and flight speed in birds.

    PubMed Central

    Nudds, Robert L.; Taylor, Graham K.; Thomas, Adrian L. R.

    2004-01-01

    The wing kinematics of birds vary systematically with body size, but we still, after several decades of research, lack a clear mechanistic understanding of the aerodynamic selection pressures that shape them. Swimming and flying animals have recently been shown to cruise at Strouhal numbers (St) corresponding to a regime of vortex growth and shedding in which the propulsive efficiency of flapping foils peaks (St approximately fA/U, where f is wingbeat frequency, U is cruising speed and A approximately bsin(theta/2) is stroke amplitude, in which b is wingspan and theta is stroke angle). We show that St is a simple and accurate predictor of wingbeat frequency in birds. The Strouhal numbers of cruising birds have converged on the lower end of the range 0.2 < St < 0.4 associated with high propulsive efficiency. Stroke angle scales as theta approximately 67b-0.24, so wingbeat frequency can be predicted as f approximately St.U/bsin(33.5b-0.24), with St0.21 and St0.25 for direct and intermittent fliers, respectively. This simple aerodynamic model predicts wingbeat frequency better than any other relationship proposed to date, explaining 90% of the observed variance in a sample of 60 bird species. Avian wing kinematics therefore appear to have been tuned by natural selection for high aerodynamic efficiency: physical and physiological constraints upon wing kinematics must be reconsidered in this light. PMID:15451698

  1. 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. PMID:27372244

  2. Improved predictive modeling of white LEDs with accurate luminescence simulation and practical inputs with TracePro opto-mechanical design software

    NASA Astrophysics Data System (ADS)

    Tsao, Chao-hsi; Freniere, Edward R.; Smith, Linda

    2009-02-01

    The use of white LEDs for solid-state lighting to address applications in the automotive, architectural and general illumination markets is just emerging. LEDs promise greater energy efficiency and lower maintenance costs. However, there is a significant amount of design and cost optimization to be done while companies continue to improve semiconductor manufacturing processes and begin to apply more efficient and better color rendering luminescent materials such as phosphor and quantum dot nanomaterials. In the last decade, accurate and predictive opto-mechanical software modeling has enabled adherence to performance, consistency, cost, and aesthetic criteria without the cost and time associated with iterative hardware prototyping. More sophisticated models that include simulation of optical phenomenon, such as luminescence, promise to yield designs that are more predictive - giving design engineers and materials scientists more control over the design process to quickly reach optimum performance, manufacturability, and cost criteria. A design case study is presented where first, a phosphor formulation and excitation source are optimized for a white light. The phosphor formulation, the excitation source and other LED components are optically and mechanically modeled and ray traced. Finally, its performance is analyzed. A blue LED source is characterized by its relative spectral power distribution and angular intensity distribution. YAG:Ce phosphor is characterized by relative absorption, excitation and emission spectra, quantum efficiency and bulk absorption coefficient. Bulk scatter properties are characterized by wavelength dependent scatter coefficients, anisotropy and bulk absorption coefficient.

  3. The VACS Index Accurately Predicts Mortality and Treatment Response among Multi-Drug Resistant HIV Infected Patients Participating in the Options in Management with Antiretrovirals (OPTIMA) Study

    PubMed Central

    Brown, Sheldon T.; Tate, Janet P.; Kyriakides, Tassos C.; Kirkwood, Katherine A.; Holodniy, Mark; Goulet, Joseph L.; Angus, Brian J.; Cameron, D. William; Justice, Amy C.

    2014-01-01

    Objectives The VACS Index is highly predictive of all-cause mortality among HIV infected individuals within the first few years of combination antiretroviral therapy (cART). However, its accuracy among highly treatment experienced individuals and its responsiveness to treatment interventions have yet to be evaluated. We compared the accuracy and responsiveness of the VACS Index with a Restricted Index of age and traditional HIV biomarkers among patients enrolled in the OPTIMA study. Methods Using data from 324/339 (96%) patients in OPTIMA, we evaluated associations between indices and mortality using Kaplan-Meier estimates, proportional hazards models, Harrel’s C-statistic and net reclassification improvement (NRI). We also determined the association between study interventions and risk scores over time, and change in score and mortality. Results Both the Restricted Index (c = 0.70) and VACS Index (c = 0.74) predicted mortality from baseline, but discrimination was improved with the VACS Index (NRI = 23%). Change in score from baseline to 48 weeks was more strongly associated with survival for the VACS Index than the Restricted Index with respective hazard ratios of 0.26 (95% CI 0.14–0.49) and 0.39(95% CI 0.22–0.70) among the 25% most improved scores, and 2.08 (95% CI 1.27–3.38) and 1.51 (95%CI 0.90–2.53) for the 25% least improved scores. Conclusions The VACS Index predicts all-cause mortality more accurately among multi-drug resistant, treatment experienced individuals and is more responsive to changes in risk associated with treatment intervention than an index restricted to age and HIV biomarkers. The VACS Index holds promise as an intermediate outcome for intervention research. PMID:24667813

  4. Change in ST segment elevation 60 minutes after thrombolytic initiation predicts clinical outcome as accurately as later electrocardiographic changes

    PubMed Central

    Purcell, I; Newall, N; Farrer, M

    1997-01-01

    Objective—To compare prospectively the prognostic accuracy of a 50% decrease in ST segment elevation on standard 12-lead electrocardiograms (ECGs) recorded at 60, 90, and 180 minutes after thrombolysis initiation in acute myocardial infarction.
Design—Consecutive sample prospective cohort study.
Setting—A single coronary care unit in the north of England.
Patients—190 consecutive patients receiving thrombolysis for first acute myocardial infarction.
Interventions—Thrombolysis at baseline.
Main outcome measures—Cardiac mortality and left ventricular size and function assessed 36 days later.
Results—Failure of ST segment elevation to resolve by 50% in the single lead of maximum ST elevation or the sum ST elevation of all infarct related ECG leads at each of the times studied was associated with a significantly higher mortality, larger left ventricular volume, and lower ejection fraction. There was some variation according to infarct site with only the 60 minute ECG predicting mortality after inferior myocardial infarction and only in anterior myocardial infarction was persistent ST elevation associated with worse left ventricular function. The analysis of the lead of maximum ST elevation at 60 minutes from thrombolysis performed as well as later ECGs in receiver operating characteristic curves for predicting clinical outcome.
Conclusion—The standard 12-lead ECG at 60 minutes predicts clinical outcome as accurately as later ECGs after thrombolysis for first acute myocardial infarction.

 Keywords: myocardial infarction;  thrombolysis;  ST segment elevation PMID:9415005

  5. Fecal Calprotectin is an Accurate Tool and Correlated to Seo Index in Prediction of Relapse in Iranian Patients With Ulcerative Colitis

    PubMed Central

    Hosseini, Seyed Vahid; Jafari, Peyman; Taghavi, Seyed Alireza; Safarpour, Ali Reza; Rezaianzadeh, Abbas; Moini, Maryam; Mehrabi, Manoosh

    2015-01-01

    . Besides, FC level of 341 μg/g was identified as the cut-off point with 11.2% and 79.7% relapse rate below and above this point, respectively. Additionally, Pearson correlation coefficient (r) between FC and the Seo index was significant in prediction of relapse (r = 0.63, P < 0.001). Conclusions: As a simple and noninvasive marker, FC is highly accurate and significantly correlated to the Seo activity index in prediction of relapse in the course of quiescent UC in Iranian patients. PMID:25793117

  6. Multiple sclerosis: microRNA expression profiles accurately differentiate patients with relapsing-remitting disease from healthy controls.

    PubMed

    Keller, Andreas; Leidinger, Petra; Lange, Julia; Borries, Anne; Schroers, Hannah; Scheffler, Matthias; Lenhof, Hans-Peter; Ruprecht, Klemens; Meese, Eckart

    2009-01-01

    Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system, which is heterogenous with respect to clinical manifestations and response to therapy. Identification of biomarkers appears desirable for an improved diagnosis of MS as well as for monitoring of disease activity and treatment response. MicroRNAs (miRNAs) are short non-coding RNAs, which have been shown to have the potential to serve as biomarkers for different human diseases, most notably cancer. Here, we analyzed the expression profiles of 866 human miRNAs. In detail, we investigated the miRNA expression in blood cells of 20 patients with relapsing-remitting MS (RRMS) and 19 healthy controls using a human miRNA microarray and the Geniom Real Time Analyzer (GRTA) platform. We identified 165 miRNAs that were significantly up- or downregulated in patients with RRMS as compared to healthy controls. The best single miRNA marker, hsa-miR-145, allowed discriminating MS from controls with a specificity of 89.5%, a sensitivity of 90.0%, and an accuracy of 89.7%. A set of 48 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 95%, a sensitivity of 97.6%, and an accuracy of 96.3%. While 43 of the 165 miRNAs deregulated in patients with MS have previously been related to other human diseases, the remaining 122 miRNAs are so far exclusively associated with MS. The implications of our study are twofold. The miRNA expression profiles in blood cells may serve as a biomarker for MS, and deregulation of miRNA expression may play a role in the pathogenesis of MS. PMID:19823682

  7. Multiple Sclerosis: MicroRNA Expression Profiles Accurately Differentiate Patients with Relapsing-Remitting Disease from Healthy Controls

    PubMed Central

    Lange, Julia; Borries, Anne; Schroers, Hannah; Scheffler, Matthias; Lenhof, Hans-Peter; Ruprecht, Klemens; Meese, Eckart

    2009-01-01

    Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system, which is heterogenous with respect to clinical manifestations and response to therapy. Identification of biomarkers appears desirable for an improved diagnosis of MS as well as for monitoring of disease activity and treatment response. MicroRNAs (miRNAs) are short non-coding RNAs, which have been shown to have the potential to serve as biomarkers for different human diseases, most notably cancer. Here, we analyzed the expression profiles of 866 human miRNAs. In detail, we investigated the miRNA expression in blood cells of 20 patients with relapsing-remitting MS (RRMS) and 19 healthy controls using a human miRNA microarray and the Geniom Real Time Analyzer (GRTA) platform. We identified 165 miRNAs that were significantly up- or downregulated in patients with RRMS as compared to healthy controls. The best single miRNA marker, hsa-miR-145, allowed discriminating MS from controls with a specificity of 89.5%, a sensitivity of 90.0%, and an accuracy of 89.7%. A set of 48 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 95%, a sensitivity of 97.6%, and an accuracy of 96.3%. While 43 of the 165 miRNAs deregulated in patients with MS have previously been related to other human diseases, the remaining 122 miRNAs are so far exclusively associated with MS. The implications of our study are twofold. The miRNA expression profiles in blood cells may serve as a biomarker for MS, and deregulation of miRNA expression may play a role in the pathogenesis of MS. PMID:19823682

  8. Simulated surgical-type cerebral biopsies from post-mortem brains allows accurate neuropathological diagnoses in the majority of neurodegenerative disease groups

    PubMed Central

    2013-01-01

    Background In theory, cerebral biopsies could provide the diagnosis in a significant proportion of patients with neurodegenerative diseases, however, there are considerable ethical barriers. Previous series of cerebral biopsies have shown variable diagnostic accuracy but have understandably suffered because of lack of post-mortem tissue with which to compare the diagnosis. To determine the accuracy of such biopsies in neurodegenerative disease we took small biopsy-sized samples of predominantly fresh post-mortem brain tissue from frontal and temporal lobes in 62 cases. These were processed as for a biopsy and stained for H&E, p62, tau, Aβ, α-synuclein, and TDP-43. The sections were assessed blind by 3 neuropathologists and the results compared with the final post-mortem diagnosis. Results The agreement and sensitivity in most cases was good especially: controls; Alzheimer’s disease (AD); multiple system atrophy (MSA); frontotemporal lobar degeneration with TDP-43 positive inclusions and/or motor neurone disease (FTLD-TDP/MND); Huntington’s disease (HD); corticobasal degeneration (CBD) / microtubular associated protein tau mutation cases with CBD-like features (CBD/MAPT); and combined AD- Dementia with Lewy Bodies (AD-DLB) where the sensitivity on assessing both brain regions varied between 75-100%. There was poor sensitivity for progressive supranuclear palsy (PSP) and amyotrophic lateral sclerosis (ALS) (both 0%), but moderate sensitivity for pure DLB (60%). The temporal lobe assessment was marginally more accurate than the frontal lobe but these were only slightly worse than both combined. Conclusions The study shows that with certain caveats the cerebral biopsy in life should be a viable method of accurately diagnosing many neurodegenerative diseases. PMID:24252649

  9. Predicting relapse following medical therapy for Graves' disease

    SciTech Connect

    McKillop, J.H.; Wilson, R.; Pearson, D.W.; Cuthbert, G.F.; Jenkins, C.; Caine, S.; Thomson, J.A.

    1984-01-01

    In 40 patients with Graves' disease (35 female, 5 male; mean age at presentation = 38 yrs) the authors examined the ability of thyroidal /sup 99m/Tc uptake and serum thyroid stimulating immunoglobins (TSI) to identify patients who would relapse after a course of medical therapy. Serum TSI and 20 minute thyroidal /sup 99m/Tc uptake were estimated every 3 months during a 12 month course of carbimazole and tri iodothyronine. TSI levels were estimated by inhibition of receptor binding and expressed as an index (normal value <25). 17 patients (Group 1) remained biochemically euthyroid for at least 1 year after cessation of therapy. 23 (Group II) developed recurrent thyrotoxicosis. Thyroid hormone level did not differ between Groups I and II at presentation. /sup 99m/Tc uptake did not differ significantly in the two groups at presentation and overlap of values persisted throughout therapy. 3 patients had undetectable TSI levels at presentation and throughout follow-up. In the remaining 37, TSI levels at presentation were significantly higher in Group II and all 7 patients with initial values >80 relapsed. After 12 months therapy a TSI level of >25 was present in 1 Group I patient and 16 Group II patients who had detectable TSI at presentation. /sup 99m/Tc uptake was a poor predictor of relapse of thyrotoxicosis. A very high TSI level at presentation (>80) was associated with relapse. An abnormal TSI on completion of 12 months medical therapy had a sensitivity of 86% and a specificity of 94% for prediction of relapse of thyrotoxicosis in the subsequent year.

  10. Raman spectroscopy detection of platelet for Alzheimer’s disease with predictive probabilities

    NASA Astrophysics Data System (ADS)

    Wang, L. J.; Du, X. Q.; Du, Z. W.; Yang, Y. Y.; Chen, P.; Tian, Q.; Shang, X. L.; Liu, Z. C.; Yao, X. Q.; Wang, J. Z.; Wang, X. H.; Cheng, Y.; Peng, J.; Shen, A. G.; Hu, J. M.

    2014-08-01

    Alzheimer’s disease (AD) is a common form of dementia. Early and differential diagnosis of AD has always been an arduous task for the medical expert due to the unapparent early symptoms and the currently imperfect imaging examination methods. Therefore, obtaining reliable markers with clinical diagnostic value in easily assembled samples is worthy and significant. Our previous work with laser Raman spectroscopy (LRS), in which we detected platelet samples of different ages of AD transgenic mice and non-transgenic controls, showed great effect in the diagnosis of AD. In addition, a multilayer perception network (MLP) classification method was adopted to discriminate the spectral data. However, there were disturbances, which were induced by noise from the machines and so on, in the data set; thus the MLP method had to be trained with large-scale data. In this paper, we aim to re-establish the classification models of early and advanced AD and the control group with fewer features, and apply some mechanism of noise reduction to improve the accuracy of models. An adaptive classification method based on the Gaussian process (GP) featured, with predictive probabilities, is proposed, which could tell when a data set is related to some kind of disease. Compared with MLP on the same feature set, GP showed much better performance in the experimental results. What is more, since the spectra of platelets are isolated from AD, GP has good expansibility and can be applied in diagnosis of many other similar diseases, such as Parkinson’s disease (PD). Spectral data of 4 month and 12 month AD platelets, as well as control data, were collected. With predictive probabilities, the proposed GP classification method improved the diagnostic sensitivity to nearly 100%. Samples were also collected from PD platelets as classification and comparison to the 12 month AD. The presented approach and our experiments indicate that utilization of GP with predictive probabilities in

  11. POSTERIOR PREDICTIVE MODEL CHECKS FOR DISEASE MAPPING MODELS. (R827257)

    EPA Science Inventory

    Disease incidence or disease mortality rates for small areas are often displayed on maps. Maps of raw rates, disease counts divided by the total population at risk, have been criticized as unreliable due to non-constant variance associated with heterogeneity in base population si...

