Best Accuracy (best + accuracy)

Distribution by Scientific Domains

Selected Abstracts


Wenjie Li
The existence of structural ambiguity in modifying clauses renders noun phrase (NP) extraction from running Chinese texts complicated. It is shown from previous experiments that nearly 33% of the errors in an NP extractor were actually caused by the use of clause modifiers. For example, consider the sequence "V + NP1+ (of) + NP0." It can be interpreted as two alternatives, a verb phrase (i.e., [V[NP1++ NP0]NP]VP) or a noun phrase (i.e., [[V NP1]VP++ NP0]NP). To resolve this ambiguity, syntactical, contextual, and semantics-based approaches are investigated in this article. The conclusion is that the problem can be overcome only when the semantic knowledge about words is adopted. Therefore, a structural disambiguation algorithm based on lexical association is proposed. The algorithm uses the semantic class relation between a word pair derived from a standard Chinese thesaurus, , to work out whether a noun phrase or a verb phrase has a stronger lexical association within the collocation. This can, in turn, determine the intended phrase structure. With the proposed algorithm, the best accuracy and coverage are 79% and 100%, respectively. The experiment also shows that the backed-off model is more effective for this purpose. With this disambiguation algorithm, parsing performance can be significantly improved. [source]

PET-CT vs contrast-enhanced CT: What is the role for each after chemoradiation for advanced oropharyngeal cancer?,

Amy Y. Chen MD
Abstract Purpose. The aim of our study was to assess the utility of positron emission tomography (PET) and 2 fluoro-2-deoxy- D -glucose coupled with neck CT compared with contrast-enhanced CT in predicting persistent cancer either at the primary site or cervical lymphatics in patients with oropharyngeal cancer treated with concurrent chemoradiation Methods. Thirty consecutive patients underwent clinical examination, PET-CT, and contrast-enhanced CT to assess response after the completion of the treatment. The outcome variable was positive tissue diagnosis or negative disease at 6 months. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the primary site as well as cervical disease. Results. Contrast-enhanced CT alone showed the best accuracy in detecting disease at the primary site after treatment (85.7%). Accuracy in evaluating residual tumor in the cervical lymphatics for contrast-enhanced CT and PET-CT was 59.3% and 74.1%, respectively. For evaluating the neck, PET-CT and contrast-enhanced CT demonstrated 100% NPV, but the PPV was 36.3% and 26.6%, respectively. Conclusions. In this preliminary study, PET-CT seems to be superior to contrast-enhanced CT in predicting persistent disease in the neck after chemoradiation for oropharyngeal or unknown primary cancer, but not at the primary site. However, the possibility of a false-positive result in the neck remains high, and thus overtreatment may result. Even more concerning are the false-negative results. Larger, prospective studies will be important in defining the role of PET-CT in obviating the need for salvage neck dissections after chemoradiation. © 2006 Wiley Periodicals, Inc. Head Neck 28:487,495, 2006 [source]

Prediction of pKa shifts in proteins using a combination of molecular mechanical and continuum solvent calculations

Bernd Kuhn
Abstract The prediction of pKa shifts of ionizable groups in proteins is of great relevance for a number of important biological phenomena. We present an implementation of the MM-GBSA approach, which combines molecular mechanical (MM) and generalized Born (GB) continuum solvent energy terms, to the calculation of pKa values of a panel of nine proteins, including 69 individual comparisons with experiment. While applied so far mainly to the calculation of biomolecular binding free energies, we show that this method can also be used for the estimation of protein pKa shifts, with an accuracy around 1 pKa unit, even for strongly shifted residues. Our analysis reveals that the nonelectrostatic terms that are part of the MM-GBSA free energy expression are important contributors to improved prediction accuracy. This suggests that most of the previous approaches that focus only on electrostatic interactions could be improved by adding other nonpolar energy terms to their free energy expression. Interestingly, our method yields best accuracy at protein dielectric constants of ,int = 2,4, which is in contrast to previous approaches that peak at higher ,int , 8. An important component of our procedure is an intermediate minimization step of each protonation state involving different rotamers and tautomers as a way to explicitly model protein relaxation upon (de)protonation. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1865,1872, 2004 [source]

Prognostic models in cirrhotics admitted to intensive care units better predict outcome when assessed at 48 h after admission

