| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
ANIMAL GENETICS |
Animal and Dairy Science Department, University of Georgia, Athens 30602-2771
Abstract
Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were -0.62 d (0.19 d) and -0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were -0.70 d (0.14 d) and -0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d2 (4.1 d2), 363.2 d2 (4.8 d2), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multitrait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.
Key Words: Beef Cattle • Genetic Evaluation • Heifer Fertility
Introduction
Little attention has been given to genetic evaluations of fertility that incorporate both natural service (NS) and AI information in one analysis. Previous work investigating reproductive performance under NS matings (e.g., Buddenberg et al., 1990
; Johnston and Bunter, 1996) has focused on the traits of calving date and days to calving (DC), whereas studies using information from AI mating data have primarily evaluated binary traits, such as heifer pregnancy and calving success (CS).
Donoghue et al. (2004b) investigated calving to first insemination (CFI) as a potential trait from which information from both natural and artificial matings could be combined and compared. These authors reported a genetic correlation of 0.82 between CFI under the two types of mating data and concluded that cows with a higher probability of CFI when mated using AI also had a high probability of CFI when mated via NS.
One alternative method for combining both sources of mating information may be to fit a threshold-linear bivariate analysis to the data and report one genetic value for fertility. Continuous measures of fertility, such as DC, have several advantages over binary traits such as CS and CFI. They not only allow identification of animals more likely to conceive, but also allow for the identification of animals that will conceive early in the breeding season. It may be desirable to report only the genetic value for DC when AI and NS mating are combined, given that reasonable genetic relationships exist with the binary trait.
The objective of this study was to investigate the genetic relationships between DC and measures of fertility in AI data (CS and CFI). Two bivariate analyses were conducted: DC with CFI and DC with CS. Genetic correlations between DC and CS or CFI were used to examine the magnitude of the genetic relationships. In addition, correlations between breeding values of CS or CFI and DC were compared for sires with progeny records.
Materials and Methods
Data
The data comprised mating and calving records for first-calf females from Angus herds located in predominantly temperate regions of Australia. The Australian Angus database has field records and is a total female inventory recording system, for which mating details on every female in the herd are available. Animals had either an AI or a NS record, but not both. The majority of animals with AI records had a single mating record, whereas a small number (n = 2,277) had two mating records: AI mating followed by NS mating. The traits of CS and CFI were defined only for AI records, whereas DC was defined only for NS records. Only animals that had their first mating record between 270 and 625 d of age were included in the analysis. After editing, records from 47,533 females born between 1987 and 2000 were available for analysis. Edits performed for all traits included removal of 1) animals with incomplete records and 2) mating records resulting in multiple births. In addition, single-record contemporary groups were removed from the AI data, whereas contemporary groups with less than four records and contemporary groups consisting of only noncalvers were removed from the NS data. The final AI and NS datasets comprised 16,358 and 31,175 records, respectively. The total number of animals, including ancestors, was 78,912. A total of 2,239 and 3,945 sires was represented in the AI and NS datasets, respectively, with 1,547 sires having progeny in both datasets. The measures of fertility for AI matings were recorded for the same animals, and only differed in definition: CFI vs. CS. The structures of the datasets are shown in Table 1, with summary statistics for CFI and CS combined.
|
). Nongenetic effects were linear and quadratic covariates for age at mating, month of mating, and random effects of service sire and contemporary group. Contemporary group was defined to include animals from the same herd mated in the same year. The mean incidence of calving to first insemination was 79%. Calving success was recorded only for AI data, and was defined as a binary trait. Females that calved, whether to an AI mating or a subsequent NS mating, were coded as 1, whereas animals that failed to calve were coded as 0. Nongenetic effects were linear and quadratic covariates for age at mating, month of mating, and random effects of service sire and contemporary group. Service sire was designated as the first sire to which the female was mated (i.e., the AI mating sire). Contemporary group was defined to include animals from the same herd mated in the same year. The mean incidence of calving success was 92%.