  12. Accurate discrimination of Alzheimer's disease from other dementia and/or normal subjects using SPECT specific volume analysis

    NASA Astrophysics Data System (ADS)

    Iyatomi, Hitoshi; Hashimoto, Jun; Yoshii, Fumuhito; Kazama, Toshiki; Kawada, Shuichi; Imai, Yutaka

    2014-03-01

    Discrimination between Alzheimer's disease and other dementia is clinically significant, however it is often difficult. In this study, we developed classification models among Alzheimer's disease (AD), other dementia (OD) and/or normal subjects (NC) using patient factors and indices obtained by brain perfusion SPECT. SPECT is commonly used to assess cerebral blood flow (CBF) and allows the evaluation of the severity of hypoperfusion by introducing statistical parametric mapping (SPM). We investigated a total of 150 cases (50 cases each for AD, OD, and NC) from Tokai University Hospital, Japan. In each case, we obtained a total of 127 candidate parameters from: (A) 2 patient factors (age and sex), (B) 12 CBF parameters and 113 SPM parameters including (C) 3 from specific volume analysis (SVA), and (D) 110 from voxel-based analysis stereotactic extraction estimation (vbSEE). We built linear classifiers with a statistical stepwise feature selection and evaluated the performance with the leave-one-out cross validation strategy. Our classifiers achieved very high classification performances with reasonable number of selected parameters. In the most significant discrimination in clinical, namely those of AD from OD, our classifier achieved both sensitivity (SE) and specificity (SP) of 96%. In a similar way, our classifiers achieved a SE of 90% and a SP of 98% in AD from NC, as well as a SE of 88% and a SP of 86% in AD from OD and NC cases. Introducing SPM indices such as SVA and vbSEE, classification performances improved around 7-15%. We confirmed that these SPM factors are quite important for diagnosing Alzheimer's disease.

  13. Calibrating transition-metal energy levels and oxygen bands in first-principles calculations: Accurate prediction of redox potentials and charge transfer in lithium transition-metal oxides

    NASA Astrophysics Data System (ADS)

    Seo, Dong-Hwa; Urban, Alexander; Ceder, Gerbrand

    2015-09-01

    Transition-metal (TM) oxides play an increasingly important role in technology today, including applications such as catalysis, solar energy harvesting, and energy storage. In many of these applications, the details of their electronic structure near the Fermi level are critically important for their properties. We propose a first-principles-based computational methodology for the accurate prediction of oxygen charge transfer in TM oxides and lithium TM (Li-TM) oxides. To obtain accurate electronic structures, the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional is adopted, and the amount of exact Hartree-Fock exchange (mixing parameter) is adjusted to reproduce reference band gaps. We show that the HSE06 functional with optimal mixing parameter yields not only improved electronic densities of states, but also better energetics (Li-intercalation voltages) for LiCo O2 and LiNi O2 as compared to the generalized gradient approximation (GGA), Hubbard U corrected GGA (GGA +U ), and standard HSE06. We find that the optimal mixing parameters for TM oxides are system specific and correlate with the covalency (ionicity) of the TM species. The strong covalent (ionic) nature of TM-O bonding leads to lower (higher) optimal mixing parameters. We find that optimized HSE06 functionals predict stronger hybridization of the Co 3 d and O 2 p orbitals as compared to GGA, resulting in a greater contribution from oxygen states to charge compensation upon delithiation in LiCo O2 . We also find that the band gaps of Li-TM oxides increase linearly with the mixing parameter, enabling the straightforward determination of optimal mixing parameters based on GGA (α =0.0 ) and HSE06 (α =0.25 ) calculations. Our results also show that G0W0@GGA +U band gaps of TM oxides (M O ,M =Mn ,Co ,Ni ) and LiCo O2 agree well with experimental references, suggesting that G0W0 calculations can be used as a reference for the calibration of the mixing parameter in cases when no experimental band gap has been

  14. Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology.

    PubMed

    Buil-Bruna, Núria; Sahota, Tarjinder; López-Picazo, José-María; Moreno-Jiménez, Marta; Martín-Algarra, Salvador; Ribba, Benjamin; Trocóniz, Iñaki F

    2015-06-15

    Predictive biomarkers can play a key role in individualized disease monitoring. Unfortunately, the use of biomarkers in clinical settings has thus far been limited. We have previously shown that mechanism-based pharmacokinetic/pharmacodynamic modeling enables integration of nonvalidated biomarker data to provide predictive model-based biomarkers for response classification. The biomarker model we developed incorporates an underlying latent variable (disease) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment. Here, we show that by integrating CT scan data, the population model can be expanded to include patient outcome. Moreover, we show that in conjunction with routine medical monitoring data, the population model can support accurate individual predictions of outcome. Our combined model predicts that a change in disease of 29.2% (relative standard error 20%) between two consecutive CT scans (i.e., 6-8 weeks) gives a probability of disease progression of 50%. We apply this framework to an external dataset containing biomarker data from 22 small cell lung cancer patients (four patients progressing during follow-up). Using only data up until the end of treatment (a total of 137 lactate dehydrogenase and 77 neuron-specific enolase observations), the statistical framework prospectively identified 75% of the individuals as having a predictable outcome in follow-up visits. This included two of the four patients who eventually progressed. In all identified individuals, the model-predicted outcomes matched the observed outcomes. This framework allows at risk patients to be identified early and therapeutic intervention/monitoring to be adjusted individually, which may improve overall patient survival. PMID:25939602

  15. Toward Relatively General and Accurate Quantum Chemical Predictions of Solid-State 17O NMR Chemical Shifts in Various Biologically Relevant Oxygen-containing Compounds

    PubMed Central

    Rorick, Amber; Michael, Matthew A.; Yang, Liu; Zhang, Yong

    2015-01-01

    Oxygen is an important element in most biologically significant molecules and experimental solid-state 17O NMR studies have provided numerous useful structural probes to study these systems. However, computational predictions of solid-state 17O NMR chemical shift tensor properties are still challenging in many cases and in particular each of the prior computational work is basically limited to one type of oxygen-containing systems. This work provides the first systematic study of the effects of geometry refinement, method and basis sets for metal and non-metal elements in both geometry optimization and NMR property calculations of some biologically relevant oxygen-containing compounds with a good variety of XO bonding groups, X= H, C, N, P, and metal. The experimental range studied is of 1455 ppm, a major part of the reported 17O NMR chemical shifts in organic and organometallic compounds. A number of computational factors towards relatively general and accurate predictions of 17O NMR chemical shifts were studied to provide helpful and detailed suggestions for future work. For the studied various kinds of oxygen-containing compounds, the best computational approach results in a theory-versus-experiment correlation coefficient R2 of 0.9880 and mean absolute deviation of 13 ppm (1.9% of the experimental range) for isotropic NMR shifts and R2 of 0.9926 for all shift tensor properties. These results shall facilitate future computational studies of 17O NMR chemical shifts in many biologically relevant systems, and the high accuracy may also help refinement and determination of active-site structures of some oxygen-containing substrate bound proteins. PMID:26274812

  16. Deep vein thrombosis is accurately predicted by comprehensive analysis of the levels of microRNA-96 and plasma D-dimer

    PubMed Central

    Xie, Xuesheng; Liu, Changpeng; Lin, Wei; Zhan, Baoming; Dong, Changjun; Song, Zhen; Wang, Shilei; Qi, Yingguo; Wang, Jiali; Gu, Zengquan

    2016-01-01

    The aim of the present study was to investigate the association between platelet microRNA-96 (miR-96) expression levels and the occurrence of deep vein thrombosis (DVT) in orthopedic patients. A total of consecutive 69 orthopedic patients with DVT and 30 healthy individuals were enrolled. Ultrasonic color Doppler imaging was performed on lower limb veins after orthopedic surgery to determine the occurrence of DVT. An enzyme-linked fluorescent assay was performed to detect the levels of D-dimer in plasma. A quantitative polymerase chain reaction assay was performed to determine the expression levels of miR-96. Expression levels of platelet miR-96 were significantly increased in orthopedic patients after orthopedic surgery. miR-96 expression levels in orthopedic patients with DVT at days 1, 3 and 7 after orthopedic surgery were significantly increased when compared with those in the control group. The increased miR-96 expression levels were correlated with plasma D-dimer levels in orthopedic patients with DVT. However, for the orthopedic patients in the non-DVT group following surgery, miR-96 expression levels were correlated with plasma D-dimer levels. In summary, the present results suggest that the expression levels of miR-96 may be associated with the occurrence of DVT. The occurrence of DVT may be accurately predicted by comprehensive analysis of the levels of miR-96 and plasma D-dimer. PMID:27588107

  17. [The crystal ball: reflections on the prediction of disease].

    PubMed

    Smulders, Yvo

    2015-01-01

    How exactly to take the family history so that it renders maximum diagnostic or predictive yield is controversial. This comment discusses a paper that suggests that asking for the presence of risk in children of patients may have added value in terms of predicting first or recurrent cardiovascular events in their parents. PMID:26629604

  18. Predicting chronic obstructive pulmonary disease hospitalizations based on concurrent influenza activity

    PubMed Central

    Gerke, Alicia K.; Tang, Fan; Yang, Ming; Foster, Eric D.; Cavanaugh, Joseph E.; Polgreen, Philip M.

    2014-01-01

    Although influenza has been associated with chronic obstructive pulmonary disease (COPD) exacerbations, it is not clear the extent to which this association affects healthcare use in the United States. The first goal of this project was to determine to what extent the incidence of COPD hospitalizations is associated with seasonal influenza. Second, as a natural experiment, we used influenza activity to help predict COPD admissions during the 2009 H1N1 influenza pandemic. To do this, we identified all hospitalizations between 1998 and 2010 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project (HCUP) during which a primary diagnosis of COPD was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We formulated time series regression models to investigate the association of monthly COPD admissions with influenza incidence. Finally, we applied these models, fit using 1998–2008 data, to forecast monthly COPD admissions during the 2009 pandemic. Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of COPD hospitalizations (p-value<0.0001). The association is especially strong among older patients requiring mechanical ventilation. Use of influenza data to predict COPD admissions during the 2009 H1N1 pandemic reduced the mean-squared prediction error by 29.9%. We conclude that influenza activity is significantly associated with COPD hospitalizations in the United States and influenza activity can be exploited to more accurately forecast COPD admissions. Our results suggest that improvements in influenza surveillance, prevention, and treatment may decrease hospitalizations of patients diagnosed with COPD. PMID:23819753

  19. The Positive Predictive Value of Lyme Elisa for the Diagnosis of Lyme Disease in Children.

    PubMed

    Lipsett, Susan C; Pollock, Nira R; Branda, John A; Gordon, Caroline D; Gordon, Catherine R; Lantos, Paul M; Nigrovic, Lise E

    2015-11-01

    By using a Lyme enzyme-linked immunosorbent assay (ELISA), we demonstrated that high ELISA index values are strongly predictive of Lyme disease. In children with clinical presentations consistent with Lyme disease, ELISA index values ≥3.0 had a positive predictive value of 99.4% (95% confidence interval: 98.1-99.8%) for Lyme disease, making a supplemental Western immunoblot potentially unnecessary. PMID:26222064

  20. WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

    PubMed

    Chen, Xing; Yan, Chenggang Clarence; Zhang, Xu; You, Zhu-Hong; Deng, Lixi; Liu, Ying; Zhang, Yongdong; Dai, Qionghai

    2016-01-01

    Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms, and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84%, and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification. PMID:26880032

  1. Biodiversity decreases disease through predictable changes in host community competence.

    PubMed

    Johnson, Pieter T J; Preston, Daniel L; Hoverman, Jason T; Richgels, Katherine L D

    2013-02-14

    Accelerating rates of species extinctions and disease emergence underscore the importance of understanding how changes in biodiversity affect disease outcomes. Over the past decade, a growing number of studies have reported negative correlations between host biodiversity and disease risk, prompting suggestions that biodiversity conservation could promote human and wildlife health. Yet the generality of the diversity-disease linkage remains conjectural, in part because empirical evidence of a relationship between host competence (the ability to maintain and transmit infections) and the order in which communities assemble has proven elusive. Here we integrate high-resolution field data with multi-scale experiments to show that host diversity inhibits transmission of the virulent pathogen Ribeiroia ondatrae and reduces amphibian disease as a result of consistent linkages among species richness, host composition and community competence. Surveys of 345 wetlands indicated that community composition changed nonrandomly with species richness, such that highly competent hosts dominated in species-poor assemblages whereas more resistant species became progressively more common in diverse assemblages. As a result, amphibian species richness strongly moderated pathogen transmission and disease pathology among 24,215 examined hosts, with a 78.4% decline in realized transmission in richer assemblages. Laboratory and mesocosm manipulations revealed an approximately 50% decrease in pathogen transmission and host pathology across a realistic diversity gradient while controlling for host density, helping to establish mechanisms underlying the diversity-disease relationship and their consequences for host fitness. By revealing a consistent link between species richness and community competence, these findings highlight the influence of biodiversity on infection risk and emphasize the benefit of a community-based approach to understanding infectious diseases. PMID:23407539

  2. Patient-specific coronary stenoses can be modeled using a combination of OCT and flow velocities to accurately predict hyperemic pressure gradients.

    PubMed

    Kousera, C A; Nijjer, S; Torii, R; Petraco, R; Sen, S; Foin, N; Hughes, A D; Francis, D P P; Xu, X Y; Davies, J E

    2014-06-01

    Computational fluid dynamics (CFD) is increasingly being developed for the diagnostics of arterial diseases. Imaging methods such as computed tomography (CT) and angiography are commonly used. However, these have limited spatial resolution and are subject to movement artifact. This study developed a new approach to generate CFD models by combining high-fidelity, patient-specific coronary anatomy models derived from optical coherence tomography (OCT) imaging with patient-specific pressure and velocity phasic data. Additionally, we used a new technique which does not require the catheter to be used to determine the centerline of the vessel. The CFD data were then compared with invasively measured pressure and velocity. Angiography imaging data of 21 vessels collected from 19 patients were fused with OCT visualizations of the same vessels using an algorithm that produces reconstructions inheriting the in-plane (10 μm) and longitudinal (0.2 mm) resolution of OCT. Proximal pressure and distal velocity waveforms ensemble averaged from invasively measured data were used as inlet and outlet boundary conditions, respectively, in CFD simulations. The resulting distal pressure waveform was compared against the measured waveform to test the model. The results followed the shape of the measured waveforms closely (cross-correlation coefficient = 0.898 ± 0.005, ), indicating realistic modeling of flow resistance, the mean of differences between measured and simulated results was -3. 5 mmHg, standard deviation of differences (SDD) = 8.2 mmHg over the cycle and -9.8 mmHg, SDD = 16.4 mmHg at peak flow. Models incorporating phasic velocity in patient-specific models of coronary anatomy derived from high-resolution OCT images show a good correlation with the measured pressure waveforms in all cases, indicating that the model results may be an accurate representation of the measured flow conditions. PMID:24845301

  3. Soluble DNAM-1, as a Predictive Biomarker for Acute Graft-Versus-Host Disease.

    PubMed

    Kanaya, Minoru; Shibuya, Kazuko; Hirochika, Rei; Kanemoto, Miyoko; Ohashi, Kazuteru; Okada, Masafumi; Wagatsuma, Yukiko; Cho, Yukiko; Kojima, Hiroshi; Teshima, Takanori; Imamura, Masahiro; Sakamaki, Hisashi; Shibuya, Akira

    2016-01-01

    Acute graft-versus-host disease (aGVHD) is a major complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Because diagnosis of aGVHD is exclusively based on clinical symptoms and pathological findings, reliable and noninvasive laboratory tests for accurate diagnosis are required. An activating immunoreceptor, DNAM-1 (CD226), is expressed on T cells and natural killer cells and is involved in the development of aGVHD. Here, we identified a soluble form of DNAM-1 (sDNAM-1) in human sera. In retrospective univariate and multivariate analyses of allo-HSCT patients (n = 71) at a single center, cumulative incidences of all grade (grade I-IV) and sgrade II-IV aGVHD in patients with high maximal serum levels of sDNAM-1 (≥30 pM) in the 7 days before allo-HSCT were significantly higher than those in patients with low maximal serum levels of sDNAM-1 (<30 pM) in the same period. However, sDNAM-1 was not associated with other known allo-HSCT complications. Our data suggest that sDNAM-1 is potentially a unique candidate as a predictive biomarker for the development of aGVHD. PMID:27257974

  4. Soluble DNAM-1, as a Predictive Biomarker for Acute Graft-Versus-Host Disease

    PubMed Central

    Kanaya, Minoru; Shibuya, Kazuko; Hirochika, Rei; Kanemoto, Miyoko; Ohashi, Kazuteru; Okada, Masafumi; Wagatsuma, Yukiko; Cho, Yukiko; Kojima, Hiroshi; Teshima, Takanori; Imamura, Masahiro; Sakamaki, Hisashi; Shibuya, Akira

    2016-01-01

    Acute graft-versus-host disease (aGVHD) is a major complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Because diagnosis of aGVHD is exclusively based on clinical symptoms and pathological findings, reliable and noninvasive laboratory tests for accurate diagnosis are required. An activating immunoreceptor, DNAM-1 (CD226), is expressed on T cells and natural killer cells and is involved in the development of aGVHD. Here, we identified a soluble form of DNAM-1 (sDNAM-1) in human sera. In retrospective univariate and multivariate analyses of allo-HSCT patients (n = 71) at a single center, cumulative incidences of all grade (grade I–IV) and sgrade II–IV aGVHD in patients with high maximal serum levels of sDNAM-1 (≥30 pM) in the 7 days before allo-HSCT were significantly higher than those in patients with low maximal serum levels of sDNAM-1 (<30 pM) in the same period. However, sDNAM-1 was not associated with other known allo-HSCT complications. Our data suggest that sDNAM-1 is potentially a unique candidate as a predictive biomarker for the development of aGVHD. PMID:27257974

  5. BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER’S DISEASE*

    PubMed Central

    Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G

    2015-01-01

    The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM. PMID:26900412

  6. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network

    PubMed Central

    Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

    2016-01-01

    Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

  7. Detection and prediction of post harvest carrot diseases

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Carrot is the most important outdoor vegetable crop in Norway with a market value of NOK 675 million in 2010. Because demand for carrots is year-round but the growing season is short, the crop is typically stored after harvest for as long as 6 months. Diseases that develop during low temperature sto...

