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Successful Prediction (successful + prediction)
Selected AbstractsInfants' Evolving Representations of Object Motion During Occlusion: A Longitudinal Study of 6- to 12-Month-Old InfantsINFANCY, Issue 2 2004Gustaf Gredebäck Infants' ability to track temporarily occluded objects that moved on circular trajectories was investigated in 20 infants using a longitudinal design. They were first seen at 6 months and then every 2nd month until the end of their 1st year. Infants were presented with occlusion events covering 20% of the target's trajectory (effective occlusion interval ranged from 500,4,000 msec). Gaze was measured using an ASL 504 infrared eye-tracking system. Results effectively demonstrate that infants from 6 months of age can represent the spatiotemporal dynamics of occluded objects. Infants at all ages tested were able to predict, under certain conditions, when and where the object would reappear after occlusion. They moved gaze accurately to the position where the object was going to reappear and scaled their timing to the current occlusion duration. The average rate of predictive gaze crossings increased with occlusion duration. These results are discussed as a 2-factor process. Successful predictions are dependent on strong representations, themselves dependent on the richness of information available during encoding and graded representations. [source] Method for predicting solubilities of solids in mixed solventsAICHE JOURNAL, Issue 5 2009Martin E. Ellegaard Abstract A method is presented for predicting solubilities of solid solutes in mixed solvents, based on excess Henry's law constants. The basis is statistical mechanical fluctuation solution theory for composition derivatives of solute/solvent infinite dilution activity coefficients. Suitable approximations are made for a single parameter characterizing solute/solvent interactions. Comparisons with available data show that the method is successful in describing a variety of observed mixed solvent solubility behavior, including nearly ideal systems with small excess solubilities, systems with solute-independent excess solubilities, and systems deviating from these simple rules. Successful predictions for new solvent mixtures can be made using limited data from other mixtures. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Interrill erosion on cultivated Greek soils: modelling sediment deliveryEARTH SURFACE PROCESSES AND LANDFORMS, Issue 8 2006D. Dimoyiannis Abstract For interrill erosion, raindrop-induced detachment and transport of sediment by rainfall-disturbed sheet flow are the predominant processes, while detachment by sheet flow and transport by raindrop impact are negligible. In general, interrill subprocesses are inter-actively affected by rainfall, soil and surface properties. The objective of this work was to study the relationships among interrill runoff and sediment loss and some selected para-meters, for cultivated soils in central Greece, and also the development of a formula for predicting single storm sediment delivery. Runoff and soil loss measurement field experiments have been conducted for a 3·5-year period, under natural storms. The soils studied were developed on Tertiary calcareous materials and Quaternary alluvial deposits and were textured from sandy loam to clay. The second group of soils showed greater susceptibility to sealing and erosion than the first group. Single storm sediment loss was mainly affected by rain and runoff erosivity, being significantly correlated with rain kinetic energy (r = 0·64***), its maximum 30-minute intensity (r = 0·64***) and runoff amount (r = 0·56***). Runoff had the greatest correlation with rain kinetic energy (r = 0·64***). A complementary effect on soil loss was detected between rain kinetic energy and its maximum 30-minute intensity. The same was true for rain kinetic energy and topsoil aggregate instability, on surface seal formation and thus on infiltration characteristics and overland flow rate. Empirical analysis showed that the following formula can be used for the successful prediction of sediment delivery (Di): Di = 0·638,EI30tan(,) (R2 = 0·893***), where , is a topsoil aggregate instability index, E the rain kinetic energy, I30 the maximum 30-minute rain intensity and , the slope angle. It describes soil erodibility using a topsoil aggregate instability index, which can be determined easily by a simple laboratory technique, and runoff through the product of this index and rain kinetic energy. Copyright © 2006 John Wiley & Sons, Ltd. [source] A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear dataJOURNAL OF CHEMOMETRICS, Issue 11-12 2005Alejandro C. Olivieri Abstract Three-way instrumental data offer the second-order advantage to analysts, a property of great utility in the field of complex sample analysis in the presence of unsuspected components as potential interferents. The available multivariate methodologies for obtaining this advantage are all based on linear models, and hence they are not applicable to spectral information behaving in a non-linear manner with respect to target analyte concentrations. This work describes the combination of a back-propagation artificial neural network model with a technique known as residual bilinearization, applicable to second-order spectral information. The joint model allows one to efficiently extract analyte concentrations from intrinsically non-linear data, even in the presence of unsuspected constituents. Simulations have been performed by mimicking deviations from linearity brought about by: (1) exponential relationship between fluorescence and concentration, (2) kinetic evolution of responsive reaction products and (3) analytes acting as reaction catalysts. In all of these cases, successful prediction of the analyte concentrations was achieved on large test sample sets, which included the presence of overlapping components not included in the training step. The new method not only obtains the second-order advantage, but also correctly retrieves the contribution of the unsuspected components to the total test sample signals. The comparison with a multivariate methodology based on partial least-squares regression with second-order advantage shows that the presently described method displays better predictive ability. Copyright © 2006 John Wiley & Sons, Ltd. [source] Physiologically based predictions of the impact of inhibition of intestinal and hepatic metabolism on human pharmacokinetics of CYP3A substratesJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 1 2010Frederique Fenneteau Abstract The first objective of the present study was to predict the pharmacokinetics of selected CYP3A substrates administered at a single oral dose to human. The second objective was to predict pharmacokinetics of the selected drugs in presence of inhibitors of the intestinal and/or hepatic CYP3A activity. We developed a whole-body physiologically based pharmacokinetics (WB-PBPK) model accounting for presystemic elimination of midazolam (MDZ), alprazolam (APZ), triazolam (TRZ), and simvastatin (SMV). The model also accounted for concomitant administration of the above-mentioned drugs with CYP3A inhibitors, namely ketoconazole (KTZ), itraconazole (ITZ), diltiazem (DTZ), saquinavir (SQV), and a furanocoumarin contained in grape-fruit juice (GFJ), namely 6,,7,-dihydroxybergamottin (DHB). Model predictions were compared to published clinical data. An uncertainty analysis was performed to account for the variability and uncertainty of model parameters when predicting the model outcomes. We also briefly report on the results of our efforts to develop a global sensitivity analysis and its application to the current WB-PBPK model. Considering the current criterion for a successful prediction, judged satisfied once the clinical data are captured within the 5th and 95th percentiles of the predicted concentration,time profiles, a successful prediction has been obtained for a single oral administration of MDZ and SMV. For APZ and TRZ, however, a slight deviation toward the 95th percentile was observed especially for Cmax but, overall, the in vivo profiles were well captured by the PBPK model. Moreover, the impact of DHB-mediated inhibition on the extent of intestinal pre-systemic elimination of MDZ and SMV has been accurately predicted by the proposed PBPK model. For concomitant administrations of MDZ and ITZ, APZ and KTZ, as well as SMV and DTZ, the in vivo concentration,time profiles were accurately captured by the model. A slight deviation was observed for SMV when coadministered with ITZ, whereas more important deviations have been obtained between the model predictions and in vivo concentration,time profiles of MDZ coadministered with SQV. The same observation was made for TRZ when administered with KTZ. Most of the pharmacokinetic parameters predicted by the PBPK model were successfully predicted within a two-fold error range either in the absence or presence of metabolism-based inhibition. Overall, the present study demonstrated the ability of the PBPK model to predict DDI of CYP3A substrates with promising accuracy. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:486,514, 2010 [source] Prediction of protein folding rates from primary sequences using hybrid sequence representationJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 5 2009Yingfu Jiang Abstract The ability to predict protein folding rates constitutes an important step in understanding the overall folding mechanisms. Although many of the prediction methods are structure based, successful predictions can also be obtained from the sequence. We developed a novel method called prediction of protein folding rates (PPFR), for the prediction of protein folding rates from protein sequences. PPFR implements a linear regression model for each of the mainstream folding dynamics including two-, multi-, and mixed-state proteins. The proposed method provides predictions characterized by strong correlations with the experimental folding rates, which equal 0.87 for the two- and multistate proteins and 0.82 for the mixed-state proteins, when evaluated with out-of-sample jackknife test. Based on in-sample and out-of-sample tests, the PPFR's predictions are shown to be better than most of other sequence only and structure-based predictors and complementary to the predictions of the most recent sequence-based QRSM method. We show that simultaneous incorporation of several characteristics, including the sequence, physiochemical properties of residues, and predicted secondary structure provides improved quality. This hybridized prediction model was analyzed to reveal the complementary factors that can be used in tandem to predict folding rates. We show that bigger proteins require more time for folding, higher helical and coil content and the presence of Phe, Asn, and Gln may accelerate the folding process, the inclusion of Ile, Val, Thr, and Ser may slow down the folding process, and for the two-state proteins increased ,-strand content may decelerate the folding process. Finally, PPFR provides strong correlation when predicting sequences with low similarity. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009 [source] Prediction of model pools for a long-term experiment using near-infrared spectroscopyJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 1 2010Kerstin Michel Abstract Fourty-one soil samples from the "Eternal Rye" long-term experiment in Halle, Germany, were used to test the usefulness of near-infrared spectroscopy (NIRS) to differentiate between C derived from C3 and C4 plants by using the isotopic signature (,13C) and to predict the pools considered in the Rothamsted Carbon (RothC) model, i.e., decomposable plant material, resistant plant material, microbial biomass, humified organic matter, and inert organic matter. All samples were scanned in the visible-light and near-infrared region (400,2500 nm). Cross-validation equations were developed using the whole spectrum (first to third derivative) and a modified partial least-square regression method. ,13C values and all pools of the RothC model were successfully predicted by NIRS as reflected by RSC values (ratio between standard deviation of the laboratory results and standard error of cross-validation) ranging from 3.2 to 3.4. Correlations analysis indicated that organic C can be excluded as basis for the successful predictions by NIRS in most cases, i.e., 11 out of 16. [source] Significant progress in predicting the crystal structures of small organic molecules , a report on the fourth blind testACTA CRYSTALLOGRAPHICA SECTION B, Issue 2 2009Graeme M. Day We report on the organization and outcome of the fourth blind test of crystal structure prediction, an international collaborative project organized to evaluate the present state in computational methods of predicting the crystal structures of small organic molecules. There were 14 research groups which took part, using a variety of methods to generate and rank the most likely crystal structures for four target systems: three single-component crystal structures and a 1:1 cocrystal. Participants were challenged to predict the crystal structures of the four systems, given only their molecular diagrams, while the recently determined but as-yet unpublished crystal structures were withheld by an independent referee. Three predictions were allowed for each system. The results demonstrate a dramatic improvement in rates of success over previous blind tests; in total, there were 13 successful predictions and, for each of the four targets, at least two groups correctly predicted the observed crystal structure. The successes include one participating group who correctly predicted all four crystal structures as their first ranked choice, albeit at a considerable computational expense. The results reflect important improvements in modelling methods and suggest that, at least for the small and fairly rigid types of molecules included in this blind test, such calculations can be constructively applied to help understand crystallization and polymorphism of organic molecules. [source] | |||||||||||||