The continuous trait of DC was recorded only for NS data and was defined as the number of days between the time a bull was turned out in the pasture and the subsequent parturition of the female. Nongenetic effects were linear and quadratic covariates for age at mating, sex of calf, and contemporary group. Contemporary group was defined to include animals from the same herd that were mated to the same service sire in the same month and year. The sex of calf effect was randomly assigned to either male or female for all animals with censored records. Using the approach of Donoghue et al. (2004a), trait values for censored records were simulated from their respective predictive distributions (truncated normal distributions). For all animals in the same contemporary group, the truncation point was the largest observed DC record. The predicted DC for a censored record was between the truncation point and positive infinity. Thus, an animal with a censored record could not receive a simulated record that was smaller than an uncensored record within its contemporary group. The number of days added to this truncation point for each of the censored records was determined by drawing samples at random from the truncated distribution, and depended on the fixed effects in the model, as well as its relationships with other animals.
Model
Two analyses were undertaken: 1) DC with CFI and 2) DC with CS. Both models were bivariate, with CFI or CS being binary (threshold) and DC being Gaussian. It was postulated that liabilities of CS or CFI and DC were jointly Gaussian. Foulley et al. (1983) developed this model.
The following mixed linear animal model was used for both analyses of traits:
 |
where y was a vector of DC observations or unobserved liabilities of CS or CFI; 
was the vector of systematic effects; s was the vector of service sire effects (for CFI and CS only); u was the vector of additive genetic values; e was the vector of residual terms; X, Zs, and Zu were known incidence matrices with the appropriate dimensions. For CFI and CS, ß1 included herd x year effects, month of mating effects, and linear and quadratic covariates for age at mating. For DC, ß2 included contemporary group effects, sex of calf effects, and linear and quadratic covariates for age at mating. The random effect of service sire was not included for the trait of DC. This effect was included as part of the contemporary group definition for DC, which is the current practice in the genetic evaluation program in Australia (Schneeberger et al., 1991).
Conditionally, on the model parameters, it was assumed that the sampling distribution of observations was as follows:
 |
where R0 is a 2 x 2 variance–covariance matrix with the following structure:
 |
Given the nonidentifiability problem of threshold models, at least two restrictions were needed (Cox and Snell, 1989; Sorensen et al., 1995). In this study for the traits of CFI and CS, the threshold and residual variance 
were arbitrarily set to zero and one, respectively. Furthermore, all animals had either an AI or a NS record; none had both traits measured. Consequently, the residual covariance (
e12 = e21) cannot be inferred and was set to zero. The prior distribution for the residual variance for DC was:
 |
For the traits of CS and CFI, the vector of systematic effects was partitioned into subvectors 
, where ßH was a vector of herd x year effects and ßR was a vector containing month of mating effects and linear and quadratic covariates for age at mating. The average number of records per herd x year class was small, and many herd x year classes were either all zeros or all ones for any of the two binary traits. This can lead to poor inferences, and is generally referred to as the extreme category problem (ECP) (Misztal et al., 1989). Hence, herd x year effects were assigned a normal prior with unknown mean and variance. This is based on the results of Rekaya et al. (2000), who found that this prior distribution alleviated ECP in such data. The prior distribution for the vector ßH was:
 |
where 
and 
are the mean and variance of herd x year effects, respectively. Both and were assumed unknown and hence, priors were specified as follows:
 |
where U [.] is the uniform distribution.
The prior distribution for vector ßR was:
 |
with 
. As 
is large relative to the residual variance, this prior distribution conveys vague prior knowledge about each of the elements of ßR.