  8. Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine

    PubMed Central

    Watanabe, Takanori; Kessler, Daniel; Scott, Clayton; Angstadt, Michael; Sripada, Chandra

    2014-01-01

    Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from whole-brain resting state functional connectomes. However, functional connectomes reside in a high dimensional space, which complicates model interpretation and introduces numerous statistical and computational challenges. Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes. We propose a regularization framework where the 6-D structure of the functional connectome (defined by pairs of points in 3-D space) is explicitly taken into account via the fused Lasso or the GraphNet regularizer. Our method only restricts the loss function to be convex and margin-based, allowing non-differentiable loss functions such as the hinge-loss to be used. Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection. We introduce a novel efficient optimization algorithm based on the augmented Lagrangian and the classical alternating direction method, which can solve both fused Lasso and GraphNet regularized SVM with very little modification. We also demonstrate that the inner subproblems of the algorithm can be solved efficiently in analytic form by coupling the variable splitting strategy with a data augmentation scheme. Experiments on simulated data and resting state scans from a large schizophrenia dataset show that our proposed approach can identify predictive regions that are spatially contiguous in the 6-D “connectome space,” offering an additional layer of interpretability that could provide new insights about various disease processes. PMID:24704268

  9. The predictive power of depression screening procedures for veterans with coronary artery disease

    PubMed Central

    Shankman, Stewart A; Nadelson, Jeffrey; McGowan, Sarah Kate; Sovari, Ali A; Vidovich, Mladen I

    2012-01-01

    Depression leads to a worse outcome for patients with coronary artery disease (CAD). Thus, accurately identifying depression in CAD patients is imperative. In many veterans affairs (VA) hospitals, patients are screened for depression once a year using the patient health questionnaire (PHQ-9). Although the PHQ-9 is generally considered a specific and sensitive measure of depression, there is reason to believe that these screening procedures may miss a large number of cases of depression within CAD patients and cardiology patients more generally. The goal of this study was to provide data as to the predictive power of this depression screening procedure by (a) comparing the prevalence rate of depression identified by the PHQ-9 to known prevalence rates and (b) examining whether patients identified as “depressed” also had conditions that consistently co-occur with depression (eg, post-traumatic stress disorder [PTSD], other medical issues). Participants were 813 consecutive patients who received an angiogram in the cardiac catheterization laboratory at a large VA Medical Center. Prevalence of depression was 6.9% in the overall sample and less than 6% when the sample was restricted to CAD patients with significant stenosis. Depression was significantly associated with PTSD, smoking, and alcohol problems. However, depression was not associated with other medical problems such as diabetes, renal failure, peripheral vascular disease, or anemia. In conclusion, the low prevalence rate of depression and lack of associations with comorbid medical problems may suggest that the VA’s depression screening procedures have low sensitivity for identifying depression in CAD patients. It is recommended that clinicians treating CAD regularly screen for depression and do not rely on archival depression screens. PMID:22566744

  10. A rapid method of accurate detection and differentiation of Newcastle disease virus pathotypes by demonstrating multiple bands in degenerate primer based nested RT-PCR.

    PubMed

    Desingu, P A; Singh, S D; Dhama, K; Kumar, O R Vinodh; Singh, R; Singh, R K

    2015-02-01

    A rapid and accurate method of detection and differentiation of virulent and avirulent Newcastle disease virus (NDV) pathotypes was developed. The NDV detection was carried out for different domestic avian field isolates and pigeon paramyxo virus-1 (25 field isolates and 9 vaccine strains) by using APMV-I "fusion" (F) gene Class II specific external primer A and B (535bp), internal primer C and D (238bp) based reverses transcriptase PCR (RT-PCR). The internal degenerative reverse primer D is specific for F gene cleavage position of virulent strain of NDV. The nested RT-PCR products of avirulent strains showed two bands (535bp and 424bp) while virulent strains showed four bands (535bp, 424bp, 349bp and 238bp) on agar gel electrophoresis. This is the first report regarding development and use of degenerate primer based nested RT-PCR for accurate detection and differentiation of NDV pathotypes by demonstrating multiple PCR band patterns. Being a rapid, simple, and economical test, the developed method could serve as a valuable alternate diagnostic tool for characterizing NDV isolates and carrying out molecular epidemiological surveillance studies for this important pathogen of poultry. PMID:25449112

  11. Prognosis Can Be Predicted More Accurately Using Pre- and Postchemoradiotherapy Carcinoembryonic Antigen Levels Compared to Only Prechemoradiotherapy Carcinoembryonic Antigen Level in Locally Advanced Rectal Cancer Patients Who Received Neoadjuvant Chemoradiotherapy

    PubMed Central

    Sung, SooYoon; Son, Seok Hyun; Kay, Chul Seung; Lee, Yoon Suk

    2016-01-01

    Abstract We aimed to evaluate the prognostic value of a change in the carcinoembryonic antigen (CEA) level during neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer. A total of 110 patients with clinical T3/T4 or node-positive disease underwent nCRT and curative total mesorectal resection from February 2006 to December 2013. Serum CEA level was measured before nCRT, after nCRT, and then again after surgery. A cut-off value for CEA level to predict prognosis was determined using the maximally selected log-rank test. According to the test, patients were classified into 3 groups, based on their CEA levels (Group A: pre-CRT CEA ≤3.2; Group B: pre-CRT CEA level >3.2 and post-CRT CEA ≤2.8; and Group C: pre-CRT CEA >3.2 and post-CRT CEA >2.8). The median follow-up time was 31.1 months. The 3-year disease-free survival (DFS) rates of Group A and Group B were similar, while Group C showed a significantly lower 3-year DFS rate (82.5% vs. 89.5% vs. 55.1%, respectively, P = 0.001). Other clinicopathological factors that showed statistical significance on univariate analysis were pre-CRT CEA, post-CRT CEA, tumor distance from the anal verge, surgery type, downstage, pathologic N stage, margin status and perineural invasion. The CEA group (P = 0.001) and tumor distance from the anal verge (P = 0.044) were significant prognostic factors for DFS on multivariate analysis. Post-CRT CEA level may be a useful prognostic factor in patients whose prognosis cannot be predicted exactly by pre-CRT CEA levels alone in the neoadjuvant treatment era. Combined pre-CRT CEA and post-CRT CEA levels enable us to predict prognosis more accurately and determine treatment and follow-up policies. Further large-scale studies are necessary to validate the prognostic value of CEA levels. PMID:26962798

  12. The influence of disease categories on gene candidate predictions from model organism phenotypes

    PubMed Central

    2014-01-01

    Background The molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have recently developed Exomiser as a tool for identifying causative variants from exome analysis results by filtering and prioritising using a number of criteria including the phenotype similarity between the disease and mouse mutants involving the gene candidates. Initial investigations revealed a variation in performance for different medical categories of disease, due in part to a varying contribution of the phenotype scoring component. Results In this study, we further analyse the performance of our cross-species phenotype matching algorithm, and examine in more detail the reasons why disease gene filtering based on phenotype data works better for certain disease categories than others. We found that in addition to misleading phenotype alignments between species, some disease categories are still more amenable to automated predictions than others, and that this often ties in with community perceptions on how well the organism works as model. Conclusions In conclusion, our automated disease gene candidate predictions are highly dependent on the organism used for the predictions and the disease category being studied. Future work on computational disease gene prediction using phenotype data would benefit from methods that take into account the disease category and the source of model organism data. PMID:25093073

  13. Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis

    PubMed Central

    Ranganath, Rajesh; Hirsch, Jamie S; Blei, David; Elhadad, Noémie

    2015-01-01

    Background As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. Objective The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. Methods The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. Results A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). Conclusions A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration. PMID:25896647

  14. RandomForest4Life: a Random Forest for predicting ALS disease progression.

    PubMed

    Hothorn, Torsten; Jung, Hans H

    2014-09-01

    We describe a method for predicting disease progression in amyotrophic lateral sclerosis (ALS) patients. The method was developed as a submission to the DREAM Phil Bowen ALS Prediction Prize4Life Challenge of summer 2012. Based on repeated patient examinations over a three- month period, we used a random forest algorithm to predict future disease progression. The procedure was set up and internally evaluated using data from 1197 ALS patients. External validation by an expert jury was based on undisclosed information of an additional 625 patients; all patient data were obtained from the PRO-ACT database. In terms of prediction accuracy, the approach described here ranked third best. Our interpretation of the prediction model confirmed previous reports suggesting that past disease progression is a strong predictor of future disease progression measured on the ALS functional rating scale (ALSFRS). We also found that larger variability in initial ALSFRS scores is linked to faster future disease progression. The results reported here furthermore suggested that approaches taking the multidimensionality of the ALSFRS into account promise some potential for improved ALS disease prediction. PMID:25141076

  15. Predicting and controlling infectious disease epidemics using temporal networks

    PubMed Central

    Holme, Petter

    2013-01-01

    Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments. PMID:23513178

  16. Predicting cardiovascular disease from handgrip strength: the potential clinical implications.

    PubMed

    Leong, Darryl P; Teo, Koon K

    2015-12-01

    The measurement of handgrip strength has proven prognostic value for all-cause and cardiovascular death, and for cardiovascular disease. It is also an important indicator of frailty and vulnerability. The measurement of handgrip strength may be most useful in the context of multi-morbidity, where it may be a simple tool to identify the individual at particularly high risk of adverse outcomes, who may benefit from closer clinical attention. Research into dietary, exercise, and pharmacologic strategies to increase muscle strength is ongoing. Important issues will be the feasibility and sustainability of increases in muscle strength, and whether these increases translate into clinical benefit. PMID:26513210

  17. Iron requirements based upon iron absorption tests are poorly predicted by haematological indices in patients with inactive inflammatory bowel disease.

    PubMed

    Lomer, Miranda C E; Cook, William B; Jan-Mohamed, Hamid Jan B; Hutchinson, Carol; Liu, Ding Yong; Hider, Robert C; Powell, Jonathan J

    2012-06-01

    Fe deficiency and Fe-deficiency anaemia are common in patients with inflammatory bowel disease (IBD). Traditional clinical markers of Fe status can be skewed in the presence of inflammation, meaning that a patient's Fe status can be misinterpreted. Additionally, Fe absorption is known to be down-regulated in patients with active IBD. However, whether this is the case for quiescent or mildly active disease has not been formally assessed. The present study aimed to investigate the relationship between Fe absorption, Fe requirements and standard haematological indices in IBD patients without active disease. A group of twenty-nine patients with quiescent or mildly active IBD and twenty-eight control subjects undertook an Fe absorption test that measured sequential rises in serum Fe over 4 h following ingestion of 200 mg ferrous sulphate. At baseline, serum Fe, transferrin saturation, non-transferrin-bound Fe (NTBI), ferritin and soluble transferrin receptor were all measured. Thereafter (30-240 min), only serum Fe and NTBI were measured. Fe absorption did not differ between the two groups (P = 0·9; repeated-measures ANOVA). In control subjects, baseline haematological parameters predicted Fe absorption (i.e. Fe requirements), but this was not the case for patients with IBD. Fe absorption is normal in quiescent or mildly active IBD patients but standard haematological parameters do not accurately predict Fe requirements. PMID:22152498

  18. Ischemic placental syndrome - prediction and new disease monitoring.

    PubMed

    Kwiatkowski, Sebastian; Kwiatkowska, Ewa; Rzepka, Rafał; Torbe, Andrzej; Dolegowska, Barbara

    2016-06-01

    The last decade has seen an improved understanding of the cause of the development of pathologies such as gestational hypertension, preeclampsia, intrauterine growth restriction, intrauterine fetal death or placental abruption. Nowadays, we know that most conditions within this group share the same pathogenesis, the cause of which is placental ischemia. The following review is an attempt to propose a new method for prediction, diagnosis and - above all - appropriate monitoring of pregnant women and fetuses developing the ischemic placental syndrome with the use of tests that are new but yet widely available in clinical diagnosis. They are closely related to the condition's pathogenesis, therefore their elevated levels may predate clinical symptoms, and - most importantly - they correlate with syndrome aggravation and the occurrence of complications. Perhaps, the new look will allow us to improve perinatal results by reducing mortality and severe complications in pregnant women and fetal deaths resulting from sudden intrauterine fetal death or placental abruption. PMID:26444581

  19. A Review of Quality of Life after Predictive Testing for and Earlier Identification of Neurodegenerative Diseases

    PubMed Central

    Paulsen, Jane S.; Nance, Martha; Kim, Ji-In; Carlozzi, Noelle E.; Panegyres, Peter K.; Erwin, Cheryl; Goh, Anita; McCusker, Elizabeth; Williams, Janet K.

    2013-01-01

    The past decade has witnessed an explosion of evidence suggesting that many neurodegenerative diseases can be detected years, if not decades, earlier than previously thought. To date, these scientific advances have not provoked any parallel translational or clinical improvements. There is an urgency to capitalize on this momentum so earlier detection of disease can be more readily translated into improved health-related quality of life for families at risk for, or suffering with, neurodegenerative diseases. In this review, we discuss health-related quality of life (HRQOL) measurement in neurodegenerative diseases and the importance of these “patient reported outcomes” for all clinical research. Next, we address HRQOL following early identification or predictive genetic testing in some neurodegenerative diseases: Huntington disease, Alzheimer's disease, Parkinson's disease, Dementia with Lewy bodies, frontotemporal dementia, amyotrophic lateral sclerosis, prion diseases, hereditary ataxias, Dentatorubral-pallidoluysian atrophy and Wilson's disease. After a brief report of available direct-to-consumer genetic tests, we address the juxtaposition of earlier disease identification with assumed reluctance towards predictive genetic testing. Forty-one studies examining health related outcomes following predictive genetic testing for neurodegenerative disease suggested that (a) extreme or catastrophic outcomes are rare; (b) consequences commonly include transiently increased anxiety and/or depression; (c) most participants report no regret; (d) many persons report extensive benefits to receiving genetic information; and (e) stigmatization and discrimination for genetic diseases are poorly understood and policy and laws are needed. Caution is appropriate for earlier identification of neurodegenerative diseases but findings suggest further progress is safe, feasible and likely to advance clinical care. PMID:24036231

  20. Predicting Mendelian Disease-Causing Non-Synonymous Single Nucleotide Variants in Exome Sequencing Studies

    PubMed Central

    Bao, Su-Ying; Yang, Wanling; Ho, Shu-Leong; Song, Yong-Qiang; Sham, Pak C.