Evangelos Cholongitas
Abstract Background and Aim:, The accuracy of prognostic models in critically ill cirrhotics at admission to intensive care units (ICU) may be unreliable. Predictive accuracy could be improved by evaluating changes over time, but this has not been published. The aim of the present study was to assess the performance of prognostic models in cirrhotics at admission (baseline) and at 48 h to predict mortality in the ICU or within 6 weeks after discharge from the ICU. Methods:, One hundred and twenty-eight cirrhotics (77 males, mean age 49 ± 11.3 years) were consecutively admitted and alive 48 h after admission with 89% on mechanical ventilation, 76% on inotrope support, and 42% with renal failure. Prognostic models used were Child-Turcotte-Pugh (CTP), Model for End-stage Liver Disease (MELD), Acute Physiology and Chronic Health Evaluation (APACHE) II, Sequential Organ Failure Assessment (SOFA), failing organ systems (FOS) at baseline and at 48 h, ,score (difference between baseline and at 48 h) and the mean score (MN , score admission + 48 h/2) which were compared by area under the receiver operating characteristic curves (AUC). Results:, Mortality was 54.7% (n = 70) due to multiple organ failure in 55%. CTP, MELD, APACHE II, SOFA and FOS performed better at 48 h (AUC: 0.78, 0.86, 0.78, 0.88 and 0.85, respectively) than at baseline (AUC: 0.75, 0.78, 0.75, 0.81 and 0.79, respectively). The mean score had better discrimination than the baseline score; the ,score had poor predictive ability (AUC < 0.70). SOFA score (48 h: 0.88, mean: 0.88) and FOS (mean: 0.88) had the best accuracy, with a SOFA and MN-SOFA , 10 predicting mortality in 93% and 91%, respectively, and MN-FOS , 1.5 in 98%. Conclusions:, In cirrhotics, prognostic scores in the ICU at 48 h had better discrimination than baseline scores for short-term mortality. SOFA and FOS models had the best performance. [source]

Computational identification of altered metabolism using gene expression and metabolic pathways

Hojung Nam
Abstract Understanding altered metabolism is an important issue because altered metabolism is often revealed as a cause or an effect in pathogenesis. It has also been shown to be an important factor in the manipulation of an organism's metabolism in metabolic engineering. Unfortunately, it is not yet possible to measure the concentration levels of all metabolites in the genome-wide scale of a metabolic network; consequently, a method that infers the alteration of metabolism is beneficial. The present study proposes a computational method that identifies genome-wide altered metabolism by analyzing functional units of KEGG pathways. As control of a metabolic pathway is accomplished by altering the activity of at least one rate-determining step enzyme, not all gene expressions of enzymes in the pathway demonstrate significant changes even if the pathway is altered. Therefore, we measure the alteration levels of a metabolic pathway by selectively observing expression levels of significantly changed genes in a pathway. The proposed method was applied to two strains of Saccharomyces cerevisiae gene expression profiles measured in very high-gravity (VHG) fermentation. The method identified altered metabolic pathways whose properties are related to ethanol and osmotic stress responses which had been known to be observed in VHG fermentation because of the high sugar concentration in growth media and high ethanol concentration in fermentation products. With the identified altered pathways, the proposed method achieved best accuracy and sensitivity rates for the Red Star (RS) strain compared to other three related studies (gene-set enrichment analysis (GSEA), significance analysis of microarray to gene set (SAM-GS), reporter metabolite), and for the CEN.PK 113-7D (CEN) strain, the proposed method and the GSEA method showed comparably similar performances. Biotechnol. Bioeng. 2009;103: 835,843. © 2009 Wiley Periodicals, Inc. [source]

Sizing of Safety Valves Using ANSYS CFX-Flo®

D. Moncalvo
Abstract This work discusses the effect of the degree of fineness of the flow volume discretization and that of the turbulence model on the accuracy of reproduction of air mass flow rates in two safety valves using the CFD software ANSYS Flo®. Calculations show that the degree of fineness of the discretization is the decisive factor affecting the exactness of the calculations and that the best reproduction is achieved with grids where at least two cells are built on the smallest edge. The selection of the turbulence model has by far in comparison a lower impact; however, the best accuracy is obtained using the standard k - , model and the SST modification of Menter. [source]