For DC, the following prior distribution was assumed for the systematic effects:
 |
A normal distribution was used as the prior for the effect of service sire for the traits of CFI and CS:
 |
The following uniform-bounded prior was assigned to the service sire variance for these traits:
 |
A multivariate normal distribution was used as the prior for the animal effects:
 |
where 
was the additive (co)variance matrix, and A was the additive relationship matrix between animals. A conjugate proper prior was assumed. The scaling factor for the prior of G0, shown below, was taken from the literature (Johnston et al., 2001). The degree of belief a priori was set to 5 conveying little weight to the prior information.
 |
The joint prior density had the form:
 |
The joint posterior density is proportional to the product of the density of the conditional distribution of the observation x the joint prior density. Draws from the conditional posterior distribution of all the parameters were obtained using a Gibbs sampler with data augmentation (Sorensen et al., 1995). The joint posterior was augmented with the univariate normal liabilities for CS or CFI. After augmentation, all the fully conditional posterior distributions of model parameters can be derived as described by Albert and Chib (1993) and Sorensen et al. (1995). These distributions are normal for the systematic parameters (service sire and animal effects), truncated normal for the liabilities, scaled-inverted 
2 for the residual variance for DC, and scaled-inverted Wishart distributions for the dispersion parameters. Liabilities were sampled from their truncated normal distribution using inverse cumulative distribution function technique (Devroye, 1986). The posterior distribution was augmented with the unobserved calving dates corresponding to the censored DC observations as described in Donoghue et al. (2004a).
(Co)variance components were estimated for both analyses, and genetic correlations of CS or CFI with DC were compared. Breeding values were predicted for all animals, and correlations between breeding values of CS or CFI and DC were compared for sires with progeny records.
Results and Discussion
For all analyses, convergence was assessed using methodology presented by Raftery and Lewis (1992). The required length of the burn-in period was always less then 5,000 iterations for all parameters. Thus, 200,000 iterations of the sampler were run with a conservative 50,000 iterations discarded as burn-in; the remaining 150,000 iterations were retained with thinning for post-Gibbs analysis.
The mean and SD of DC for uncensored females was 302 ± 19 d, whereas the corresponding statistic for censored females was 354 ± 25 d. The number of days added to the largest observed DC record within a contemporary group ranged from 5 to 26 d. The majority of censored animals (63%) received a record ranging from 10 to 16 d greater than the largest observed DC record within their contemporary group, whereas 21, 14, and 2% of censored females received records with 5 to 9, 17 to 21, and 21 to 26 d added, respectively.
Summaries of the posterior distributions of (co)variance components, heritabilities, and genetic correlation from the CFI-DC bivariate analysis are presented in Table 2. The posterior mean (SD) of the additive covariance between CFI and DC was -0.62 d (0.19 d), and the corresponding genetic correlation was -0.66 (0.12). These results suggest a high, negative correlation between probability of CFI and DC. A large, negative correlation indicates that, genetically, cows with a higher probability of calving to their first insemination will also record a shorter DC interval. Thus, selection for increased probability of CFI would result in correlated decreases in DC interval. This value is similar to that reported by Johnston et al. (2001) for the genetic correlation between DC and calving success (-0.66). Morris et al. (2000) also found a similar relationship between higher pregnancy rates and early calving dates in Angus heifers, but did not report the magnitude of this association.
|
); furthermore, both estimates were within the high posterior density (HPD) (95%) intervals presented in the earlier study. The point estimates of heritability were smaller than estimates reported in the literature for DC or the equivalent trait of calving date for analyses when censored records are included (Johnston and Bunter, 1996; Morris et al., 2000; Johnston et al., 2001); however, our estimates were within the range of standard error associated with these literature estimates. The point estimate of residual variance for DC was similar to estimates previously reported for this population (Donoghue et al., 2004a). In the current study, the effect of service sire was included as part of the contemporary group definition to align with current industry practices for evaluation of DC. An additional analysis was undertaken with the random effect of service sire included in the model. The results from this analysis (not shown) were very similar to the results obtained in the current study. Fitting service sire as an additional random effect allowed for the formation of larger contemporary groups and had little effect on the estimation of additive variance or on heritability of the trait. Thus, including service sire as a random effect may be a reasonable option to avoid small contemporary groups.