    2013-01-01

    Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ∼22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases. PMID:23341771

  1. Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

    PubMed

    Li, Miao-Xin; Kwan, Johnny S H; Bao, Su-Ying; Yang, Wanling; Ho, Shu-Leong; Song, Yong-Qiang; Sham, Pak C

    2013-01-01

    Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ~22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases. PMID:23341771

  2. NTSMDA: prediction of miRNA-disease associations by integrating network topological similarity.

    PubMed

    Sun, Dongdong; Li, Ao; Feng, Huanqing; Wang, Minghui

    2016-06-21

    Recently, accumulating studies have indicated that microRNAs (miRNAs) play an important role in exploring the pathogenesis of various human diseases at the molecular level and may result in the design of specific tools for diagnosis, treatment evaluation and prevention. Experimental identification of disease-related miRNAs is time-consuming and labour-intensive. Hence, there is a stressing need to propose efficient computational methods to detect more potential miRNA-disease associations. Currently, several computational approaches for identifying disease-related miRNAs on the miRNA-disease network have gained much attention by means of integrating miRNA functional similarities and disease semantic similarities. However, these methods rarely consider the network topological similarity of the miRNA-disease association network. Here, in this paper we develop an improved computational method named NTSMDA that is based on known miRNA-disease network topological similarity to exploit more potential disease-related miRNAs. We achieve an AUC of 89.4% by using the leave-one-out cross-validation experiment, demonstrating the excellent predictive performance of NTSMDA. Furthermore, predicted highly ranked miRNA-disease associations of breast neoplasms, lung neoplasms and prostatic neoplasms are manually confirmed by different related databases and literature, providing evidence for the good performance and potential value of the NTSMDA method in inferring miRNA-disease associations. The R code and readme file of NTSMDA can be downloaded from . PMID:27153230

  3. Calibration of DFT Functionals for the Prediction of 57Fe Mössbauer Spectral Parameters in Iron-Nitrosyl and Iron-Sulfur Complexes: Accurate Geometries Prove Essential

    PubMed Central

    Sandala, Gregory M.; Hopmann, Kathrin H.; Ghosh, Abhik

    2011-01-01

    Six popular density functionals in conjunction with the conductor-like screening (COSMO) solvation model have been used to obtain linear Mössbauer isomer shift (IS) and quadrupole splitting (QS) parameters for a test set of 20 complexes (with 24 sites) comprised of nonheme nitrosyls (Fe–NO) and non-nitrosyl (Fe–S) complexes. For the first time in an IS analysis, the Fe electron density was calculated both directly at the nucleus, ρ(0)N, which is the typical procedure, and on a small sphere surrounding the nucleus, ρ(0)S, which is the new standard algorithm implemented in the ADF software package. We find that both methods yield (near) identical slopes from each linear regression analysis but are shifted with respect to ρ(0) along the x-axis. Therefore, the calculation of the Fe electron density with either method gives calibration fits with equal predictive value. Calibration parameters obtained from the complete test set for OLYP, OPBE, PW91, and BP86 yield correlation coefficients (r2) of approximately 0.90, indicating that the calibration fit is of good quality. However, fits obtained from B3LYP and B3LYP* with both Slater-type and Gaussian-type orbitals are generally found to be of poorer quality. For several of the complexes examined in this study, we find that B3LYP and B3LYP* give geometries that possess significantly larger deviations from the experimental structures than OLYP, OPBE, PW91 or BP86. This phenomenon is particularly true for the di- and tetranuclear Fe complexes examined in this study. Previous Mössbauer calibration fit studies using these functionals have usually included mononuclear Fe complexes alone, where these discrepancies are less pronounced. An examination of spin expectation values reveals B3LYP and B3LYP* approach the weak-coupling limit more closely than the GGA exchange-correlation functionals. The high degree of variability in our calculated S2 values for the Fe–NO complexes highlights their challenging electronic

  4. Prediction of individual clinical scores in patients with Parkinson's disease using resting-state functional magnetic resonance imaging.

    PubMed

    Hou, YanBing; Luo, ChunYan; Yang, Jing; Ou, RuWei; Song, Wei; Wei, QianQian; Cao, Bei; Zhao, Bi; Wu, Ying; Shang, Hui-Fang; Gong, QiYong

    2016-07-15

    Neuroimaging holds the promise that it may one day aid the clinical assessment. However, the vast majority of studies using resting-state functional magnetic resonance imaging (fMRI) have reported average differences between Parkinson's disease (PD) patients and healthy controls, which do not permit inferences at the level of individuals. This study was to develop a model for the prediction of PD illness severity ratings from individual fMRI brain scan. The resting-state fMRI scans were obtained from 84 patients with PD and the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) scores were obtained before scanning. The RVR method was used to predict clinical scores (UPDRS-III) from fMRI scans. The application of RVR to whole-brain resting-state fMRI data allowed prediction of UPDRS-III scores with statistically significant accuracy (correlation=0.35, P-value=0.001; mean sum of squares=222.17, P-value=0.002). This prediction was informed strongly by negative weight areas including prefrontal lobe and medial occipital lobe, and positive weight areas including medial parietal lobe. It was suggested that fMRI scans contained sufficient information about neurobiological change in patients with PD to permit accurate prediction about illness severity, on an individual subject basis. Our results provided preliminary evidence, as proof-of-concept, to support that fMRI might be possible to be a clinically useful quantitative assessment aid in PD at individual level. This may enable clinicians to target those uncooperative patients and machines to replace human for a more efficient use of health care resources. PMID:27288771

  5. Predicting relapse of Graves' disease following treatment with antithyroid drugs

    PubMed Central

    LIU, LIN; LU, HONGWEN; LIU, YANG; LIU, CHANGSHAN; XUN, CHU

    2016-01-01

    The aim of the present study was to monitor long term antithyroid drug treatments and to identify prognostic factors for Graves' disease (GD). A total of 306 patients with GD who were referred to the Endocrinology Clinic at Weifang People's Hospital (Weifang, China) between August 2005 and June 2009 and treated with methimazole were included in the present study. Following treatment, patients were divided into non-remission, including recurrence and constant treatment subgroups, and remission groups. Various prognosis factors were analyzed and compared, including: Patient age, gender, size of thyroid prior to and following treatment, thyroid hormone levels, disease relapse, hypothyroidism and drug side-effects, and states of thyrotropin suppression were observed at 3, 6 and 12 months post-treatment. Sixty-five patients (21.2%) were male, and 241 patients (78.8%) were female. The mean age was 42±11 years, and the follow-up was 31.5±6.8 months. Following long-term treatment, 141 patients (46%) demonstrated remission of hyperthyroidism with a mean duration of 18.7±1.9 months. The average age at diagnosis was 45.6±10.3 years in the remission group, as compared with 36.4±8.8 years in the non-remission group (t=3.152; P=0.002). Free thyroxine (FT)3 levels were demonstrated to be 25.2±8.9 and 18.7±9.4 pmol/l in the non-remission and remission groups, respectively (t=3.326, P=0.001). The FT3/FT4 ratio and thyrotrophin receptor antibody (TRAb) levels were both significantly higher in the non-remission group (t=3.331, 3.389, P=0.001), as compared with the remission group. Logistic regression analysis demonstrated that elevated thyroid size, FT3/FT4 ratio and TRAb at diagnosis were associated with poor outcomes. The ratio of continued thyrotropin suppression in the recurrent subgroup was significantly increased, as compared with the remission group (P=0.001), as thyroid function reached euthyroid state at 3, 6 and 12 months post-treatment. Patients with GD exhibiting

  6. Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.

    PubMed

    Reeb, Jonas; Hecht, Maximilian; Mahlich, Yannick; Bromberg, Yana; Rost, Burkhard

    2016-08-01

    Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease. PMID:27536940

  7. Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

    PubMed

    Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

    2015-01-01

    Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care. PMID:26262380

  8. Accurate prediction of explicit solvent atom distribution in HIV-1 protease and F-ATP synthase by statistical theory of liquids

    NASA Astrophysics Data System (ADS)

    Sindhikara, Daniel; Yoshida, Norio; Hirata, Fumio

    2012-02-01

    We have created a simple algorithm for automatically predicting the explicit solvent atom distribution of biomolecules. The explicit distribution is coerced from the 3D continuous distribution resulting from a 3D-RISM calculation. This procedure predicts optimal location of solvent molecules and ions given a rigid biomolecular structure. We show examples of predicting water molecules near KNI-275 bound form of HIV-1 protease and predicting both sodium ions and water molecules near the rotor ring of F-ATP synthase. Our results give excellent agreement with experimental structure with an average prediction error of 0.45-0.65 angstroms. Further, unlike experimental methods, this method does not suffer from the partial occupancy limit. Our method can be performed directly on 3D-RISM output within minutes. It is useful not only as a location predictor but also as a convenient method for generating initial structures for MD calculations.

  9. Genetic prediction of common diseases. Still no help for the clinical diabetologist!

    PubMed Central

    Prudente, Sabrina; Dallapiccola, Bruno; Pellegrini, Fabio; Doria, Alessandro; Trischitta, Vincenzo

    2013-01-01

    Genome-wide association studies (GWAS) have identified several loci associated with many common, multifactorial diseases which have been recently used to market genetic testing directly to the consumers. We here addressed the clinical utility of such GWAS-derived genetic information in predicting type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) in diabetic patients. In addition, the development of new statistical approaches, novel technologies of genome sequencing and ethical, legal and social aspects related to genetic testing have been also addressed. Available data clearly show that, similarly to what reported for most common diseases, genetic testing offered today by commercial companies cannot be used as predicting tools for T2DM and CAD, both in the general and in the diabetic population. Further studies taking into account the complex interaction between genes as well as between genetic and non genetic factors, including age, obesity and glycemic control which seem to modify genetic effects on the risk of T2DM and CAD, might mitigate such negative conclusions. Also, addressing the role of relatively rare variants by next-generation sequencing may help identify novel and strong genetic markers with an important role in genetic prediction. Finally, statistical tools concentrated on reclassifying patients might be a useful application of genetic information for predicting many common diseases. By now, prediction of such diseases, including those of interest for the clinical diabetologist, have to be pursued by using traditional clinical markers which perform well and are not costly. PMID:22819342

  10. Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives.

    PubMed

    Müller, Bent; Wilcke, Arndt; Boulesteix, Anne-Laure; Brauer, Jens; Passarge, Eberhard; Boltze, Johannes; Kirsten, Holger

    2016-03-01

    Reliable risk assessment of frequent, but treatable diseases and disorders has considerable clinical and socio-economic relevance. However, as these conditions usually originate from a complex interplay between genetic and environmental factors, precise prediction remains a considerable challenge. The current progress in genotyping technology has resulted in a substantial increase of knowledge regarding the genetic basis of such diseases and disorders. Consequently, common genetic risk variants are increasingly being included in epidemiological models to improve risk prediction. This work reviews recent high-quality publications targeting the prediction of common complex diseases. To be included in this review, articles had to report both, numerical measures of prediction performance based on traditional (non-genetic) risk factors, as well as measures of prediction performance when adding common genetic variants to the model. Systematic PubMed-based search finally identified 55 eligible studies. These studies were compared with respect to the chosen approach and methodology as well as results and clinical impact. Phenotypes analysed included tumours, diabetes mellitus, and cardiovascular diseases. All studies applied one or more statistical measures reporting on calibration, discrimination, or reclassification to quantify the benefit of including SNPs, but differed substantially regarding the methodological details that were reported. Several examples for improved risk assessments by considering disease-related SNPs were identified. Although the add-on benefit of including SNP genotyping data was mostly moderate, the strategy can be of clinical relevance and may, when being paralleled by an even deeper understanding of disease-related genetics, further explain the development of enhanced predictive and diagnostic strategies for complex diseases. PMID:26839113

  11. Fast and accurate predictions of heat of formation by G4MP2-SFM parameterization scheme: An application to imidazole derivatives

    NASA Astrophysics Data System (ADS)

    Shoaib, Mahbubul Alam; Cho, Soo Gyeong; Choi, Cheol Ho

    2014-04-01

    We proposed a new parameterization scheme, G4MP2-SFM, for the prediction of heat of formation by combining SFM (Systematic Fragmentation Method) and high accuracy G4MP2 theories. In an application to imidazole derivatives, we found that the overall MAD and RMSD of the particular G4MP2-SFM(opt) are 1.9 and 2.2 kcal/mol, respectively, demonstrating its high prediction accuracy. In addition, our parameterization scheme replaces the ab initio computations with a set of simple arithmetic, allowing fast predictions. Our new computational scheme can be of practical use in high throughput search for new high energy materials.

  12. Biomarkers of respiratory syncytial virus (RSV) infection: specific neutrophil and cytokine levels provide increased accuracy in predicting disease severity.

    PubMed

    Brown, Paul M; Schneeberger, Dana L; Piedimonte, Giovanni

    2015-09-01

    Despite fundamental advances in the research on respiratory syncytial virus (RSV) since its initial identification almost 60 years ago, recurring failures in developing vaccines and pharmacologic strategies effective in controlling the infection have allowed RSV to become a leading cause of global infant morbidity and mortality. Indeed, the burden of this infection on families and health care organizations worldwide continues to escalate and its financial costs are growing. Furthermore, strong epidemiologic evidence indicates that early-life lower respiratory tract infections caused by RSV lead to the development of recurrent wheezing and childhood asthma. While some progress has been made in the identification of reliable biomarkers for RSV bronchiolitis, a "one size fits all" biomarker capable of accurately and consistently predicting disease severity and post-acute outcomes has yet to be discovered. Therefore, it is of great importance on a global scale to identify useful biomarkers for this infection that will allow pediatricians to cost-effectively predict the clinical course of the disease, as well as monitor the efficacy of new therapeutic strategies. PMID:26074450

  13. Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology

    NASA Astrophysics Data System (ADS)

    Li, Jie; Wu, Zengrui; Cheng, Feixiong; Li, Weihua; Liu, Guixia; Tang, Yun

    2014-07-01

    MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.

  14. Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology.

    PubMed

    Li, Jie; Wu, Zengrui; Cheng, Feixiong; Li, Weihua; Liu, Guixia; Tang, Yun

    2014-01-01

    MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs. PMID:24992957

  15. Histology Verification Demonstrates That Biospectroscopy Analysis of Cervical Cytology Identifies Underlying Disease More Accurately than Conventional Screening: Removing the Confounder of Discordance

    PubMed Central

    Gajjar, Ketan; Ahmadzai, Abdullah A.; Valasoulis, George; Trevisan, Júlio; Founta, Christina; Nasioutziki, Maria; Loufopoulos, Aristotelis; Kyrgiou, Maria; Stasinou, Sofia Melina; Karakitsos, Petros; Paraskevaidis, Evangelos; Da Gama-Rose, Bianca; Martin-Hirsch, Pierre L.; Martin, Francis L.

