Livestock Research for Rural Development 23 (8) 2011 Notes to Authors LRRD Newsletter

Citation of this paper

The genetics of morphological traits in the grasscutter

S Y Annor*,***, B K Ahunu*, G S Aboagye*, K Boa-Amponsem**, K T Djang-Fordjour and J P Cassady***

Department of Animal Science Education, College of Agriculture Education, University of Education, Winneba, P.O. Box 40, Mampong-Ashanti, Ghana.
sayannor@yahoo.com
* Department of Animal Science, College of Agriculture and Consumer Sciences, University of Ghana, P.O. Box LG 571, Legon, Accra, Ghana.
** Animal Research Institute, Council for Scientific and Industrial Research, P.O. Box AH 20, Achimota, Accra, Ghana.
*** Department of Animal Science, College of Agriculture and Life Sciences, North Carolina State University, Campus Box 7621/232B Polk Hall, Raleigh NC 27695-7621, USA

Abstract

The objectives of this study were to estimate phenotypic and genetic parameters of morphological traits of the grasscutter, and to find the best morphological trait predictor of body weight. The study was carried out at the grasscutter section of the Department of Animal Science Education, University of Education, Winneba, Mampong-Ashanti campus, Ghana from 2009 to 2010. Data were recorded on morphological characteristics and body weights of 116 animals (66 females and 50 males) selected at random. Body weights and linear body measurements were recorded for the animals at birth, at weaning (2 months) and at 6 months.

 Mean body, tail and head lengths, heart girth and height-at-withers were 35.6, 10.2, 8.2, 18.0 and 11.6 cm, respectively. At birth, males and females had similar (P > 0.05) body measurements for all the morphological traits.  However, at 2 and 6 months, males had significantly (P < 0.05) longer body, head and tail lengths, larger heart girth and taller height-at-withers than females. The best predictor of body weight was heart girth. Direct genetic diversity (variability) of body length, head length and heart girth were low whilst those of tail length and height-at-withers were moderate. Proportion of phenotypic variance due to permanent environmental effects of dam (c2) was low to moderate (3-23%). Covariances between direct and maternal effects were negative (-2.3 to -29.2). Direct heritability for all traits was high (0.52-0.60) and maternal heritability was medium to high (0.39-0.75). Correlations between direct and maternal genetic effects were high negative (-0.61 to -0.78). Genetic correlations among morphological traits were medium (0.45) to high (0.85), whilst their phenotypic correlations were high (0.71 to 0.90). Genetic correlations between body weight and all morphological traits were low (0.04 to 0.23), whilst their phenotypic correlations were high (0.75 to 0.90).

 It was concluded that genetic selection will be effective in improving all the morphological traits. However, little to no correlated response in body weight would be expected. It was also concluded that heart girth can be used by farmers to predict body weight of grasscutters. It was recommended that farmers should not use head shape for sex determination at birth.

Keywords: Body length, body weight, genetic variation, head length, heart girth, height-at-withers, heritability, phenotypic and genetic correlation, tail length, Thryonomys swinderianus


Introduction

Morphology deals with the size, shape, and structure of an animal or one of its parts. Morphological characteristics or traits of animals are used for classification and identification of species or breeds. Within species or breeds, morphological characteristics are used to differentiate between sexes. The differences in the morphological body measurements of the sexes are indications of sexual dimorphism (Lindenfors et al 2007). Morphological traits are often used to predict body weights in livestock, thereby enabling poor farmers who cannot afford expensive weighing scales to use measuring tapes to estimate body weights. Body length and heart girth have long been recognized in livestock production as measures to predict body weight (Thys and Hardouin 1991; Guye et al 1998; Slippers et al 2000; Abdelhadi and Babiker 2009). Body shapes, measured objectively, could also improve selection for growth by enabling the breeder to recognize early-maturing and late-maturing animals of different size (Brown et al 1973; 1974).

