can be formulated in terms of basis functions and discuss the dif culties in posterior simulations due to interdependence of these functions. Such interdependence affects the ef ciency of the sampling algorithms to draw from posterior distribution of the tree structures. Different strategies that are suggested in the literature for dealing with the sampling problem are discussed, and a detailed example using binary classi cation is presented to illustrate the Bayesian analysis. To alleviate the problems that arise in tree models, Chapter 7 introduces partition models. The partition models can be considered a generalization of tree models, and they allow for sampling from the posterior distributions of the tree structure. As noted by the authors, these models suffer from lack of interpretability in high dimensions. The authors present one-dimensional partition models whose analysis provides a general framework for the changepoint problems. Multidimensional partition models, where partitions are de ned by Dirichlet tessellations, are considered, and Bayesian inference for classi cation problems is discussed. Disease mapping models are presented as an application of partition models for spatial problems. Chapter 8 is a short chapter describing Bayesian nearest-neighbor modeling. The nearest-neighbor classi cation algorithm, which is used commonly in pattern recognition, is given a probabilistic formulation by introducing a parameter that controls the degree of association between the neighboring classes. In Chapter 9 the authors generalize the single-response case to multipleresponse models where the observed response is a collection of values. The chapter focuses on regression models and does not consider classi cation models such as the multivariate probit models. Multivariate Bayesian regression framework is introduced where basis functions can be determined from the data using methods of Chapter 3. A generalization is considered using seemingly unrelated regression (SUR) models and their Bayesian analysis is presented. Prior speci cation for basis function matrix are discussed, and computational details of the MCMC methods are given for both models. The book has a comprehensive bibliography, and each chapter (except Chap. 1) has a section on further reading. There are two appendixes at the end. Appendix B gives a summary of posterior inference results that are helpful for some of the development in the text. In summary, this book presents recent developments in Bayesian nonlinear modeling and provides a complete treatment of regression and classi cation problems by emphasizing a data-driven approach in determining appropriate models. Its in-depth coverage of implementation issues and detailed discussion of pros and cons of different modeling strategies make it attractive for many researchers. It de nitely makes my list of recommended texts in Bayesian statistics.

Resampling Methods for Dependent Data

An experiment was conducted with 240, 1-d-old, male broilers to investigate the effects of Saccharomyces cerevisiae (SC) cell components on the growth performance, meat quality, and ileal mucosa development. There were 4 dietary treatments, each consisting of 6 replicates. Whole yeast (WY), SC extract (YE), and SC cell wall (CW) were added at 0.5, 0.3, and 0.3%, respectively, to the control starter and finisher diets. From 0 to 3 wk of age, a lower feed/gain ratio (P < or = 0.05) was observed with CW, whereas the WY-fed birds at 4 to 5 wk of age showed a lower feed/gain ratio compared with the control. From 0 to 5 wk of age, WY and CW gave higher BW gains than did the control. The shear force of raw drumstick decreased in the WY treatment relative to the control, and YE and CW treatments were intermediate. The shear forces in cooked breast and drumstick in treatments WY and YE decreased when compared with the control. The amount of 2-thiobarbituric acid-reactive substances (TBARS) in the breast meats of WY, YE, and CW were lower than the control at 10 d of incubation. In raw drumstick meats, TBARS values were lower in treatments WY and YE than that of the control at 6 and 10 d of incubation. At 10 d of incubation, skins from YE and CW treatments had lower TBARS values than did the control. Villus height was greater in WY and CW compared with those in control and YE. No differences were found in crypt depth among the 4 treatments. The villus height/ crypt depth ratios in WY and CW were greater than those of the control and YE. It could be concluded that dietary yeast components, such as WY or CW supplementation improved growth performance. Meat tenderness could be improved by the WY or YE. Both YE and CW had oxidation-reducing effects. Yeast cell wall may improve ileal villus development.