    2014-01-01

    Background Subjective visual assessment of cervical cytology is flawed, and this can manifest itself by inter- and intra-observer variability resulting ultimately in the degree of discordance in the grading categorisation of samples in screening vs. representative histology. Biospectroscopy methods have been suggested as sensor-based tools that can deliver objective assessments of cytology. However, studies to date have been apparently flawed by a corresponding lack of diagnostic efficiency when samples have previously been classed using cytology screening. This raises the question as to whether categorisation of cervical cytology based on imperfect conventional screening reduces the diagnostic accuracy of biospectroscopy approaches; are these latter methods more accurate and diagnose underlying disease? The purpose of this study was to compare the objective accuracy of infrared (IR) spectroscopy of cervical cytology samples using conventional cytology vs. histology-based categorisation. Methods Within a typical clinical setting, a total of n = 322 liquid-based cytology samples were collected immediately before biopsy. Of these, it was possible to acquire subsequent histology for n = 154. Cytology samples were categorised according to conventional screening methods and subsequently interrogated employing attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. IR spectra were pre-processed and analysed using linear discriminant analysis. Dunn’s test was applied to identify the differences in spectra. Within the diagnostic categories, histology allowed us to determine the comparative efficiency of conventional screening vs. biospectroscopy to correctly identify either true atypia or underlying disease. Results Conventional cytology-based screening results in poor sensitivity and specificity. IR spectra derived from cervical cytology do not appear to discriminate in a diagnostic fashion when categories were based on conventional screening

  16. Gamma-Glutamyltransferase: A Predictive Biomarker of Cellular Antioxidant Inadequacy and Disease Risk.

    PubMed

    Koenig, Gerald; Seneff, Stephanie

    2015-01-01

    Gamma-glutamyltransferase (GGT) is a well-established serum marker for alcohol-related liver disease. However, GGT's predictive utility applies well beyond liver disease: elevated GGT is linked to increased risk to a multitude of diseases and conditions, including cardiovascular disease, diabetes, metabolic syndrome (MetS), and all-cause mortality. The literature from multiple population groups worldwide consistently shows strong predictive power for GGT, even across different gender and ethnic categories. Here, we examine the relationship of GGT to other serum markers such as serum ferritin (SF) levels, and we suggest a link to exposure to environmental and endogenous toxins, resulting in oxidative and nitrosative stress. We observe a general upward trend in population levels of GGT over time, particularly in the US and Korea. Since the late 1970s, both GGT and incident MetS and its related disorders have risen in virtual lockstep. GGT is an early predictive marker for atherosclerosis, heart failure, arterial stiffness and plaque, gestational diabetes, and various liver diseases, including viral hepatitis, other infectious diseases, and several life-threatening cancers. We review literature both from the medical sciences and from life insurance industries demonstrating that serum GGT is a superior marker for future disease risk, when compared against multiple other known mortality risk factors. PMID:26543300

  17. Predicting disease-related genes by topological similarity in human protein-protein interaction network

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Hu, Ke; Tang, Yi

    2010-08-01

    Predicting genes likely to be involved in human diseases is an important task in bioinformatics field. Nowadays, the accumulation of human protein-protein interactions (PPIs) data provides us an unprecedented opportunity to gain insight into human diseases. In this paper, we adopt the topological similarity in human protein-protein interaction network to predict disease-related genes. As a computational algorithm to speed up the identification of disease-related genes, the topological similarity has substantial advantages over previous topology-based algorithms. First of all, it provides a global measurement of similarity between two vertices. Secondly, quantity which can measure new topological feature has been integrated into the notion of topological similarity. Our method is specially designed for predicting disease-related genes of single disease-gene family. The proposed method is applied to human protein-protein interaction and hepatocellular carcinoma (HCC) data. The results show a significant enrichment of disease-related genes that are characterized by higher topological similarity than other genes.

  18. Gamma-Glutamyltransferase: A Predictive Biomarker of Cellular Antioxidant Inadequacy and Disease Risk

    PubMed Central

    Koenig, Gerald; Seneff, Stephanie

    2015-01-01

    Gamma-glutamyltransferase (GGT) is a well-established serum marker for alcohol-related liver disease. However, GGT's predictive utility applies well beyond liver disease: elevated GGT is linked to increased risk to a multitude of diseases and conditions, including cardiovascular disease, diabetes, metabolic syndrome (MetS), and all-cause mortality. The literature from multiple population groups worldwide consistently shows strong predictive power for GGT, even across different gender and ethnic categories. Here, we examine the relationship of GGT to other serum markers such as serum ferritin (SF) levels, and we suggest a link to exposure to environmental and endogenous toxins, resulting in oxidative and nitrosative stress. We observe a general upward trend in population levels of GGT over time, particularly in the US and Korea. Since the late 1970s, both GGT and incident MetS and its related disorders have risen in virtual lockstep. GGT is an early predictive marker for atherosclerosis, heart failure, arterial stiffness and plaque, gestational diabetes, and various liver diseases, including viral hepatitis, other infectious diseases, and several life-threatening cancers. We review literature both from the medical sciences and from life insurance industries demonstrating that serum GGT is a superior marker for future disease risk, when compared against multiple other known mortality risk factors. PMID:26543300

  19. Coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC) study.

    PubMed

    Chambless, Lloyd E; Folsom, Aaron R; Sharrett, A Richey; Sorlie, Paul; Couper, David; Szklo, Moyses; Nieto, F Javier

    2003-09-01

    Risk prediction functions for incident coronary heart disease (CHD) were estimated using data from the Atherosclerosis Risk in Communities (ARIC) Study, a prospective study of CHD in 15,792 persons recruited in 1987-1989 from four U.S. communities, with follow-up through 1998. Predictivity of which individuals had incident CHD was assessed by increase in area under ROC curves resulting from adding nontraditional risk factors and markers of subclinical disease to a basic model containing only traditional risk factors. We also assessed the increase in population attributable risk. The additional factors were body mass index; waist-hip ratio; sport activity index; forced expiratory volume; plasma fibrinogen, factor VIII, von Willebrand factor, and Lp(a); heart rate; Keys score; pack-years smoking; and subclinical disease marker carotid intima-media thickness. These factors substantially improved prediction of future CHD for men, less for women, and also increased attributable risks. PMID:14505774

  20. Bioinformatic Screening of Autoimmune Disease Genes and Protein Structure Prediction with FAMS for Drug Discovery

    PubMed Central

    Ishida, Shigeharu; Umeyama, Hideaki; Iwadate, Mitsuo; Y-h, Taguchi

    2014-01-01

    Autoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease. Full Automatic Modeling System (FAMS) was used to predict the three-dimensional structures of selected proteins and successfully inferred relatively confident structures. Several possibilities of the application to the drug discovery based on obtained structures are discussed. PMID:23855671

  1. Relationships between predicted moonlighting proteins, human diseases, and comorbidities from a network perspective

    PubMed Central

    Zanzoni, Andreas; Chapple, Charles E.; Brun, Christine

    2015-01-01

    Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes. PMID:26157390

  2. Predicting disease progression from short biomarker series using expert advice algorithm

    NASA Astrophysics Data System (ADS)

    Morino, Kai; Hirata, Yoshito; Tomioka, Ryota; Kashima, Hisashi; Yamanishi, Kenji; Hayashi, Norihiro; Egawa, Shin; Aihara, Kazuyuki

    2015-05-01

    Well-trained clinicians may be able to provide diagnosis and prognosis from very short biomarker series using information and experience gained from previous patients. Although mathematical methods can potentially help clinicians to predict the progression of diseases, there is no method so far that estimates the patient state from very short time-series of a biomarker for making diagnosis and/or prognosis by employing the information of previous patients. Here, we propose a mathematical framework for integrating other patients' datasets to infer and predict the state of the disease in the current patient based on their short history. We extend a machine-learning framework of ``prediction with expert advice'' to deal with unstable dynamics. We construct this mathematical framework by combining expert advice with a mathematical model of prostate cancer. Our model predicted well the individual biomarker series of patients with prostate cancer that are used as clinical samples.

  3. Climate change and infectious diseases: from evidence to a predictive framework.

    PubMed

    Altizer, Sonia; Ostfeld, Richard S; Johnson, Pieter T J; Kutz, Susan; Harvell, C Drew

    2013-08-01

    Scientists have long predicted large-scale responses of infectious diseases to climate change, giving rise to a polarizing debate, especially concerning human pathogens for which socioeconomic drivers and control measures can limit the detection of climate-mediated changes. Climate change has already increased the occurrence of diseases in some natural and agricultural systems, but in many cases, outcomes depend on the form of climate change and details of the host-pathogen system. In this review, we highlight research progress and gaps that have emerged during the past decade and develop a predictive framework that integrates knowledge from ecophysiology and community ecology with modeling approaches. Future work must continue to anticipate and monitor pathogen biodiversity and disease trends in natural ecosystems and identify opportunities to mitigate the impacts of climate-driven disease emergence. PMID:23908230

  4. A Physically Based Theoretical Model of Spore Deposition for Predicting Spread of Plant Diseases.

    PubMed

    Isard, Scott A; Chamecki, Marcelo

    2016-03-01

    A physically based theory for predicting spore deposition downwind from an area source of inoculum is presented. The modeling framework is based on theories of turbulence dispersion in the atmospheric boundary layer and applies only to spores that escape from plant canopies. A "disease resistance" coefficient is introduced to convert the theoretical spore deposition model into a simple tool for predicting disease spread at the field scale. Results from the model agree well with published measurements of Uromyces phaseoli spore deposition and measurements of wheat leaf rust disease severity. The theoretical model has the advantage over empirical models in that it can be used to assess the influence of source distribution and geometry, spore characteristics, and meteorological conditions on spore deposition and disease spread. The modeling framework is refined to predict the detailed two-dimensional spatial pattern of disease spread from an infection focus. Accounting for the time variations of wind speed and direction in the refined modeling procedure improves predictions, especially near the inoculum source, and enables application of the theoretical modeling framework to field experiment design. PMID:26595112

  5. Cytokine Profiles during Invasive Nontyphoidal Salmonella Disease Predict Outcome in African Children.

    PubMed

    Gilchrist, James J; Heath, Jennifer N; Msefula, Chisomo L; Gondwe, Esther N; Naranbhai, Vivek; Mandala, Wilson; MacLennan, Jenny M; Molyneux, Elizabeth M; Graham, Stephen M; Drayson, Mark T; Molyneux, Malcolm E; MacLennan, Calman A

    2016-07-01

    Nontyphoidal Salmonella is a leading cause of sepsis in African children. Cytokine responses are central to the pathophysiology of sepsis and predict sepsis outcome in other settings. In this study, we investigated cytokine responses to invasive nontyphoidal Salmonella (iNTS) disease in Malawian children. We determined serum concentrations of 48 cytokines with multiplexed immunoassays in Malawian children during acute iNTS disease (n = 111) and in convalescence (n = 77). Principal component analysis and logistic regression were used to identify cytokine signatures of acute iNTS disease. We further investigated whether these responses are altered by HIV coinfection or severe malnutrition and whether cytokine responses predict inpatient mortality. Cytokine changes in acute iNTS disease were associated with two distinct cytokine signatures. The first is characterized by increased concentrations of mediators known to be associated with macrophage function, and the second is characterized by raised pro- and anti-inflammatory cytokines typical of responses reported in sepsis secondary to diverse pathogens. These cytokine responses were largely unaltered by either severe malnutrition or HIV coinfection. Children with fatal disease had a distinctive cytokine profile, characterized by raised mediators known to be associated with neutrophil function. In conclusion, cytokine responses to acute iNTS infection in Malawian children are reflective of both the cytokine storm typical of sepsis secondary to diverse pathogens and the intramacrophage replicative niche of NTS. The cytokine profile predictive of fatal disease supports a key role of neutrophils in the pathogenesis of NTS sepsis. PMID:27170644

  6. Route prediction model of infectious diseases for 2018 Winter Olympics in Korea

    NASA Astrophysics Data System (ADS)

    Kim, Eungyeong; Lee, Seok; Byun, Young Tae; Kim, Jae Hun; Lee, Hyuk-jae; Lee, Taikjin

    2014-03-01

    There are many types of respiratory infectious diseases caused by germs, virus, mycetes and parasites. Researchers recently have tried to develop mathematical models to predict the epidemic of infectious diseases. However, with the development of ground transportation system in modern society, the spread of infectious diseases became faster and more complicated in terms of the speed and the pathways. The route of infectious diseases during Vancouver Olympics was predicted based on the Susceptible-Infectious-Recovered (SIR) model. In this model only the air traffic as an essential factor for the intercity migration of infectious diseases was involved. Here, we propose a multi-city transmission model to predict the infection route during 2018 Winter Olympics in Korea based on the pre-existing SIR model. Various types of transportation system such as a train, a car, a bus, and an airplane for the interpersonal contact in both inter- and intra-city are considered. Simulation is performed with assumptions and scenarios based on realistic factors including demographic, transportation and diseases data in Korea. Finally, we analyze an economic profit and loss caused by the variation of the number of tourists during the Olympics.

  7. Establishing Precise Evolutionary History of a Gene Improves Predicting Disease Causing Missense Mutations

    PubMed Central

    Adebali, Ogun; Reznik, Alexander O.; Ory, Daniel S.; Zhulin, Igor B.

    2015-01-01

    Purpose Predicting the phenotypic effects of mutations has become an important application in clinical genetic diagnostics. Computational tools evaluate the behavior of the variant over evolutionary time and assume that variations seen during the course of evolution are likely benign in humans. However, current tools do not take into account orthologous/paralogous relationships. Paralogs have dramatically different roles in Mendelian diseases. For example, while inactivating mutations in the NPC1 gene cause the neurodegenerative disorder Niemann-Pick C, inactivating mutations in its paralog NPC1L1 are not disease-causing and moreover are implicated in protection from coronary heart disease. Methods We identified major events in NPC1 evolution and revealed and compared orthologs and paralogs of the human NPC1 gene through phylogenetic and protein sequence analyses. We predicted whether an amino acid substitution affects protein function by reducing the organism’s fitness. Results Removing the paralogs and distant homologs improved the overall performance of categorizing disease-causing and benign amino acid substitutions. Conclusion The results show that a thorough evolutionary analysis followed by identification of orthologs improves the accuracy in predicting disease-causing missense mutations. We anticipate that this approach will be used as a reference in the interpretation of variants in other genetic diseases as well. PMID:26890452

  8. Cytokine Profiles during Invasive Nontyphoidal Salmonella Disease Predict Outcome in African Children

    PubMed Central

    Gilchrist, James J.; Heath, Jennifer N.; Msefula, Chisomo L.; Gondwe, Esther N.; Naranbhai, Vivek; Mandala, Wilson; MacLennan, Jenny M.; Molyneux, Elizabeth M.; Graham, Stephen M.; Drayson, Mark T.; Molyneux, Malcolm E.

    2016-01-01

    Nontyphoidal Salmonella is a leading cause of sepsis in African children. Cytokine responses are central to the pathophysiology of sepsis and predict sepsis outcome in other settings. In this study, we investigated cytokine responses to invasive nontyphoidal Salmonella (iNTS) disease in Malawian children. We determined serum concentrations of 48 cytokines with multiplexed immunoassays in Malawian children during acute iNTS disease (n = 111) and in convalescence (n = 77). Principal component analysis and logistic regression were used to identify cytokine signatures of acute iNTS disease. We further investigated whether these responses are altered by HIV coinfection or severe malnutrition and whether cytokine responses predict inpatient mortality. Cytokine changes in acute iNTS disease were associated with two distinct cytokine signatures. The first is characterized by increased concentrations of mediators known to be associated with macrophage function, and the second is characterized by raised pro- and anti-inflammatory cytokines typical of responses reported in sepsis secondary to diverse pathogens. These cytokine responses were largely unaltered by either severe malnutrition or HIV coinfection. Children with fatal disease had a distinctive cytokine profile, characterized by raised mediators known to be associated with neutrophil function. In conclusion, cytokine responses to acute iNTS infection in Malawian children are reflective of both the cytokine storm typical of sepsis secondary to diverse pathogens and the intramacrophage replicative niche of NTS. The cytokine profile predictive of fatal disease supports a key role of neutrophils in the pathogenesis of NTS sepsis. PMID:27170644

  9. Lesion Explorer: a video-guided, standardized protocol for accurate and reliable MRI-derived volumetrics in Alzheimer's disease and normal elderly.

    PubMed

    Ramirez, Joel; Scott, Christopher J M; McNeely, Alicia A; Berezuk, Courtney; Gao, Fuqiang; Szilagyi, Gregory M; Black, Sandra E

    2014-01-01

    Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests(1,2). However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs. LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors. While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures. PMID:24797507

  10. Intestinal Intraepithelial Lymphocyte Cytometric Pattern Is More Accurate than Subepithelial Deposits of Anti-Tissue Transglutaminase IgA for the Diagnosis of Celiac Disease in Lymphocytic Enteritis

    PubMed Central

    García-Puig, Roger; Rosinach, Mercè; González, Clarisa; Alsina, Montserrat; Loras, Carme; Salas, Antonio; Viver, Josep M.; Esteve, Maria

    2014-01-01

    Background & Aims An increase in CD3+TCRγδ+ and a decrease in CD3− intraepithelial lymphocytes (IEL) is a characteristic flow cytometric pattern of celiac disease (CD) with atrophy. The aim was to evaluate the usefulness of both CD IEL cytometric pattern and anti-TG2 IgA subepithelial deposit analysis (CD IF pattern) for diagnosing lymphocytic enteritis due to CD. Methods Two-hundred and five patients (144 females) who underwent duodenal biopsy for clinical suspicion of CD and positive celiac genetics were prospectively included. Fifty had villous atrophy, 70 lymphocytic enteritis, and 85 normal histology. Eight patients with non-celiac atrophy and 15 with lymphocytic enteritis secondary to Helicobacter pylori acted as control group. Duodenal biopsies were obtained to assess both CD IEL flow cytometric (complete or incomplete) and IF patterns. Results Sensitivity of IF, and complete and incomplete cytometric patterns for CD diagnosis in patients with positive serology (Marsh 1+3) was 92%, 85 and 97% respectively, but only the complete cytometric pattern had 100% specificity. Twelve seropositive and 8 seronegative Marsh 1 patients had a CD diagnosis at inclusion or after gluten free-diet, respectively. CD cytometric pattern showed a better diagnostic performance than both IF pattern and serology for CD diagnosis in lymphocytic enteritis at baseline (95% vs 60% vs 60%, p = 0.039). Conclusions Analysis of the IEL flow cytometric pattern is a fast, accurate method for identifying CD in the initial diagnostic biopsy of patients presenting with lymphocytic enteritis, even in seronegative patients, and seems to be better than anti-TG2 intestinal deposits. PMID:25010214

  11. Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

    PubMed Central

    Ramirez, Joel; Scott, Christopher J.M.; McNeely, Alicia A.; Berezuk, Courtney; Gao, Fuqiang; Szilagyi, Gregory M.; Black, Sandra E.