The European Association of Animal Production (EAAP) and the Food and Agriculture Organization (FAO) used withers height as prime indicator of type (Simon and Buchenauer 1994). There is a group within the International Committee on Animal Recording (ICAR) that is mandated to study conformation recording (Stoll et al 1996). These indicate that morphological characterization forms an important aspect of animal production.

Important physical or morphological information that may be needed to classify uncharacterized population include coat colour, body length, height-at-withers, heart girth, tail length, head length, tail type and hair type (ILRI 1997). There is little information in the literature about morphological characteristics of the grasscutter. The objectives of this study were to estimate phenotypic and genetic parameters of morphological traits of the grasscutter, and to find the best morphological trait predictor of body weight.


Materials and methods

Study location and time

The study was carried out at the grasscutter section of the Department of Animal Science Education, University of Education, Winneba, Ghana, from 2009 to 2010. The study area has been described by Annor et al (2011).

Experimental animals and design

Data were recorded on morphological characteristics and body weights of 116 animals (66 females and 50 males) selected at random. The animals were reared from birth to 6 months. Body weights and linear body measurements on the animals were recorded at birth, weaning (2 months) and 6 months. Eighteen (18) and 5 of the 116 animals died before weaning and from weaning to 6 months, respectively. All body weights were taken by using Way Master Digital Precision Scale. The following morphological traits were measured in centimeters (cm) with tape measure:

Body length (BL): Distance from the tip of the nose to the tip of the tail.

Height-at-withers (HW): Distance from the surface of a platform to the withers

 Heart girth (HG): Circumference of chest

Tail length (TL): Distance from base of tail to the tip

 Head length (HL): Distance from the tip of the nose to the level of the 7th cervical vertebrae.

 Management of animals

The management of the experimental animals has been described in Annor et al (2011).

Data collection

Each animal record included animal (kid), sire and dam identification, sex, age, birth weight, weaning weight, 6-month weight, BL, HW, HG, TL and HL.

Statistical analysis

Data were subjected to least squares analysis using Generalized Linear Models (GLM) Type III procedure of SAS (SAS, 2008) on the following fixed models:

Where yij is the observation or trait being considered; = the overall mean; Si = the effect of the ith sex, i = 1…2; and eij is the random error term. Differences between means of significant effects were separated by the probability of difference (PDIFF) procedure of SAS (SAS, 2008).


A study on the prediction of body weight from linear body measurements was carried out by regression analysis (SAS, 2008). Body weight was regressed individually on body, tail and head lengths, heart girth and height-at-withers. The general simple linear regression equation was:

 

Where Yi is body weight or dependent variable; α is the intercept or the value of Yi when Xi = 0; β is the coefficient of regression or slope defined as the change in Yi resulting from a unit change in Xi; Xi is the independent variable represented by BL, TL, HL, HG or HW; and ei is the random residual associated with Yi. Orthogonal polynomials were fitted in SAS for each morphological trait to determine the nature of the response (linear, quadratic and cubic).

Data to estimate genetic parameters were analyzed by mixed model methodology using a full animal model and all known genetic relationships, in single or multiple trait analysis, using the MTDFREML programme (Boldman et al 1995). Parameters estimated were phenotypic variance, direct additive and maternal genetic variance, covariance between direct and maternal effects, direct additive and maternal genetic coefficient of variation, proportion of phenotypic variance due to permanent effects of dam, direct and maternal heritability, direct-maternal genetic correlations, and phenotypic and genetic correlations of traits.