The posterior means for additive variance and heritability for CFI were similar to previous estimates reported for this population (Donoghue et al., 2004b); furthermore, both estimates were within the HPD (95%) intervals presented in the earlier study. The point estimate of h2 in the current study was lower than estimates reported in literature for heifer fertility; however, it is within the 90% CI reported by Evans et al. (1999) for the trait of heifer pregnancy. The low estimates of h2 observed in this study could result from more appropriate analytical procedures for data analysis, Bayesian approach vs. Method R for a small dataset, or perhaps are a reflection of the slight differences in trait definitions between CFI and heifer pregnancy.
The posterior mean of herd-year variance for CFI in this study was much smaller than reported in an earlier study for the same population: 0.06 vs. 0.84 (Donoghue et al., 2004b). The variable nature of this parameter is most likely caused by a high incidence of ECP in the AI data; 50% of herd-year classes contained observations that fell into the same category for CFI. Possible reasons for this high incidence of ECP in the AI data were discussed in the earlier study, and included incomplete data recording and implementation of different management levels under AI matings. The highly fluctuating nature of this parameter suggests that caution should be used in the interpretation of the herd-year variance in the presence of ECP. Despite the large difference in this parameter observed between this study and the earlier study for CFI, additive variance and heritability point estimates were very similar in both studies, indicating that estimation of these parameters appears stable.
The posterior mean of service sire variance for CFI was larger in magnitude than both herd-year and additive variances. The point estimate in the current study was higher than the estimate reported in the earlier study for this population and was outside the HPD (95%) interval for that study. However, examination of the SD and HPD (95%) interval associated with this point estimate indicates a lack of statistical evidence proving it was significantly different from zero. This result was most likely a reflection of the small number of service sires (n = 687) represented in the AI data.
Summaries of the posterior distributions of (co)variance components, heritabilities, and genetic correlation from the CS-DC bivariate analysis are presented in Table 3. The posterior mean (SD) of the additive covariance between CS and DC was -0.70 d (0.14 d), and the corresponding genetic correlation was -0.73 (0.06). These results suggest a high, negative correlation between probability of CS and DC. A large, negative correlation indicates that, genetically, females with a higher probability of calving success will also record a shorter DC interval. Thus, selection for increased probability of CS would result in correlated decreases in DC interval. This value is similar in magnitude to that reported by Johnston et al. (2001) for the genetic correlation between the same traits (-0.66), and is slightly larger than the genetic correlation between CFI and DC (-0.66) in the current study. However, the SD and HPD (95%) intervals associated with the point estimates of genetic correlations between CS or CFI and DC in the current study indicate that they were not significantly different from each other.
|
The posterior means for additive variance and heritability for CS in this study were low in magnitude. The point estimate of h2 for CS is very similar to the estimate for the same trait reported by Johnston et al. (2001) when the categorical nature of the trait was taken into account. Higher estimates of h2 for CS have been reported in the literature when the trait was analyzed without adjustment for the categorical nature (Johnston and Bunter, 1996). The point estimates for all parameters for CS were very close to the corresponding estimates for CFI, with all estimates within the HPD (95%) intervals for CFI. These results may reflect the similar definitions of the two traits and the fact that only a small number of animals changed from a trait value of 0 under CFI to a trait value of 1 under CS (n = 2,219). The level of ECP was high under both trait definitions; 50 and 74% of herd-year classes had observations that fell into the same category for CFI and CS, respectively. The similarity of results observed in this study between CFI and CS may not hold for a dataset when complete recording is available (i.e., both successful and unsuccessful AI matings are reported, as well as information regarding NS matings that may follow). The incidence of CFI would be expected to be lower under complete recording, with a lower incidence of ECP.