    2014-01-01

    Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs. LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors. While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures. PMID:24797507

  12. MREdictor: a two-step dynamic interaction model that accounts for mRNA accessibility and Pumilio binding accurately predicts microRNA targets

    PubMed Central

    Incarnato, Danny; Neri, Francesco; Diamanti, Daniela; Oliviero, Salvatore

    2013-01-01

    The prediction of pairing between microRNAs (miRNAs) and the miRNA recognition elements (MREs) on mRNAs is expected to be an important tool for understanding gene regulation. Here, we show that mRNAs that contain Pumilio recognition elements (PRE) in the proximity of predicted miRNA-binding sites are more likely to form stable secondary structures within their 3′-UTR, and we demonstrated using a PUM1 and PUM2 double knockdown that Pumilio proteins are general regulators of miRNA accessibility. On the basis of these findings, we developed a computational method for predicting miRNA targets that accounts for the presence of PRE in the proximity of seed-match sequences within poorly accessible structures. Moreover, we implement the miRNA-MRE duplex pairing as a two-step model, which better fits the available structural data. This algorithm, called MREdictor, allows for the identification of miRNA targets in poorly accessible regions and is not restricted to a perfect seed-match; these features are not present in other computational prediction methods. PMID:23863844

  13. An Object-Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes.

    PubMed

    Zhao, Lue Ping; Bolouri, Hamid; Zhao, Michael; Geraghty, Daniel E; Lernmark, Åke

    2016-05-01

    Recent genome-wide association studies confirm that human leukocyte antigen (HLA) genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA-based disease predictive model. However, conventional model-building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as "objects" or "exemplars," one focuses on systemic associations of disease phenotype with "objects" via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease-associated genotypes or alleles. Effectively, it transforms large, discrete, and sparse HLA genotypes into a matrix of similarity-based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease-associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set, indicating that this methodology is useful to build predictive models with complex HLA genotypes. PMID:27080919

  14. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

    PubMed Central

    Himmelstein, Daniel S.; Baranzini, Sergio E.

    2015-01-01

    The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data

  15. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.

    PubMed

    Himmelstein, Daniel S; Baranzini, Sergio E

    2015-07-01

    The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data

  16. Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree

    PubMed Central

    Kim, Jaekwon; Lee, Jongsik

    2015-01-01

    Objectives The importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans. Methods A model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model. Results The rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction. Conclusions The accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models. PMID:26279953

  17. Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree-Fock energies, and small subsets of the database

    NASA Astrophysics Data System (ADS)

    Malshe, M.; Pukrittayakamee, A.; Raff, L. M.; Hagan, M.; Bukkapatnam, S.; Komanduri, R.

    2009-09-01

    A novel method is presented that significantly reduces the computational bottleneck of executing high-level, electronic structure calculations of the energies and their gradients for a large database that adequately samples the configuration space of importance for systems containing more than four atoms that are undergoing multiple, simultaneous reactions in several energetically open channels. The basis of the method is the high-degree of correlation that generally exists between the Hartree-Fock (HF) and higher-level electronic structure energies. It is shown that if the input vector to a neural network (NN) includes both the configuration coordinates and the HF energies of a small subset of the database, MP4(SDQ) energies with the same basis set can be predicted for the entire database using only the HF and MP4(SDQ) energies for the small subset and the HF energies for the remainder of the database. The predictive error is shown to be less than or equal to the NN fitting error if a NN is fitted to the entire database of higher-level electronic structure energies. The general method is applied to the computation of MP4(SDQ) energies of 68 308 configurations that comprise the database for the simultaneous, unimolecular decomposition of vinyl bromide into six different reaction channels. The predictive accuracy of the method is investigated by employing successively smaller subsets of the database to train the NN to predict the MP4(SDQ) energies of the remaining configurations of the database. The results indicate that for this system, the subset can be as small as 8% of the total number of configurations in the database without loss of accuracy beyond that expected if a NN is employed to fit the higher-level energies for the entire database. The utilization of this procedure is shown to save about 78% of the total computational time required for the execution of the MP4(SDQ) calculations. The sampling error involved with selection of the subset is shown to be

  18. Physical Activity Level Improves the Predictive Accuracy of Cardiovascular Disease Risk Score: The ATTICA Study (2002–2012)

    PubMed Central

    Georgousopoulou, Ekavi N.; Panagiotakos, Demosthenes B.; Bougatsas, Dimitrios; Chatzigeorgiou, Michael; Kavouras, Stavros A.; Chrysohoou, Christina; Skoumas, Ioannis; Tousoulis, Dimitrios; Stefanadis, Christodoulos; Pitsavos, Christos

    2016-01-01

    Background: Although physical activity (PA) has long been associated with cardiovascular disease (CVD), assessment of PA status has never been used as a part of CVD risk prediction tools. The aim of the present work was to examine whether the inclusion of PA status in a CVD risk model improves its predictive accuracy. Methods: Data from the 10-year follow-up (2002–2012) of the n = 2020 participants (aged 18–89 years) of the ATTICA prospective study were used to test the research hypothesis. The HellenicSCORE (that incorporates age, sex, smoking, total cholesterol, and systolic blood pressure levels) was calculated to estimate the baseline 10-year CVD risk; assessment of PA status was based on the International Physical Activity Questionnaire. The estimated CVD risk was tested against the observed 10-year incidence (i.e., development of acute coronary syndromes, stroke, or other CVD according to the World Health Organization [WHO]-International Classification of Diseases [ICD]-10 criteria). Changes in the predictive ability of the nested CVD risk model that contained the HellenicSCORE plus PA assessment were evaluated using Harrell's C and net reclassification index. Results: Both HellenicSCORE and PA status were predictors of future CVD events (P < 0.05). However, the estimating classification bias of the model that included only the HellenicSCORE was significantly reduced when PA assessment was included (Harrel's C = 0.012, P = 0.032); this reduction remained significant even when adjusted for diabetes mellitus and dietary habits (P < 0.05). Conclusions: CVD risk scores seem to be more accurate by incorporating individuals’ PA status; thus, may be more effective tools in primary prevention by efficiently allocating CVD candidates. PMID:27076890

  19. Prediction of Quality of Life for Children and Adolescents with Cardiac Disease by Clinicians

    PubMed Central

    Costello, John M.; Mussatto, Kathleen; Cassedy, Amy; Wray, Jo; Mahony, Lynn; Teele, Sarah A.; Brown, Kate L.; Franklin, Rodney C.; Wernovsky, Gil; Marino, Bradley S.

    2015-01-01

    Objective To determine whether clinicians could reliably predict health-related quality of life (HRQOL) for children with cardiac disease, the level of agreement in predicted HRQOL scores between clinician sub-types, and agreement between clinician-predicted HRQOL scores and patient and parent-proxy reported HRQOL scores. Study design In this multi-center, cross-sectional study, a random sample of clinical summaries of children with cardiac disease and related patient and parent-proxy reported HRQOL scores were extracted from the Pediatric Cardiac Quality of Life Inventory data registry. We asked clinicians to review each clinical summary and predict HRQOL. Results Experienced pediatric cardiac clinicians (n=140), including intensive care physicians, outpatient cardiologists, and intensive care, outpatient and advanced practice nurses, each predicted HRQOL for the same 21 pediatric cardiac patients. Reliability within clinician subspecialty groups for predicting HRQOL was poor (intraclass correlation coefficients, 0.34-0.38). Agreement between clinician groups was low (Pearson's correlation coefficients, 0.10-0.29). When comparing the average clinician predicted HRQOL scores to those reported by patients and parent-proxies by Bland Altman plots, little systematic bias was present, but substantial variability existed. Proportional bias was found, in that clinicians tended to overestimate HRQOL for those patients and parent-proxies who reported lower HRQOL, and underestimate HRQOL for those reporting higher HRQOL. Conclusions Clinicians perform poorly when asked to predict HRQOL for children with cardiac disease. Clinicians should be cognizant of these data when providing counseling. Incorporating reported HRQOL into clinical assessment may help guide individualized treatment decision-making. PMID:25722271

  20. Use of dose-dependent absorption into target tissues to more accurately predict cancer risk at low oral doses of hexavalent chromium.

    PubMed

    Haney, J

    2015-02-01

    The mouse dose at the lowest water concentration used in the National Toxicology Program hexavalent chromium (CrVI) drinking water study (NTP, 2008) is about 74,500 times higher than the approximate human dose corresponding to the 35-city geometric mean reported in EWG (2010) and over 1000 times higher than that based on the highest reported tap water concentration. With experimental and environmental doses differing greatly, it is a regulatory challenge to extrapolate high-dose results to environmental doses orders of magnitude lower in a meaningful and toxicologically predictive manner. This seems particularly true for the low-dose extrapolation of results for oral CrVI-induced carcinogenesis since dose-dependent differences in the dose fraction absorbed by mouse target tissues are apparent (Kirman et al., 2012). These data can be used for a straightforward adjustment of the USEPA (2010) draft oral slope factor (SFo) to be more predictive of risk at environmentally-relevant doses. More specifically, the evaluation of observed and modeled differences in the fraction of dose absorbed by target tissues at the point-of-departure for the draft SFo calculation versus lower doses suggests that the draft SFo be divided by a dose-specific adjustment factor of at least an order of magnitude to be less over-predictive of risk at more environmentally-relevant doses. PMID:25445295

  1. European Crohn's and Colitis Organisation Topical Review on Prediction, Diagnosis and Management of Fibrostenosing Crohn's Disease.

    PubMed

    Rieder, Florian; Latella, Giovanni; Magro, Fernando; Yuksel, Elif S; Higgins, Peter D R; Di Sabatino, Antonio; de Bruyn, Jessica R; Rimola, Jordi; Brito, Jorge; Bettenworth, Dominik; van Assche, Gert; Bemelman, Willem; d'Hoore, Andre; Pellino, Gianluca; Dignass, Axel U

    2016-08-01

    This ECCO topical review of the European Crohn's and Colitis Organisation [ECCO] focused on prediction, diagnosis, and management of fibrostenosing Crohn's disease [CD]. The objective was to achieve evidence-supported, expert consensus that provides guidance for clinical practice. PMID:26928961

  2. Normal Tissue Complication Probability Estimation by the Lyman-Kutcher-Burman Method Does Not Accurately Predict Spinal Cord Tolerance to Stereotactic Radiosurgery

    SciTech Connect

    Daly, Megan E.; Luxton, Gary; Choi, Clara Y.H.; Gibbs, Iris C.; Chang, Steven D.; Adler, John R.; Soltys, Scott G.

    2012-04-01

    Purpose: To determine whether normal tissue complication probability (NTCP) analyses of the human spinal cord by use of the Lyman-Kutcher-Burman (LKB) model, supplemented by linear-quadratic modeling to account for the effect of fractionation, predict the risk of myelopathy from stereotactic radiosurgery (SRS). Methods and Materials: From November 2001 to July 2008, 24 spinal hemangioblastomas in 17 patients were treated with SRS. Of the tumors, 17 received 1 fraction with a median dose of 20 Gy (range, 18-30 Gy) and 7 received 20 to 25 Gy in 2 or 3 sessions, with cord maximum doses of 22.7 Gy (range, 17.8-30.9 Gy) and 22.0 Gy (range, 20.2-26.6 Gy), respectively. By use of conventional values for {alpha}/{beta}, volume parameter n, 50% complication probability dose TD{sub 50}, and inverse slope parameter m, a computationally simplified implementation of the LKB model was used to calculate the biologically equivalent uniform dose and NTCP for each treatment. Exploratory calculations were performed with alternate values of {alpha}/{beta} and n. Results: In this study 1 case (4%) of myelopathy occurred. The LKB model using radiobiological parameters from Emami and the logistic model with parameters from Schultheiss overestimated complication rates, predicting 13 complications (54%) and 18 complications (75%), respectively. An increase in the volume parameter (n), to assume greater parallel organization, improved the predictive value of the models. Maximum-likelihood LKB fitting of {alpha}/{beta} and n yielded better predictions (0.7 complications), with n = 0.023 and {alpha}/{beta} = 17.8 Gy. Conclusions: The spinal cord tolerance to the dosimetry of SRS is higher than predicted by the LKB model using any set of accepted parameters. Only a high {alpha}/{beta} value in the LKB model and only a large volume effect in the logistic model with Schultheiss data could explain the low number of complications observed. This finding emphasizes that radiobiological models

  3. Plasma viral RNA load predicts disease progression in accelerated feline immunodeficiency virus infection.

    PubMed Central

    Diehl, L J; Mathiason-Dubard, C K; O'Neil, L L; Hoover, E A

    1996-01-01

    Viral RNA load has been shown to indicate disease stage and predict the rapidity of disease progression in human immunodeficiency virus type 1 (HIV-1)-infected individuals. We had previously demonstrated that feline immunodeficiency virus (FIV) RNA levels in plasma correlate with disease stage in infected cats. Here we expand upon those observations by demonstrating that plasma virus load is 1 to 2 logs higher in cats with rapidly progressive FIV disease than in long-term survivors. Differences in plasma FIV RNA levels are evident by 1 to 2 weeks after infection and are consistent throughout infection. We also evaluated humoral immune responses in FIV-infected cats for correlation with survival times. Total anti-FIV antibody titers did not differ between cats with rapidly progressive FIV disease and long-term survivors. These findings indicate that virus replication plays an important role in FIV disease progression, as it does in HIV-1 disease progression. The parallels in virus loads and disease progressions between HIV-1 and FIV support the idea that the accelerated disease model is well suited for the study of therapeutic agents directed at reducing lentiviral replication. PMID:8642679

  4. Hemoglobin and Hematocrit Levels in the Prediction of Complicated Crohn's Disease Behavior – A Cohort Study

    PubMed Central

    Rieder, Florian; Paul, Gisela; Schnoy, Elisabeth; Schleder, Stephan; Wolf, Alexandra; Kamm, Florian; Dirmeier, Andrea; Strauch, Ulrike; Obermeier, Florian; Lopez, Rocio; Achkar, Jean-Paul; Rogler, Gerhard; Klebl, Frank

    2014-01-01

    Background Markers that predict the occurrence of a complicated disease behavior in patients with Crohn's disease (CD) can permit a more aggressive therapeutic regimen for patients at risk. The aim of this cohort study was to test the blood levels of hemoglobin (Hgb) and hematocrit (Hct) for the prediction of complicated CD behavior and CD related surgery in an adult patient population. Methods Blood samples of 62 CD patients of the German Inflammatory Bowel Disease-network “Kompetenznetz CED” were tested for the levels of Hgb and Hct prior to the occurrence of complicated disease behavior or CD related surgery. The relation of these markers and clinical events was studied using Kaplan-Meier survival analysis and adjusted COX-proportional hazard regression models. Results The median follow-up time was 55.8 months. Of the 62 CD patients without any previous complication or surgery 34% developed a complication and/or underwent CD related surgery. Low Hgb or Hct levels were independent predictors of a shorter time to occurrence of the first complication or CD related surgery. This was true for early as well as late occurring complications. Stable low Hgb or Hct during serial follow-up measurements had a higher frequency of complications compared to patients with a stable normal Hgb or Hct, respectively. Conclusions Determination of Hgb or Hct in complication and surgery naïve CD patients might serve as an additional tool for the prediction of complicated disease behavior. PMID:25116048

  5. Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

    PubMed Central

    Stephens, Christopher R.; Heau, Joaquín Giménez; González, Camila; Ibarra-Cerdeña, Carlos N.; Sánchez-Cordero, Victor; González-Salazar, Constantino

    2009-01-01

    Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases. PMID:19478956

  6. Predicting utility of a model for end stage liver disease in alcoholic liver disease

    PubMed Central

    Soultati, Aspasia S; Dourakis, Spyridon P; Alexopoulou, Alexandra; Deutsch, Melanie; Vasilieva, Larissa; Archimandritis, Athanasios J

    2006-01-01

    AIM: To validate the statistic utility of both the Maddrey Discriminant Function score and the Model for End-Stage Liver Disease as predictors of short term (30 d and 90 d) mortality in patients with alcoholic hepatitis and to assess prognostic factors among clinical characteristics and laboratory variables of patients with alcoholic hepatitis. METHODS: Thirty-four patients with the diagnosis of alcoholic hepatitis admitted to Hippokration University Hospital of Athens from 2000 to 2005 were assessed in the current retrospective study and a statistical analysis was conducted. RESULTS: 30- and 90-d mortality rates were reported at 5.9% (2/34) and 14.7% (5/34), respectively. Significant correlation was demonstrated for the Model for End-Stage Liver Disease (P30 = 0.094, P90 = 0.046) and the Maddrey Discriminant Function score (P30 = 0.033, P90 = 0.038) with 30- and 90-d mortality whereas a significant association was also established for alanine aminotransferase (P = 0.057), fibrin degradation products (P = 0.048) and C-reactive protein (P = 0.067) with 90-d mortality. For 30-d mortality the Area Under the Curve was 0.969 (95%CI: 0.902-1.036, P = 0.028) for the Model for End-Stage Liver Disease score and 0.984 (95%CI: 0.942-1.027, P = 0.023) for the Maddrey Discriminant Function score with the optimal cut off point of 30.5 (sensitivity 1, specificity 0.937) and 108.68 (sensitivity 1, specificity 0.969), respectively. Accordingly, for 90-d mortality the Area Under the Curve was 0.762 (95%CI: 0.559-0.965, P = 0.065) for the Model for End-Stage Liver Disease score and 0.752 (95%CI: 0.465-1.038, P = 0.076) for the Maddrey Discriminant Function score with the optimal cut off point of 19 (sensitivity 0.6, specificity 0.6) and 92 (sensitivity 0.6, specificity 0.946), respectively. The observed Kaplan Meier survival rates for different score-categories were compared with log-rank tests and higher score values were correlated with a lower survival. CONCLUSION: Equivalency of

  7. Commentary: Children and Predictive Genomic Testing: Disease Prevention, Research Protection, and Our Future

    PubMed Central

    Tercyak, Kenneth P.; Wilfond, Benjamin S.