Single trait analysis was done according to the general mixed model:

          where,

y = vector of observations; X = incidence matrix that associates with y; = vector of fixed effects of sex and age; a = vector of breeding values for direct genetic effects; m = vector of breeding values for maternal genetic effects; C = vector of permanent environmental effects due to dam; Zi, Zj and Zk = incidence matrices that associate a, m and c with y; and e = vector of random errors or residuals. e is unique to the observation y and is independently and identically distributed according to the Normal distribution with mean zero and variance σ2e. Furthermore, with A, the numerator relationship matrix between animals, In, an identity matrix with order n, the number of dams and I, an identity matrix with order of the number of records, the co(variance) structure of random effects can be described as : V(a) = σ2aA, V(m) = σ2mA, V(c) = σ2cIn, V(e) = σ2eI, where σ2a is the direct genetic variance, σ2m is the maternal genetic variance, σ2c is the maternal permanent environmental variance and σ2e is the residual variance.

The 2-trait animal model used for the estimates was:

 

Where, yi and yj are vectors of records of animals for trait i and trait j ; i and j are vectors of fixed effect of sex for traits i and j; ai and aj are vectors of random additive genetic effects for animals for traits i and j; mi and mj are vectors of maternal genetic effects for traits i and j; pi and pj are vectors of random permanent environmental effects for dams for traits i and j; ei and ej are vectors of random residual effects for traits i and j; Xi, Zi, and Wi are known design matrices for trait i; and Xj, Zj, and Wj are known design matrices for trait j.

There were 336 animals in the pedigree that included the base animals for the analysis. Local convergence was considered to be met if the variance of the -2 log likelihoods in the simplex was less than 1 x 10-6. After first convergence, restarts were made to find global convergence, with convergence declared when the values of -2 log likelihoods did not change to the second decimal. Heritability was categorized as low (< 0.30), medium (≥ 0.30-< 0.50) and high (≥ 0.50) (Rice et al 1970; Falconer and Mackay 1996). Correlations were classified as low (0.10 – < 0.30), medium (≥ 0.30 – < 0.50) and high (≥ 0.50 – 1.00), regardless of sign (Cohen 1988). Genetic coefficient of a trait was used as measure for ability of docility to respond to selection and to determine genetic diversity of the trait (Morris et al 1978; McLennan and Lewer 2005). Coefficient of variation was computed as CVx (%) = 100 x σx/μ, where σx is the standard deviation of the trait and μ is the estimated trait mean (Houle et al 1996). Coefficient of variation was classified as low (0-20%), medium (>20-< 40%) and high (≥ 40%).


Results

Least squares means of traits

Mean body measurements for males and females at birth, weaning and 6 months are presented in Table 1.


Table 1: Least squares means and standard errors for linear body measurements

Sex class

No.1

BL (cm)

TL (cm)

HL (cm)

HG (cm)

HW (cm)

1-day old female kids

66

21.1 0.26

6.0 0.21

5.1 0.11

11.6 0.23

7.4 0.06

1-day old male kids

50

21.7 0.30

6.5 0.25

5.2 0.12

12.0 0.26

7.5 0.06

P Value of 1-day old2

 

0.1143

0.0974

0.5985

0.3107

0.9655

60-day old females

57

33.0 1.05a

9.8 0.45a

7.3 0.33

15.6 0.35a

11.0 0.33a

60-day old males

41

37.0 1.24b

11.2 0.53b

8.2 0.39

17.4 0.41b

12.3 0.41b

P Value of 60-day old2

 

0.0147

0.0552

0.0938

0.0011

0.0153

180-day old females

52

52.1 1.01a

14.1 0.36a

11.8 0.28a

25.4 0.72a

15.7 0.58a

180-day old males

41

57.2 1.14b

16.0 0.41b

13.6 0.31b

29.8 0.81b

17.7 0.65b

P Value of 180-day old2

 

0.0012

0.0006

0.0006

<0.0001

0.0275

1Number of animals

2 Probability value of test of main effects

ab Subclass means having superscripts in common are not different.at P<0.05

Body length (BL); tail length (TL); head length (HL); heart girth (HG); height-at-withers (HW)

Effect of sex on morphological traits

At birth, males had slightly higher body measurements for all morphological traits than females (Table 1), although there was no significant (P > 0.05) difference between the two sexes for all measurements.  However, at 60 days, males had significantly longer (P < 0.05) body and tail lengths, larger (P < 0.01) heart girth and taller (P < 0.05) height-at-withers than females. At 180 days, mean body, tail and head lengths, heart girth and height-at-withers of males was higher than that of females by 10 (P < 0.01), 14 (P < 0.01), 15 (P < 0.01), 17 (P < 0.01) and 13% (P < 0.05), respectively.

