Bovine oocytes in secondary follicles grow and acquire meiotic competence in severe combined immunodeficient mice

A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, performance statistics, outlier treatment, parameter identifiability, and uncertainty of the prediction. The methodology is evaluated through development of a GC method for the prediction of the heat of combustion (Δ<i>H</i>_c^o) for pure components. The results showed that robust regression lead to best performance statistics for parameter estimation. The bootstrap method is found to be a valid alternative to calculate parameter estimation errors when underlying distribution of residuals is unknown. Many parameters (first, second, third order group contributions) are found unidentifiable from the typically available data, with large estimation error bounds and significant correlation. Due to this poor parameter identifiability issues, reporting of the 95% confidence intervals of the predicted property values should be mandatory as opposed to reporting only single value prediction, currently the norm in literature. Moreover, inclusion of higher order groups (additional parameters) does not always lead to improved prediction accuracy for the GC-models; in some cases, it may even increase the prediction error (hence worse prediction accuracy). However, additional parameters do not affect calculated 95% confidence interval. Last but not least, the newly developed GC model of the heat of combustion (Δ<i>H</i>_c^o) shows predictions of great accuracy and quality (the most data falling within the 95% confidence intervals) and provides additional information on the uncertainty of each prediction compared to other Δ<i>H</i>_c^o models reported in literature

Structural Causes of Right Bundle Branch Block—Time for a Closer Look?

Right bundle branch block is an electrocardiographic phenomenon with specific criteria

Reliable Correlation for Liquid–Liquid Equilibria outside the Critical Region

A simple model for binary liquid–liquid equilibrium data correlation is explored. The model describes liquid–liquid equilibrium in terms of Henry’s law and unsymmetrically normalized activity coefficients in each phase. A procedure for parameter estimation including an approach to initial guesses, uncertainty analysis of regression results, obtained parameters, and predicted mole fractions has been formulated. The procedure is applied to three cases: hydrocarbons + water, ionic liquids + water, and nitroethane + hydrocarbons. The model has four parameters in the most basic formulation. Depending upon the available data, this number can be extended in a systematic fashion. We compare results of correlation to results obtained with a four-parameter nonrandom two-liquid (NRTL) equation and COSMO-SAC. In general, the new model does nearly as well as NRTL. Advantages of the presented model are a simple form and a parameter set that can be extended in a systematic fashion with an interpretation in terms of thermodynamic properties. The model may be developed further for validation of experimental data

Cognitive Sparing in Proton versus Photon Radiotherapy for Pediatric Brain Tumor Is Associated with White Matter Integrity: An Exploratory Study

Radiotherapy for pediatric brain tumors is associated with reduced white matter structural integrity and neurocognitive decline. Superior cognitive outcomes have been reported following proton radiotherapy (PRT) compared to photon radiotherapy (XRT), presumably due to improved sparing of normal brain tissue. This exploratory study examined the relationship between white matter change and late cognitive effects in pediatric brain tumor survivors treated with XRT versus PRT. Pediatric brain tumor survivors treated with XRT