    2011-01-01

    Genetic testing offered by direct-to-consumer companies—herein referred to as “predictive genomic testing”—has come under federal scrutiny. Critics claim testing yields uninterpretable and potentially harmful information. Supporters assert individuals have a right to this information, which could catalyze preventive health actions. Despite contentions that predictive genomic testing is a tool of primary disease prevention, little discussion has focused on its use with children. This partly stems from concerns expressed in existing professional guidelines about the potential for psychological and behavioral harm to children engendered by predictive genetic tests for Mendelian diseases. Conducting research to understand the actual benefits and harms is important for policy development and practice guidance and can be ethically justified within the pediatric regulatory framework of research that offers a prospect of direct benefit. Child health psychologists are well poised to contribute to this research effort, and promote the translation of genomic discoveries to improve pediatric medicine. PMID:21816897

  8. Predictive and preventive strategies to advance the treatments of cardiovascular and cerebrovascular diseases: the Ukrainian context

    PubMed Central

    2012-01-01

    Despite great efforts in treatments of cardiovascular diseases, the field requires innovative strategies because of high rates of morbidity, mortality and disability, indicating evident deficits in predictive vascular diagnosis and individualized treatment approaches. Talking about the vascular system, currently, physicians are not provided with integrated medical approaches to diagnose and treat vascular diseases. Only an individual global approach to the analysis of all segments in the vascular system of a patient allows finding the optimal way for vascular disease treatment. As for the existing methodology, there is a dominance of static methods such as X-ray contrast angiography and magnetic resonance imaging in angiomode. Taking into account the world experience, this article deals with innovative strategies, aiming at predictive diagnosis in vascular system, personalization of the biomedical treatment approaches, and targeted prevention of individual patient cohorts. Clinical examples illustrate the advances in corresponding healthcare sectors. Recommendations are provided to promote the field. PMID:23083430

  9. Prediction of disease relapses by multibiomarker disease activity and autoantibody status in patients with rheumatoid arthritis on tapering DMARD treatment

    PubMed Central

    Rech, Juergen; Hueber, Axel J; Finzel, Stephanie; Englbrecht, Matthias; Haschka, Judith; Manger, Bernhard; Kleyer, Arnd; Reiser, Michaela; Cobra, Jayme Fogagnolo; Figueiredo, Camille; Tony, Hans-Peter; Kleinert, Stefan; Wendler, Joerg; Schuch, Florian; Ronneberger, Monika; Feuchtenberger, Martin; Fleck, Martin; Manger, Karin; Ochs, Wolfgang; Schmitt-Haendle, Matthias; Lorenz, Hanns-Martin; Nuesslein, Hubert; Alten, Rieke; Henes, Joerg; Krueger, Klaus; Schett, Georg

    2016-01-01

    Objective To analyse the role of multibiomarker disease activity (MBDA) score in predicting disease relapses in patients with rheumatoid arthritis (RA) in sustained remission who tapered disease modifying antirheumatic drug (DMARD) therapy in RETRO, a prospective randomised controlled trial. Methods MBDA scores (scale 1–100) were determined based on 12 inflammation markers in baseline serum samples from 94 patients of the RETRO study. MBDA scores were compared between patients relapsing or remaining in remission when tapering DMARDs. Demographic and disease-specific parameters were included in multivariate logistic regression analysis for defining predictors of relapse. Results Moderate-to-high MBDA scores were found in 33% of patients with RA overall. Twice as many patients who relapsed (58%) had moderate/high MBDA compared with patients who remained in remission (21%). Baseline MBDA scores were significantly higher in patients with RA who were relapsing than those remaining in stable remission (N=94; p=0.0001) and those tapering/stopping (N=59; p=0.0001). Multivariate regression analysis identified MBDA scores as independent predictor for relapses in addition to anticitrullinated protein antibody (ACPA) status. Relapse rates were low (13%) in patients who were MBDA−/ACPA−, moderate in patients who were MBDA+/ACPA− (33.3%) and MBDA−ACPA+ (31.8%) and high in patients who were MBDA+/ACPA+ (76.4%). Conclusions MBDA improved the prediction of relapses in patients with RA in stable remission undergoing DMARD tapering. If combined with ACPA testing, MBDA allowed prediction of relapse in more than 80% of the patients. Trial registration number EudraCT 2009-015740-42. PMID:26483255

  10. Lost in translation: preclinical studies on 3,4-methylenedioxymethamphetamine provide information on mechanisms of action, but do not allow accurate prediction of adverse events in humans

    PubMed Central

    Green, AR; King, MV; Shortall, SE; Fone, KCF

    2012-01-01

    3,4-Methylenedioxymethamphetamine (MDMA) induces both acute adverse effects and long-term neurotoxic loss of brain 5-HT neurones in laboratory animals. However, when choosing doses, most preclinical studies have paid little attention to the pharmacokinetics of the drug in humans or animals. The recreational use of MDMA and current clinical investigations of the drug for therapeutic purposes demand better translational pharmacology to allow accurate risk assessment of its ability to induce adverse events. Recent pharmacokinetic studies on MDMA in animals and humans are reviewed and indicate that the risks following MDMA ingestion should be re-evaluated. Acute behavioural and body temperature changes result from rapid MDMA-induced monoamine release, whereas long-term neurotoxicity is primarily caused by metabolites of the drug. Therefore acute physiological changes in humans are fairly accurately mimicked in animals by appropriate dosing, although allometric dosing calculations have little value. Long-term changes require MDMA to be metabolized in a similar manner in experimental animals and humans. However, the rate of metabolism of MDMA and its major metabolites is slower in humans than rats or monkeys, potentially allowing endogenous neuroprotective mechanisms to function in a species specific manner. Furthermore acute hyperthermia in humans probably limits the chance of recreational users ingesting sufficient MDMA to produce neurotoxicity, unlike in the rat. MDMA also inhibits the major enzyme responsible for its metabolism in humans thereby also assisting in preventing neurotoxicity. These observations question whether MDMA alone produces long-term 5-HT neurotoxicity in human brain, although when taken in combination with other recreational drugs it may induce neurotoxicity. LINKED ARTICLES This article is commented on by Parrott, pp. 1518–1520 of this issue. To view this commentary visit http://dx.doi.org/10.1111/j.1476-5381.2012.01941.x and to view the the

  11. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults

    PubMed Central

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-01-01

    Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (P<0.01). These results were consistent by sex. Conclusions These findings highlight potential shortcomings of using short-term risk tools for primary prevention strategies because a substantial proportion of Peruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303

  12. The CUPIC algorithm: an accurate model for the prediction of sustained viral response under telaprevir or boceprevir triple therapy in cirrhotic patients.

    PubMed

    Boursier, J; Ducancelle, A; Vergniol, J; Veillon, P; Moal, V; Dufour, C; Bronowicki, J-P; Larrey, D; Hézode, C; Zoulim, F; Fontaine, H; Canva, V; Poynard, T; Allam, S; De Lédinghen, V

    2015-12-01

    Triple therapy using boceprevir or telaprevir remains the reference treatment for genotype 1 chronic hepatitis C in countries where new interferon-free regimens have not yet become available. Antiviral treatment is highly required in cirrhotic patients, but they represent a difficult-to-treat population. We aimed to develop a simple algorithm for the prediction of sustained viral response (SVR) in cirrhotic patients treated with triple therapy. A total of 484 cirrhotic patients from the ANRS CO20 CUPIC cohort treated with triple therapy were randomly distributed into derivation and validation sets. A total of 52.1% of patients achieved SVR. In the derivation set, a D0 score for the prediction of SVR before treatment initiation included the following independent predictors collected at day 0: prior treatment response, gamma-GT, platelets, telaprevir treatment, viral load. To refine the prediction at the early phase of the treatment, a W4 score included as additional parameter the viral load collected at week 4. The D0 and W4 scores were combined in the CUPIC algorithm defining three subgroups: 'no treatment initiation or early stop at week 4', 'undetermined' and 'SVR highly probable'. In the validation set, the rates of SVR in these three subgroups were, respectively, 11.1%, 50.0% and 82.2% (P < 0.001). By replacing the variable 'prior treatment response' with 'IL28B genotype', another algorithm was derived for treatment-naïve patients with similar results. The CUPIC algorithm is an easy-to-use tool that helps physicians weigh their decision between immediately treating cirrhotic patients using boceprevir/telaprevir triple therapy or waiting for new drugs to become available in their country. PMID:26216230

  13. Development of a disease risk prediction model for downy mildew (Peronospora sparsa) in boysenberry.

    PubMed

    Kim, Kwang Soo; Beresford, Robert M; Walter, Monika

    2014-01-01

    Downy mildew caused by Peronospora sparsa has resulted in serious production losses in boysenberry (Rubus hybrid), blackberry (Rubus fruticosus), and rose (Rosa sp.) in New Zealand, Mexico, and the United States and the United Kingdom, respectively. Development of a model to predict downy mildew risk would facilitate development and implementation of a disease warning system for efficient fungicide spray application in the crops affected by this disease. Because detailed disease observation data were not available, a two-step approach was applied to develop an empirical risk prediction model for P. sparsa. To identify the weather patterns associated with a high incidence of downy mildew berry infections (dryberry disease) and derive parameters for the empirical model, classification and regression tree (CART) analysis was performed. Then, fuzzy sets were applied to develop a simple model to predict the disease risk based on the parameters derived from the CART analysis. High-risk seasons with a boysenberry downy mildew incidence >10% coincided with months when the number of hours per day with temperature of 15 to 20°C averaged >9.8 over the month and the number of days with rainfall in the month was >38.7%. The Fuzzy Peronospora Sparsa (FPS) model, developed using fuzzy sets, defined relationships among high-risk events, temperature, and rainfall conditions. In a validation study, the FPS model provided correct identification of both seasons with high downy mildew risk for boysenberry, blackberry, and rose and low risk in seasons when no disease was observed. As a result, the FPS model had a significant degree of agreement between predicted and observed risks of downy mildew for those crops (P = 0.002). PMID:23883152

  14. Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease

    PubMed Central

    Pavlides, Michael; Banerjee, Rajarshi; Sellwood, Joanne; Kelly, Catherine J.; Robson, Matthew D.; Booth, Jonathan C.; Collier, Jane; Neubauer, Stefan; Barnes, Eleanor

    2016-01-01

    Background & Aims Multiparametric magnetic resonance (MR) imaging has been demonstrated to quantify hepatic fibrosis, iron, and steatosis. The aim of this study was to determine if MR can be used to predict negative clinical outcomes in liver disease patients. Methods Patients with chronic liver disease (n = 112) were recruited for MR imaging and data on the development of liver related clinical events were collected by medical records review. The median follow-up was 27 months. MR data were analysed blinded for the Liver Inflammation and Fibrosis score (LIF; <1, 1–1.99, 2–2.99, and ⩾3 representing normal, mild, moderate, and severe liver disease, respectively), T2∗ for liver iron content and proportion of liver fat. Baseline liver biopsy was performed in 102 patients. Results Liver disease aetiologies included non-alcoholic fatty liver disease (35%) and chronic viral hepatitis (30%). Histologically, fibrosis was mild in 54 (48%), moderate in 17 (15%), and severe in 31 (28%) patients. Overall mortality was 5%. Ten patients (11%) developed at least one liver related clinical event. The negative predictive value of LIF <2 was 100%. Two patients with LIF 2–2.99 and eight with LIF ⩾3 had a clinical event. Patients with LIF ⩾3 had a higher cumulative risk for developing clinical events, compared to those with LIF <1 (p = 0.02) and LIF 1–1.99 (p = 0.03). Cox regression analysis including all 3 variables (fat, iron, LIF) resulted in an enhanced LIF predictive value. Conclusions Non-invasive standardised multiparametric MR technology may be used to predict clinical outcomes in patients with chronic liver disease. PMID:26471505

  15. Alternative Dietary Indices Both Strongly Predict Risk of Chronic Disease123

    PubMed Central

    Chiuve, Stephanie E.; Fung, Teresa T.; Rimm, Eric B.; Hu, Frank B.; McCullough, Marjorie L.; Wang, Molin; Stampfer, Meir J.; Willett, Walter C.

    2012-01-01

    The Healthy Eating Index-2005 (HEI-2005) measures adherence to the 2005 Dietary Guidelines for Americans, but the association between the HEI-2005 and risk of chronic disease is not known. The Alternative Healthy Eating Index (AHEI), which is based on foods and nutrients predictive of chronic disease risk, was associated inversely with chronic disease risk previously. We updated the AHEI, including additional dietary factors involved in the development of chronic disease, and assessed the associations between the AHEI-2010 and the HEI-2005 and risk of major chronic disease prospectively among 71,495 women from the Nurses’ Health Study and 41,029 men from the Health Professionals Follow-Up Study who were free of chronic disease at baseline. During ≥24 y of follow-up, we documented 26,759 and 15,558 incident chronic diseases (cardiovascular disease, diabetes, cancer, or nontrauma death) among women and men, respectively. The RR (95% CI) of chronic disease comparing the highest with the lowest quintile was 0.84 (0.81, 0.87) for the HEI-2005 and 0.81 (0.77, 0.85) for the AHEI-2010. The AHEI-2010 and HEI-2005 were most strongly associated with coronary heart disease (CHD) and diabetes, and for both outcomes the AHEI-2010 was more strongly associated with risk than the HEI-2005 (P-difference = 0.002 and <0.001, respectively). The 2 indices were similarly associated with risk of stroke and cancer. These findings suggest that closer adherence to the 2005 Dietary Guidelines may lower risk of major chronic disease. However, the AHEI-2010, which included additional dietary information, was more strongly associated with chronic disease risk, particularly CHD and diabetes. PMID:22513989

  16. Ab initio molecular dynamics of liquid water using embedded-fragment second-order many-body perturbation theory towards its accurate property prediction.

