Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n ¼ 88; validation cohort: n ¼ 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 Â 10 À9 ). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 Â 10 À7 ). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages. Conclusions: Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies.

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Bioethanol is currently a significant gasoline additive and the major blend component of flex-fuel formulations. Ethanol is a high-octane fuel component, and vehicles designed to take advantage of higher octane fuel blends could operate at higher compression ratios than traditional gasoline engines, leading to improved performance and tank-to-wheel efficiency. There are significant uncertainties, however, regarding the mechanism for ethanol autoignition, especially at lower temperatures such as in the negative temperature coefficient (NTC) regime. We have studied an important chemical process in the autoignition and oxidation of ethanol, reaction of the alpha-hydroxyethyl radical with O2(3P), using first principles computational chemistry, variational transition state theory, and Rice-Ramsperger-Kassel-Marcus (RRKM)/master equation simulations. The alpha-hydroxyethyl + O2 association reaction is found to produce an activated alpha-hydroxy-ethylperoxy adduct with ca. 37 kcal mol(-1) of excess vibrational energy. This activated adduct predominantly proceeds to acetaldehyde + HO(2), with smaller quantities of the enol vinyl alcohol (ethenol), particularly at higher temperatures. The reaction to acetaldehyde + HO2 proceeds with such a low barrier that collision stabilization of C2O3H5 isomers is unimportant, even for high-pressure/low-temperature conditions. The short lifetimes of these radicals precludes the chain-branching addition of a second O2 molecule, responsible for NTC behavior in alkane autoignition. This result helps to explain why ignition delays for ethanol are longer than those for ethane, despite ethanol having a weaker C-C bond energy. Given its relative instability, it is also unlikely that the alpha-hydroxy-ethylperoxy radical acts as a major acetaldehyde sink in the atmosphere, as has been suggested.

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Acceleration of the chemistry solver for engine combustion is of much interest due to the fact that in practical engine simulations extensive computational time is spent solving the fuel oxidation and emission formation chemistry. A dynamic adaptive chemistry (DAC) scheme based on a directed relation graph error propagation (DRGEP) method has been applied to study homogeneous charge compression ignition (HCCI) engine combustion with detailed chemistry (over 500 species) previously using an R-valuebased breadth-first search (RBFS) algorithm, which significantly reduced computational times (by as much as 30-fold). The present paper extends the use of this on-the-fly kinetic mechanism reduction scheme to model combustion in direct-injection (DI) engines. It was found that the DAC scheme becomes less efficient when applied to DI engine simulations using a kinetic mechanism of relatively small size and the accuracy of the original DAC scheme decreases for conventional non-premixed combustion engine. The present study also focuses on determination of search-initiating species, involvement of the NO x chemistry, selection of a proper error tolerance, as well as treatment of the interaction of chemical heat release and the fuel spray. Both the DAC schemes were integrated into the ERC KIVA-3v2 code, and simulations were conducted to compare the two schemes. In general, the present DAC scheme has better efficiency and similar accuracy compared to the previous DAC scheme. The efficiency depends on the size of the chemical kinetics mechanism used and the engine operating conditions. For cases using a small n-heptane kinetic mechanism of 34 species, 30% of the computational time is saved, and 50% for a larger n-heptane kinetic mechanism of 61 species. The paper also demonstrates that by combining the present DAC scheme with an adaptive multi-grid chemistry (AMC) solver, it is feasible to simulate a direct-injection engine using a detailed n-heptane mechanism with 543 species with practical computer time.

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• IVIM analysis permits separate quantification of diffusion and perfusion. • IVIM can provide useful biomarkers ifor changes in renal pathophysiology. • IVIM can be useful for monitoring progress in renal pathophysiology undergoing CIAKI.