Mirrored Genome Size Distributions in Monocot and Dicot Plants

The variation in genome size and basic chromosome number was analyzed in the wide range of angiosperm plants. A divergence of monocots vs. dicots (eudicots) genome size distributions was revealed. A similar divergence was found for annual vs. perennial dicots. The divergence of monocots vs. dicots genome size distributions holds at different taxonomic levels and is more pronounced for species with larger genomes. Using nested analysis of variance, it was shown that putative constraints on genome size variation are not only stronger in dicots as compared to monocots but in the former they start to operate already at the family level, whereas in the latter they do so only at the order level. At the same time, variation in basic chromosome number is constrained at the order level in both groups. Higher basic chromosome numbers were found in perennial plants as compared to the annual ones, which can be explained by their need for a higher genetic recombination as compensation for the longer life-cycles. A negative correlation was found between genome size and basic chromosome number, which can be explained as a trade-off between different recombination mechanisms.     

Minimizing model fitting objectives that contain spurious local minima by bootstrap restarting

Objective functions that arise when fitting nonlinear models often contain local minima that are of little significance except for their propensity to trap minimization algorithms. The standard methods for attempting to deal with this problem treat the objective function as fixed and employ stochastic minimization approaches in the hope of randomly jumping out of local minima. This article suggests a simple trick for performing such minimizations that can be employed in conjunction with most conventional nonstochastic fitting methods. The trick is to stochastically perturb the objective function by bootstrapping the data to be fit. Each bootstrap objective shares the large-scale structure of the original objective but has different small-scale structure. Minimizations of bootstrap objective functions are alternated with minimizations of the original objective function starting from the parameter values with which minimization of the previous bootstrap objective terminated. An example is presented, fitting a nonlinear population dynamic model to population dynamic data and including a comparison of the suggested method with simulated annealing. Convergence diagnostics are discussed.     

Nonparametric estimation in a cure model with random cure times

Acute respiratory distress syndrome (ARDS) is a life-threatening acute condition that sometimes follows pneumonia or surgery. Patients who recover and leave the hospital are considered to have been cured at the time they leave the hospital. These data differ from typical data in which cure is a possibility: death times are not observed for patients who are cured and cure times are observed and vary among patients. Here we apply a competing risks model to these data and show it to be equivalent to a mixture model, the more common approach for cure data. Further, we derive an estimator for the variance of the cumulative incidence function from the competing risks model, and thus for the cure rate, based on elementary calculations. We compare our variance estimator to Gray's (1988, Annals of Statistics 16, 1140-1154) estimator, which is based on counting process theory. We find our estimator to be slightly more accurate in small samples. We apply these results to data from an ARDS clinical trial.     

A semiparametric estimate of treatment effects with censored data

A semiparametric estimate of an average regression effect with right-censored failure time data has recently been proposed under the Cox-type model where the regression effect beta(t) is allowed to vary with time. In this article, we derive a simple algebraic relationship between this average regression effect and a measurement of group differences in k-sample transformation models when the random error belongs to the G(rho) family of Harrington and Fleming (1982, Biometrika 69, 553-566), the latter being equivalent to the conditional regression effect in a gamma frailty model. The models considered here are suitable for the attenuating hazard ratios that often arise in practice. The results reveal an interesting connection among the above three classes of models as alternatives to the proportional hazards assumption and add to our understanding of the behavior of the partial likelihood estimate under nonproportional hazards. The algebraic relationship provides a simple estimator under the transformation model. We develop a variance estimator based on the empirical influence function that is much easier to compute than the previously suggested resampling methods. When there is truncation in the right tail of the failure times, we propose a method of bias correction to improve the coverage properties of the confidence intervals. The estimate, its estimated variance, and the bias correction term can all be calculated with minor modifications to standard software for proportional hazards regression.     

Microscopic and Calorimetric Assessment of Freezing Processes in Uterine Fibroid Tumor Tissue

The use of cryosurgery in the treatment of uterine fibroids is emerging as a possible treatment modality. The two known mechanisms of direct cell injury during the tissue freezing process are linked to intracellular ice formation and cellular dehydration. These processes have not been quantified within uterine fibroid tumor tissue. This study reports the use of a combination of freeze-substitution microscopy and differential scanning calorimetry (DSC) to quantify freeze-induced dehydration within uterine fibroid tumor tissue. Stereological analysis of histological tumor sections was used to obtain the initial cellular volume (V(o)) or the Krogh model dimensions (deltaX, the distance between the microvascular channels = 15.5 microm, r(vo), the initial radius of the extracellular space = 4.8 micro m, and L, the axial length of the Krogh cylinder = 19.1 microm), the interstitial volume ( approximately 23%), and the vascular volume ( approximately 7%) of the fibroid tumor tissue. A Boyle-van't Hoff plot was then constructed by examining freeze-substituted micrographs of "equilibrium"-cooled tissue slices to obtain the osmotically inactive cell volume, V(b) = 0.47V(o). The high interstitial volume precludes the use of freeze-substitution microscopy data to quantify freeze-induced dehydration. Therefore, a DSC technique, which does not suffer from this artifact, was used to obtain the water transport data. A model of water transport was fit to the calorimetric data at 5 and 20 degrees C/min to obtain the "combined best fit" membrane permeability parameters of the embedded fibroid tumor cells, assuming either a Krogh cylinder geometry, L(pg) = 0.92 x 10(-13) m(3)/Ns (0.55 microm/min atm) and E(Lp) = 129.3 kJ/mol (30.9 kcal/mol), or a spherical cell geometry (cell diameter = 18.3 microm), L(pg) = 0.45 x 10(-13) m(3)/Ns (0.27 microm/min atm) and E(Lp) = 110.5 kJ/mol (26.4 kcal/mol). In addition, numerical simulations were performed to generate conservative estimates, in the absence of ice nucleation between -5 and -30 degrees C, of intracellular ice volume in the tumor tissue at various cooling rates typical of those experienced during cryosurgery (< or =100 degrees C/min). With this assumption, the Krogh model simulations showed that the fibroid tumor tissue cells cooled at rates < or = 50 degrees C/min are essentially dehydrated; however, at rates >50 degrees C/min the amount of water trapped within the tissue cells increases rapidly with increasing cooling rate, suggesting the formation of intracellular ice.