 Prediction of body weight from linear body measurements

The prediction equations comprising the intercept and the slope of the regression line are presented in Table 2.  The best prediction equation was given by heart girth, followed by body length and head length, with the poorest being tail length and height-at-withers. The orthogonal polynomial contrasts (linear, quadratic and cubic) indicated highly significant (P < 0.01) effects for the models, intercepts and slopes of regression lines, only where the nature of the response was linear, indicating that all the morphological traits increased with increasing body weight in a linear fashion.


Table 2: Prediction of body weight from linear body measurements

Nature of Response

Equation

R2

 

Probability Value

Model

α

β1

β2

β3

Tail length

Linear

-669.2 + 151.9X

0.56

< 0.0001

< 0.0001

< 0.0001

-

-

Quadratic

-720.5 + 162.4X - 0.5X2

0.56

< 0.0001

0.0089

0.0023

0.8400

-

Cubic

-342.7 + 45.7X + 10.3X2 - 0.3X3

0.56

< 0.0001

0.6337

0.8296

0.5890

0.5699

Height-at-withers

Linear

-876.5 + 151.9X

0.63

< 0.0001

< 0.0001

< 0.0001

-

-

Quadratic

-525.1 + 93.8X + 2.1X2

0.63

< 0.0001

0.1003

0.0653

0.2480

-

Cubic

238.7 - 91.2X + 15.9X2 - 0.3X3

0.63

< 0.0001

0.7955

0.6719

0.3120

0.3769

Head length

Linear

-833.8 + 208.4X

0.73

< 0.0001

< 0.0001

< 0.0001

-

-

Quadratic

-148.5 + 36.4X + 9.1X2

0.73

< 0.0001

0.4780

0.4637

0.0005

-

Cubic

871.7 - 359.8X + 55.1X2 - 1.6X3

0.74

< 0.0001

0.0967

0.0631

0.0120

0.0345

Body length

Linear

-1045.7 + 53.2X

0.77

< 0.0001

< 0.0001

< 0.0001

-

-

Quadratic

-107.5 - 0.3X + 0.7X2

0.78

< 0.0001

0.6286

0.9830

< 0.0001

-

Cubic

84.0 - 16.3X + 1.1X2 - 0.003X3

0.78

< 0.0001

0.8957

0.7528

0.4087

0.7498

Heart girth

Linear

-1085.6 + 108.5X

0.82

< 0.0001

< 0.0001

< 0.0001

-

-

Quadratic

-957.9 + 95.0X + 0.3X2

0.82

< 0.0001

< 0.0001

< 0.0001

0.5097

-

Cubic

370.4 - 129.3X + 11.9X2 - 0.2X3

0.82

< 0.0001

0.3748

0.0478

0.0003

0.0003

Dependant variable (X); Co-efficient of multiple determination (R2); Intercept (α); Slope of regression line (β1, β2, β3)

Variation and heritability

Estimates for components of variance and covariance of traits are presented in Table 3. Absolute values of phenotypic, direct and maternal genetic variances were generally low for all the traits, except body length. Permanent environmental effects of dam (c2) accounted for 3-23% of the total phenotypic variation. Covariance between direct and maternal effects was negative. The CVg indicated that BL, HL and HG had low direct genetic diversity (variability). Tail length and HW had medium direct genetic diversity. Body length and HG had low CVm while TL, HL and HW had medium CVm.


