To observe the effect of the profile of M(d) (left panel in Figure 1C) on the macroscopic evolution patterns, we first fixed the fitness as a constant, F(d) = f. Figure 3 shows the fit of the model to the dynamics of divergence and diversity of patients S-P1—SP11 [13].
Sample text:```We also hypothesized that variation in seed mass should decrease in populations subjected to the strongest directional selection pressure (see [29]), which in this case is more arid areas where seed mass is known to be a key determinant of fitness [35,39].```
Sample intent: method
Sample text:```This is interesting in relation to the result from our previous CGH study (comparing the number of significantly hybridizing probes on the microarray) that showed high sequence divergence on the Z chromosome between the common whitethroat and the zebra finch [62]; and such patterns are also found in other species [48,51,69].```
Sample intent: result



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```We used the stepwise multiple linear regression that accounts for repeated measurements in the same dog (4) first to test whether there was a difference among values for resistances and for elastances between control and emphysema at each mean Paw, considering the entire range of f and VT of forcing.```
Sample intent: background



Sample text:```Estimation of overall mortality ratio (relative risk) was carried out using the random e ects model according to DerSimonian and Laird [9].```
Sample intent: background



Sample text:```These models are not specified and observe a single phenomenon from different points of view (Maxfield et al., 2002; Parker et al., 2009).```
Sample intent: method
Sample text:```Estimation of overall mortality ratio (relative risk) was carried out using the random e ects model according to DerSimonian and Laird [9].```
Sample intent: background



Sample text:```The GITT model performs better than an empirical lag-time model [17], however, its predicted profiles tend to underestimate Cmax, which, if consistent, may suggest confounding systematic bias or invalid assumptions.```
Sample intent: background



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result



Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method



Sample text:```We used the stepwise multiple linear regression that accounts for repeated measurements in the same dog (4) first to test whether there was a difference among values for resistances and for elastances between control and emphysema at each mean Paw, considering the entire range of f and VT of forcing.```
Sample intent: background



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```LG/J and SM/J mice have been shown to differ in their response to a high-fat diet for traits involved in various metabolic syndrome domains [23,24].```
Sample intent: method



Sample text:```These are in keeping with earlier studies on the steadystate response (2, 5, 8), step-function response, (3, 5, 6, 14) and sinusoidal response ( 14) of the various vascular beds.```
Sample intent: result



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background