There were 183 sires with more than 10 daughters with CS or CFI records, as well as more than 10 daughters with DC records. The mean (SD) DC breeding value (BV) for these sires was 0.30 d (2.57 d) with a range from -5.60 to 8.67 d. These BV were predicted using parameters from the DC-CFI analysis and are not reported for DC BV under the DC-CS analysis because of the similarity of the parameters. The mean (SD) CS BV was -0.02% (0.08), ranging from -0.28 to 0.17%. The mean (SD) CFI BV was -0.01% (0.08), ranging from -0.25 to 0.16%. The correlations between DC and CS BV and DC and CFI BV for these sires were -0.993 and -0.997, respectively. These results indicate that sires whose daughters have either an increased probability of calving success or an increased probability of calving to first insemination also produce daughters with shorter DC records. The regression coefficient of CFI on DC was -0.03% successful calving to first insemination per day; for every 1 d decrease in DC BV, there is a 0.03% increase in CFI BV, and a similar result was observed for the regression coefficient between CS and DC.
Implications
The potential of calving to first insemination as a measure of fertility in artificial insemination data has been confirmed. Parameter estimates for this trait were close to those for calving success. The former trait allows the identification of animals that not only record a calving event, but also calve to their first insemination, and the value would be even greater in a dataset more complete than that used in this study. The genetic correlations reported between days to calving and both measures of fertility in artificial insemination data indicate a strong, negative relationship. Selecting for animals with shorter days to calving intervals genetically will lead to correlated increases in both calving success and calving to first insemination. The magnitude of these correlations suggest that it may be possible to use a multitrait approach to the evaluation of artificial insemination and natural service data, but report one genetic value that could be used for selection purposes.
Footnotes
1 Appreciation is extended to the Angus Society of Australia for providing the data; Meat and Livestock Australia for the research scholarship provided to the first author, and to D. J. Johnston and C. Teseling for their contributions. 
2 Present address: Animal Genetics and Breeding Unit, University of New England, Armidale, NSW 2351 Australia.
3 Correspondence: Edgar L. Rhodes Center for Animal and Dairy Science (phone: 706-542-0949; fax: 706-583-0274; e-mail: rrekaya{at}uga.edu).
Received for publication July 3, 2003. Accepted for publication December 11, 2003.
Literature Cited
Albert, J., and S. Chib. 1993. Bayesian analysis of binary and polychotomous response data. J. Am. Statist. Assoc. 88:669–679.
Buddenberg, B. J., C. J. Brown, and A. H. Brown. 1990. Heritability estimates of calving date in Hereford cattle maintained on range under natural mating. J. Anim. Sci. 68:70–74.
Cox, D. R., and E. J. Snell. 1989. Analysis of binary data. CRC Press, London, U.K.
Devroye, L. 1986. Non-Uniform Random Variate Generation. Springer-Verlag, New York, NY.
Donoghue, K. A., R. Rekaya, and J. K. Bertrand. 2004a. Comparison of methods for handling censored records in beef fertility data: Field data. J. Anim. Sci. 82:357–361.
Donoghue, K. A., R. Rekaya, J. K. Bertrand, and I. Misztal. 2004b. Genetic evaluation of calving to first insemination using natural and artificial insemination mating data. J. Anim. Sci. 82:391–396.
Evans, J. L., B. L. Golden, R. M. Bourdon, and K. L. Long. 1999. Additive genetic relationships between heifer pregnancy and scrotal circumference in Hereford cattle. J. Anim. Sci. 77:2621–2628.
Foulley, J. L., D. Gianola, and R. Thompson. 1983. Prediction of genetic merit from data on binary and quantitative variates with an application to calving difficulty, birth weight, and pelvic opening. Genet. Sel. Evol. 15:401–424.
Johnston, D. J., and K. L. Bunter. 1996. Days to calving in Angus cattle: Genetic and environmental effects, and covariances with other traits. Livest. Prod. Sci. 45:13–22.