Table 3: Estimates of components for variance and covariance of traits**

Trait**

σ2p

σ2g

σ2m

c2

σ gm

CVg

(%)

CVm

(%)

Body length

72.8

43.9

52.7

0.07

-29.2

17.3

18.9

Tail length

9.1

5.2

5.3

0.03

-4.1

20.7

20.9

Head length

5.2

2.7

3.6

0.23

-2.3

18.7

21.6

Heart girth

14.2

7.5

5.6

0.04

-4.4

14.3

12.3

Height-at-withers

11.3

5.9

8.4

0.11

-4.3

19.7

23.6

**σ2p = Phenotypic variance; σ2g = Direct additive genetic variance; σ2m = Maternal genetic variance; c2 = proportion of phenotypic variance due to permanent effects of the dam; σgm = Covariance between direct and maternal genetic effects; CVg = Direct genetic coefficient of variation; CVm = Maternal genetic coefficient of variation

Estimates of heritability and genetic correlations between direct and maternal effects are presented in Table 4. Direct heritability for all traits was high (0.52-0.60) and h2m was medium to high (0.39-0.75). Correlations between direct and maternal genetic effects (rdm) were high negative.


Table 4: Estimates of direct and maternal heritability, and genetic correlation of traits**

Trait

h2d

s.e. of h2d

h2m

s.e. of h2m

rdm

s.e. of rdm

Body length

0.60

0.160

0.72

0.110

-0.61

0.126

Tail length

0.58

0.215

0.58

0.129

-0.78

0.117

Head length

0.52

0.198

0.68

0.131

-0.73

0.140

Heart girth

0.53

0.203

0.39

0.120

-0.69

0.165

Height-at-withers

0.52

0.171

0.75

0.108

-0.61

0.137

**h2d = Direct heritability; h2m = Maternal heritability; rdm = Genetic correlation between direct and maternal effects; s.e. = Standard error

Phenotypic and genetic correlations

Genetic and phenotypic correlations among morphological traits and between morphological traits and body weight are presented in Table 5. Genetic correlations among morphological traits were medium (0.45) to high (0.85), whilst their phenotypic correlations were high (0.71 to 0.90). Genetic correlations between body weight and all morphological traits were low (0.04 to 0.23), whilst their phenotypic correlations were high (0.75 to 0.90).


Table 5: Genetic (above diagonal) and phenotypic (below diagonal) correlations among traits measured

 

Body weight

Body length

Tail

length

Head length

Heart

girth

Height-at-withers

Body weight

 

0.04

0.23

0.23

0.23

0.23

Body length

0.88

 

0.68

0.68

0.59

0.76

Tail length

0.75

0.88

 

0.73

0.61

0.74

Head length

0.85

0.90

0.86

 

0.45

0.45

Heart girth

0.90

0.85

0.71

0.83

 

0.85

Height-at-withers

0.79

0.81

0.75

0.75

0.79

 

Discussion

Least squares means of traits

Average BL at birth (21.1 cm for females and 21.7 cm for males) and at 60 days (33.0 cm for females and 37.0 cm for males) obtained in this study are higher than values reported by Ikpeze and Ebenebe (2004a) (17.2 and 17.4 cm at birth and 21.3 cm and 22.5 cm at 60 days for females and males, respectively).  Differences in the two studies emanate from inclusion of TL in the measurement of BL in this study; otherwise the results are in agreement. Mean HG and HW in this study were similar to those reported by Ikpeze and Ebenebe (2004a).   

Mean adult BL of 52.1 cm and 57.2 cm obtained for 180-day old females and males, respectively fall within the range of 42-58 cm reported by Schrage and Yewadan (1999) and 35-61 cm reported by Nowak (1999). Mean adult TL (14.1 for females and 16.0 for males) observed in this study also falls within the range of 6.5-26.0 cm reported by Nowak (1999). However, mean adult TL (above) and HW (15.7 cm for females and 17.7 cm for males) reported in this study are below the 22-25 cm and 23-30 cm, respectively, reported by Schrage and Yewadan (1999). The differences may be due to differences in the ages of the animals. In this study data for growth traits terminated at 6 months, which was taken as adulthood whilst the other studies considered adulthood to be over 12 months.