Effects of yeast (Saccharromyces cerevisiae) cell components on growth performance, meat quality, and ileal mucosa development of broiler chicks.

Tumor growth curves are classically modeled by means of ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model, which could be used to reduce the dimensionality and improve predictive power. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 833 measurements in 94 animals. Candidate models of tumor growth included the exponential, logistic and Gompertz models. The exponential and-more notably-logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The previously reported population-level correlation between the Gompertz parameters was further confirmed in our analysis (R2 > 0.92 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a reduced Gompertz function consisting of a single individual parameter (and one population parameter). Leveraging the population approach using Bayesian inference, we estimated times of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using Bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy (prediction error) was 12.2% versus 78% and mean precision (width of the 95% prediction interval) was 15.6 days versus 210 days, for the breast cancer cell line. These results demonstrate the superior predictive power of the reduced Gompertz model, especially when combined with Bayesian estimation. They offer possible clinical perspectives for personalized prediction of the age of a tumor from limited data at diagnosis. The code and data used in our analysis are publicly available at https://github.com/cristinavaghi/plumky.

/pdf/population-modeling-of-tumor-growth-curves-and-the-reduced-ccvnc0pzjy.pdf

Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors

Comparison of non-linear growth models to describe the growth in Japanese quail.

Patterns of Growth

Japanese quail meat quality: Characteristics, heritabilities, and genetic correlations with some slaughter traits

Genetic Parameters of Growth Curve Parameters and Weekly Body Weights in Japanese Quails (Coturnix coturnix japonica)

Stress based on high temperature and humidity reduces the production performance of fast-growing broilers and causes high mortality. Temperatures higher than optimum have been applied to broilers in the embryonic period in order to overcome thermal stress. This study was conducted to investigate the effects of exposure to two long-term high-thermal environments on the developmental stability of embryonic growth, hatchability and chick quality. For this purpose, 600 broiler eggs were incubated. Treatments consisted of eggs incubated at 37.8°C at 55% relative humidity throughout (control), heated to 39.6°C at 60% relative humidity for 6 h daily from 0 to 8th day, and heated to 39.6°C at 60% relative humidity for 6 h daily from the 10 to 18th day. Embryo weights and lengths of face, wing, femur, tibia and metatarsus were measured daily between the 10th and 21st day of the experiment. Daily relative asymmetry values of bilateral traits were estimated. The hatchability, the weight of the 1-day-old chicks and chick quality were determined. In conclusion, no negative effects of the treatments of the long-term high-thermal environment in the early and late stages of incubation for epigenetic adaptation were determined on the embryo morphology, development stability and weight of the chick. Moreover, regressed hatchability of embryos that were exposed to a long-term high-thermal environment was detected. Especially between the 10 and 18th day, the thermal manipulation considerably reduced the quality of the chicks. Acclimation treatments of high temperature on the eggs from cross-breeding flocks should not be made long term; instead, short-term treatments should be made by determining the stage that generates epigenetic adaptation.

Effects of thermal manipulations during embryogenesis of broiler chickens on developmental stability, hatchability and chick quality

This review covers the egg production models used in poultry. Similarities and discrepancies among the models are illustrated using a real data obtained from a layer breeder flock. Egg laying in poultry begins at sexual maturity and quickly reaches peak production, and then declines with hen age. For many years, egg production studies have been carried out for the purpose of modelling. Some of the functions were developed for this purpose, such as nonlinear regression equations (Gamma, McNally, McMillan, Adams-Bell, Compartmental, Modified Compartmental, logistic-curvilinear, Gloor, Lokhorst, Narushin-Takma) and some Multiphasic (Segmented Polynomial, Persistency, Individual). Almost all of these functions have been developed to allow modelling on the basis of flock averages. Most models having empirical structure and a small number of parameters are considered biologically meaningful. New models are currently required to be useful for both individual egg yields and to contain biologically relevant parameters.

Egg production curve analyses in poultry science

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