Johnston, D. J., J. M. Henshall, and B. Tier. 2001. Estimate of the genetic correlation between calving success and days to calving in Angus females. Pages 353–355 in Proc. 14th Conf. Assoc. Advmt. Anim. Breed. Genet., Queenstown, New Zealand.
Kadarmideen, H. N., and M. P. Coffey. 2001. Quality and validation of insemination data for national genetic evaluations for dairy cow fertility in the United Kingdom. Interbull Bulletin 27:133–138.
Misztal, I., D. Gianola, and J. L. Foulley. 1989. Computing aspects of nonlinear methods of sire evaluation for categorical data. J. Dairy Sci. 72:1557–1568.
Morris, C. A., J. A. Wilson, G. L. Bennett, N. G. Cullen, S. M. Hickey, and J. C. Hunter. 2000. Genetic parameters for growth, puberty, and beef cow reproductive traits in a puberty selection experiment. N. Z. J. Agric. Res. 43:83–91.
Raftery, A. E., and S. Lewis. 1992. How many iterations in the Gibbs sampler? Pages 763–773 in Bayesian Statistics 4. J. M. Bernando, J. O. Berger, A. P. Dawid, and A. F. M. Smith, ed. Oxford Univ. Press, New York, NY.
Rekaya, R., K. A. Weigel, D. Gianola, B. Heringstad, and G. Klemetsdal. 2000. Methods for attenuating bias of variance component estimates in threshold models when herds are small. J. Dairy Sci. 83(Suppl. 1):56–57. (Abstr.)
Schneeberger, M., B. Tier, and K. Hammond. 1991. Introducing the third generation of BREEDPLAN and GROUP BREEDPLAN. Pages 194–199 in Proc. 9th Conf. Aust. Assoc. Anim. Breeding Genetics, Melbourne, Australia.
Sorensen, D. A., S. Andersen, D. Gianola, and I. Korsgaard. 1995. Bayesian inference in threshold using Gibbs sampling. Genet. Sel. Evol. 27:229–249.
This article has been cited by other articles:
|
|
 |
|
  K. M. Cammack, M. G. Thomas, and R. M. Enns Review: Reproductive Traits and Their Heritabilities in Beef Cattle Professional Animal Scientist, October 1, 2009; 25(5): 517 - 528. [Abstract] [PDF]
|
|
|
|
 |
|
 S. W. P. Cloete, I. Misztal, and J. J. Olivier Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability J Anim Sci, July 1, 2009; 87(7): 2196 - 2208. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Hou, P. Madsen, R. Labouriau, Y. Zhang, M. S. Lund, and G. Su Genetic analysis of days from calving to first insemination and days open in Danish Holsteins using different models and censoring scenarios J Dairy Sci, March 1, 2009; 92(3): 1229 - 1239. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. I. Urioste, I. Misztal, and J. K. Bertrand Fertility traits in spring-calving Aberdeen Angus cattle. 1. Model development and genetic parameters J Anim Sci, November 1, 2007; 85(11): 2854 - 2860. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. I. Urioste, I. Misztal, and J. K. Bertrand Fertility traits in spring-calving Aberdeen Angus cattle. 2. Model comparison J Anim Sci, November 1, 2007; 85(11): 2861 - 2865. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
I. David, L. Bodin, G. Lagriffoul, C. Leymarie, E. Manfredi, and C. Robert-Granie Genetic Analysis of Male and Female Fertility After Artificial Insemination in Sheep: Comparison of Single-Trait and Joint Models J Dairy Sci, August 1, 2007; 90(8): 3917 - 3923. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. L. de Maturana, A. Legarra, L. Varona, and E. Ugarte Analysis of Fertility and Dystocia in Holsteins Using Recursive Models to Handle Censored and Categorical Data J Dairy Sci, April 1, 2007; 90(4): 2012 - 2024. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