    PubMed

    Willow, Soohaeng Yoo; Salim, Michael A; Kim, Kwang S; Hirata, So

    2015-01-01

    A direct, simultaneous calculation of properties of a liquid using an ab initio electron-correlated theory has long been unthinkable. Here we present structural, dynamical, and response properties of liquid water calculated by ab initio molecular dynamics using the embedded-fragment spin-component-scaled second-order many-body perturbation method with the aug-cc-pVDZ basis set. This level of theory is chosen as it accurately and inexpensively reproduces the water dimer potential energy surface from the coupled-cluster singles, doubles, and noniterative triples with the aug-cc-pVQZ basis set, which is nearly exact. The calculated radial distribution function, self-diffusion coefficient, coordinate number, and dipole moment, as well as the infrared and Raman spectra are in excellent agreement with experimental results. The shapes and widths of the OH stretching bands in the infrared and Raman spectra and their isotropic-anisotropic Raman noncoincidence, which reflect the diverse local hydrogen-bond environment, are also reproduced computationally. The simulation also reveals intriguing dynamic features of the environment, which are difficult to probe experimentally, such as a surprisingly large fluctuation in the coordination number and the detailed mechanism by which the hydrogen donating water molecules move across the first and second shells, thereby causing this fluctuation. PMID:26400690

  17. Ab initio molecular dynamics of liquid water using embedded-fragment second-order many-body perturbation theory towards its accurate property prediction

    PubMed Central

    Willow, Soohaeng Yoo; Salim, Michael A.; Kim, Kwang S.; Hirata, So

    2015-01-01

    A direct, simultaneous calculation of properties of a liquid using an ab initio electron-correlated theory has long been unthinkable. Here we present structural, dynamical, and response properties of liquid water calculated by ab initio molecular dynamics using the embedded-fragment spin-component-scaled second-order many-body perturbation method with the aug-cc-pVDZ basis set. This level of theory is chosen as it accurately and inexpensively reproduces the water dimer potential energy surface from the coupled-cluster singles, doubles, and noniterative triples with the aug-cc-pVQZ basis set, which is nearly exact. The calculated radial distribution function, self-diffusion coefficient, coordinate number, and dipole moment, as well as the infrared and Raman spectra are in excellent agreement with experimental results. The shapes and widths of the OH stretching bands in the infrared and Raman spectra and their isotropic-anisotropic Raman noncoincidence, which reflect the diverse local hydrogen-bond environment, are also reproduced computationally. The simulation also reveals intriguing dynamic features of the environment, which are difficult to probe experimentally, such as a surprisingly large fluctuation in the coordination number and the detailed mechanism by which the hydrogen donating water molecules move across the first and second shells, thereby causing this fluctuation. PMID:26400690

  18. Stable, high-order SBP-SAT finite difference operators to enable accurate simulation of compressible turbulent flows on curvilinear grids, with application to predicting turbulent jet noise

    NASA Astrophysics Data System (ADS)

    Byun, Jaeseung; Bodony, Daniel; Pantano, Carlos

    2014-11-01

    Improved order-of-accuracy discretizations often require careful consideration of their numerical stability. We report on new high-order finite difference schemes using Summation-By-Parts (SBP) operators along with the Simultaneous-Approximation-Terms (SAT) boundary condition treatment for first and second-order spatial derivatives with variable coefficients. In particular, we present a highly accurate operator for SBP-SAT-based approximations of second-order derivatives with variable coefficients for Dirichlet and Neumann boundary conditions. These terms are responsible for approximating the physical dissipation of kinetic and thermal energy in a simulation, and contain grid metrics when the grid is curvilinear. Analysis using the Laplace transform method shows that strong stability is ensured with Dirichlet boundary conditions while weaker stability is obtained for Neumann boundary conditions. Furthermore, the benefits of the scheme is shown in the direct numerical simulation (DNS) of a Mach 1.5 compressible turbulent supersonic jet using curvilinear grids and skew-symmetric discretization. Particularly, we show that the improved methods allow minimization of the numerical filter often employed in these simulations and we discuss the qualities of the simulation.

  19. Health at the ballot box: disease threat does not predict attractiveness preference in British politicians

    PubMed Central

    Renberg, Adam

    2016-01-01

    According to disease avoidance theory, selective pressures have shaped adaptive behaviours to avoid people who might transmit infections. Such behavioural immune defence strategies may have social and societal consequences. Attractiveness is perceived as a heuristic cue of good health, and the relative importance of attractiveness is predicted to increase during high disease threat. Here, we investigated whether politicians' attractiveness is more important for electoral success when disease threat is high, in an effort to replicate earlier findings from the USA. We performed a cross-sectional study of 484 members of the House of Commons from England and Wales. Publicly available sexiness ratings (median 5883 ratings/politician) were regressed on measures of disease burden, operationalized as infant mortality, life expectancy and self-rated health. Infant mortality in parliamentary constituencies did not significantly predict sexiness of elected members of parliament (p = 0.08), nor did life expectancy (p = 0.06), nor self-rated health (p = 0.55). Subsample analyses failed to provide further support for the hypothesis. In conclusion, an attractive leader effect was not amplified by disease threat in the UK and these results did not replicate those of earlier studies from the USA concerning the relationship between attractiveness, disease threat and voting preference. PMID:27069671

  20. From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine

    PubMed Central

    2013-01-01

    With the significant advancement of high-throughput technologies and diagnostic techniques throughout the past decades, molecular underpinnings of many disorders have been identified. However, translation of patient-specific molecular mechanisms into tailored clinical applications remains a challenging task, which requires integration of multi-dimensional molecular and clinical data into patient-centric models. This task becomes even more challenging when dealing with complex diseases such as neurodegenerative disorders. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. We argue that integrative disease modeling will be an indispensable part of any P4 medicine research and development in the near future and that it supports the shift from descriptive to causal mechanistic diagnosis and treatment of complex diseases. For each ‘P’ in predictive, preventive, personalized and participatory (P4) medicine, we demonstrate how integrative disease modeling can contribute to addressing the real-world issues in development of new predictive, preventive, personalized and participatory measures. With the increasing recognition that application of integrative systems modeling is the key to all activities in P4 medicine, we envision that translational bioinformatics in general and integrative modeling in particular will continue to open up new avenues of scientific research for current challenges in P4 medicine. PMID:24195840

  1. Health at the ballot box: disease threat does not predict attractiveness preference in British politicians.

    PubMed

    Nilsonne, Gustav; Renberg, Adam; Tamm, Sandra; Lekander, Mats

    2016-03-01

    According to disease avoidance theory, selective pressures have shaped adaptive behaviours to avoid people who might transmit infections. Such behavioural immune defence strategies may have social and societal consequences. Attractiveness is perceived as a heuristic cue of good health, and the relative importance of attractiveness is predicted to increase during high disease threat. Here, we investigated whether politicians' attractiveness is more important for electoral success when disease threat is high, in an effort to replicate earlier findings from the USA. We performed a cross-sectional study of 484 members of the House of Commons from England and Wales. Publicly available sexiness ratings (median 5883 ratings/politician) were regressed on measures of disease burden, operationalized as infant mortality, life expectancy and self-rated health. Infant mortality in parliamentary constituencies did not significantly predict sexiness of elected members of parliament (p = 0.08), nor did life expectancy (p = 0.06), nor self-rated health (p = 0.55). Subsample analyses failed to provide further support for the hypothesis. In conclusion, an attractive leader effect was not amplified by disease threat in the UK and these results did not replicate those of earlier studies from the USA concerning the relationship between attractiveness, disease threat and voting preference. PMID:27069671

  2. Comparison of family history and SNPs for predicting risk of complex disease.

    PubMed

    Do, Chuong B; Hinds, David A; Francke, Uta; Eriksson, Nicholas

    2012-01-01

    The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)-based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%-30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history-based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP-based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis. PMID:23071447

  3. Accurate prediction of diradical chemistry from a single-reference density-matrix method: Model application to the bicyclobutane to gauche-1,3-butadiene isomerization

    SciTech Connect

    Bertels, Luke W.; Mazziotti, David A.

    2014-07-28

    Multireference correlation in diradical molecules can be captured by a single-reference 2-electron reduced-density-matrix (2-RDM) calculation with only single and double excitations in the 2-RDM parametrization. The 2-RDM parametrization is determined by N-representability conditions that are non-perturbative in their treatment of the electron correlation. Conventional single-reference wave function methods cannot describe the entanglement within diradical molecules without employing triple- and potentially even higher-order excitations of the mean-field determinant. In the isomerization of bicyclobutane to gauche-1,3-butadiene the parametric 2-RDM (p2-RDM) method predicts that the diradical disrotatory transition state is 58.9 kcal/mol above bicyclobutane. This barrier is in agreement with previous multireference calculations as well as recent Monte Carlo and higher-order coupled cluster calculations. The p2-RDM method predicts the Nth natural-orbital occupation number of the transition state to be 0.635, revealing its diradical character. The optimized geometry from the p2-RDM method differs in important details from the complete-active-space self-consistent-field geometry used in many previous studies including the Monte Carlo calculation.

  4. Prediction of miRNA-disease associations with a vector space model

    PubMed Central

    Pasquier, Claude; Gardès, Julien

    2016-01-01

    MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases. PMID:27246786

  5. Prediction of miRNA-disease associations with a vector space model.

    PubMed

    Pasquier, Claude; Gardès, Julien

    2016-01-01

    MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases. PMID:27246786

  6. Coronary flow reserve estimated by positron emission tomography to diagnose significant coronary artery disease and predict cardiac events.

    PubMed

    Naya, Masanao; Tamaki, Nagara; Tsutsui, Hiroyuki

    2015-01-01

    Coronary artery disease (CAD) is a major cause of death in Japan. Coronary angiography is useful to assess the atherosclerotic burden in CAD patients, but its ability to predict whether patients will respond favorably to optimal medical therapy and revascularization is limited. The measurement of the fractional flow reserve with angiography is a well-validated method for identifying ischemic vessels. However, neither an anatomical assessment nor a functional assessment can delineate microvasculature or estimate its function. The quantitative coronary flow reserve (CFR) estimated from sequential myocardial perfusion images obtained by positron emission tomography (PET) during stress provides an accurate index of hyperemic reactivity to vasodilatory agents in the myocardium. In fact, there is growing evidence that the CFR reflects disease activity in the entire coronary circulation, including epicardial coronary artery stenosis, diffuse atherosclerosis, and microvascular dilatory function. Importantly, reduced CFR is observed even in patients without flow-limiting coronary stenosis, and its evaluation can improve the risk stratification of patients at any stage of CAD. This review focuses on the application of CFR estimated by cardiac PET for the diagnosis and risk stratification of patients with CAD. PMID:25744627

  7. Is scoring system of computed tomography based metric parameters can accurately predicts shock wave lithotripsy stone-free rates and aid in the development of treatment strategies?

    PubMed Central

    Badran, Yasser Ali; Abdelaziz, Alsayed Saad; Shehab, Mohamed Ahmed; Mohamed, Hazem Abdelsabour Dief; Emara, Absel-Aziz Ali; Elnabtity, Ali Mohamed Ali; Ghanem, Maged Mohammed; ELHelaly, Hesham Abdel Azim

    2016-01-01

    Objective: The objective was to determine the predicting success of shock wave lithotripsy (SWL) using a combination of computed tomography based metric parameters to improve the treatment plan. Patients and Methods: Consecutive 180 patients with symptomatic upper urinary tract calculi 20 mm or less were enrolled in our study underwent extracorporeal SWL were divided into two main groups, according to the stone size, Group A (92 patients with stone ≤10 mm) and Group B (88 patients with stone >10 mm). Both groups were evaluated, according to the skin to stone distance (SSD) and Hounsfield units (≤500, 500–1000 and >1000 HU). Results: Both groups were comparable in baseline data and stone characteristics. About 92.3% of Group A rendered stone-free, whereas 77.2% were stone-free in Group B (P = 0.001). Furthermore, in both group SWL success rates was a significantly higher for stones with lower attenuation <830 HU than with stones >830 HU (P < 0.034). SSD were statistically differences in SWL outcome (P < 0.02). Simultaneous consideration of three parameters stone size, stone attenuation value, and SSD; we found that stone-free rate (SFR) was 100% for stone attenuation value <830 HU for stone <10 mm or >10 mm but total number SWL sessions and shock waves required for the larger stone group were higher than in the smaller group (P < 0.01). Furthermore, SFR was 83.3% and 37.5% for stone <10 mm, mean HU >830, SSD 90 mm and SSD >120 mm, respectively. On the other hand, SFR was 52.6% and 28.57% for stone >10 mm, mean HU >830, SSD <90 mm and SSD >120 mm, respectively. Conclusion: Stone size, stone density (HU), and SSD is simple to calculate and can be reported by radiologists to applying combined score help to augment predictive power of SWL, reduce cost, and improving of treatment strategies. PMID:27141192

  8. Identification of predictive biomarkers of disease state in transition dairy cows.

    PubMed

    Hailemariam, D; Mandal, R; Saleem, F; Dunn, S M; Wishart, D S; Ametaj, B N

    2014-05-01

    In dairy cows, periparturient disease states, such as metritis, mastitis, and laminitis, are leading to increasingly significant economic losses for the dairy industry. Treatments for these pathologies are often expensive, ineffective, or not cost-efficient, leading to production losses, high veterinary bills, or early culling of the cows. Early diagnosis or detection of these conditions before they manifest themselves could lower their incidence, level of morbidity, and the associated economic losses. In an effort to identify predictive biomarkers for postpartum or periparturient disease states in dairy cows, we undertook a cross-sectional and longitudinal metabolomics study to look at plasma metabolite levels of dairy cows during the transition period, before and after becoming ill with postpartum diseases. Specifically we employed a targeted quantitative metabolomics approach that uses direct flow injection mass spectrometry to track the metabolite changes in 120 different plasma metabolites. Blood plasma samples were collected from 12 dairy cows at 4 time points during the transition period (-4 and -1 wk before and 1 and 4 wk after parturition). Out of the 12 cows studied, 6 developed multiple periparturient disorders in the postcalving period, whereas the other 6 remained healthy during the entire experimental period. Multivariate data analysis (principal component analysis and partial least squares discriminant analysis) revealed a clear separation between healthy controls and diseased cows at all 4 time points. This analysis allowed us to identify several metabolites most responsible for separating the 2 groups, especially before parturition and the start of any postpartum disease. Three metabolites, carnitine, propionyl carnitine, and lysophosphatidylcholine acyl C14:0, were significantly elevated in diseased cows as compared with healthy controls as early as 4 wk before parturition, whereas 2 metabolites, phosphatidylcholine acyl-alkyl C42:4 and

  9. Predictive diagnostics and personalized medicine for the prevention of chronic degenerative diseases

    PubMed Central

    2010-01-01

    Progressive increase of mean age and life expectancy in both industrialized and emerging societies parallels an increment of chronic degenerative diseases (CDD) such as cancer, cardiovascular, autoimmune or neurodegenerative diseases among the elderly. CDD are of complex diagnosis, difficult to treat and absorbing an increasing proportion in the health care budgets worldwide. However, recent development in modern medicine especially in genetics, proteomics, and informatics is leading to the discovery of biomarkers associated with different CDD that can be used as indicator of disease’s risk in healthy subjects. Therefore, predictive medicine is merging and medical doctors may for the first time anticipate the deleterious effect of CDD and use markers to identify persons with high risk of developing a given CDD before the clinical manifestation of the diseases. This innovative approach may offer substantial advantages, since the promise of personalized medicine is to preserve individual health in people with high risk by starting early treatment or prevention protocols. The pathway is now open, however the road to an effective personalized medicine is still long, several (diagnostic) predictive instruments for different CDD are under development, some ethical issues have to be solved. Operative proposals for the heath care systems are now needed to verify potential benefits of predictive medicine in the clinical practice. In fact, predictive diagnostics, personalized medicine and personalized therapy have the potential of changing classical approaches of modern medicine to CDD. PMID:21172060

  10. Low acute hematological toxicity during chemotherapy predicts reduced disease control in advanced Hodgkin's disease.

    PubMed

    Brosteanu, O; Hasenclever, D; Loeffler, M; Diehl, V

    2004-03-01

    Chemotherapy-treated patients with advanced Hodgkin's disease (HD) differ considerably in acute hematotoxicity. Hematotoxicity may be indicative of pharmacological and metabolic heterogeneity. We hypothesized that low hematotoxicity might correlate with reduced systemic dose and thus reduced disease control. A total of 266 patients with advanced HD treated with cyclophosphamide, vincristine, procarbazine, prednisone, doxorubicin, bleomycin, vinblastine, and dacarbazine (COPP-ABVD) were analyzed (HD6 trial of the German Hodgkin's Lymphoma Study Group). The reported WHO grade of leukocy