Jayeola et al (2009) reported an average HL of 15.0 cm with a range of 12.1-17.5 cm for adult grasscutters aged over 12 months. The average HL of 11.8 cm obtained in this study for females fell slightly below the range reported by Jayeola et al (2009). However, the value for the males (13.6 cm) was within the ranges reported. Most studies indicate that the male grasscutter has a bigger head with a gently sloping and bluntly terminating snout, whilst the female has a comparatively smaller head with a steeply sloping and much more pointed snout (Schrage and Yewadan 1999; Adu et al 2002). The HL of the male grasscutter is longer than that of the female, as was observed in this study.

Effect of sex on morphological traits

Males and females had similar body measurements at birth, but males had longer BL, TL and HL, larger HG and taller HW than females at 60 and 180 days of age. These observations are characteristics of the grasscutter (Nowak 1999; Schrage and Yewadan 1999; Adu et al 2002; Ikpeze and Ebenebe 2004a; Jayeaola et al 2009) and mammals (Lindenfors et al 2007).

Efficient grasscutter reproductive management requires ability of farmers to distinguish between the two sexes. Although sex determination is not a problem in most livestock species, it is a major problem in the grasscutter (Annor et al 2009). The clitoris of the female is much like the penile sheath of the male and can be deceptive to the untrained eye (Adu et al 1999). The most common techniques employed by farmers in sex determination are the use of (1) head shape and/or head size and (2) ano-genital distance (Adu et al 2002). In mammals, it has been established that differentiation into the male phenotype begins during prenatal life as a result of the secretion of testicular androgen (Block et al 1971). In mice and rats, this process continues at least through the first week of postnatal life as well, though at birth, male and female rats and mice can be easily distinguished by external examination of the length between the anus and genital papilla or by internal examination of the accessory sex organs and gonads. Early experiments with rats (Arai and Gorski 1968) and mice (Edwards and Brge 1971) suggested that there was a critical developmental period that commenced shortly after birth, with both physiological and behavioural masculinization and defeminization occurring in response to androgen exposure during this time, and thus makes it difficult to differentiate between males and females. In the light of the above, head shape or size may not adequately determine sex at a very early age of the grasscutter, especially at birth, since this study found no differences in head length, and other linear body measurements of the two sexes. It is therefore recommended that farmers should not use head shape for sex determination at birth.

Prediction of body weight from linear body measurements

Negative intercepts were observed in all the linear equations. The value of the intercept is the weight of the animal when any of the measures (body, tail and head lengths, height-at-withers and heart girth) is zero. This condition is only a statistical supposition and not real since all animals were born with head, tail etc. Zero values were not encountered in any of the measures. The negative intercepts in the equations might probably therefore mean that it is not possible to achieve zero independent variables.

Heart girth was found to be the best predictor of body weight, followed by BL. The findings are in agreement with results in sheep (Thys and Hardouin 1991), poultry (Guye et al 1998), goats (Slippers et al 2000) and cattle (Abdelhadi and Babiker 2009). However, in the grasscutter, Ikpeze and Ebenebe (2004b) and Jayeola et al (2009) respectively observed that BL and HL were the best predictors of body weight. Nevertheless, the sample sizes used in the last two studies were small and therefore make them partially unreliable. It can be recommended that heart girth could adequately be used to predict body weight in grasscutters.

Variation and heritability of morphological traits

Moderate direct genetic variation was observed in TL and HW whilst low genetic variations were found in BL, HL and HG. The moderate genetic variation would enable selection pressure to be exerted in a breeding programme to alter or improve TL and HW (Rhees and Atchley 2000; Janssens and Vandepitte 2004; Gizaw et al 2008). The low to moderate proportion of permanent environmental influence due to the dam observed in the morphological traits indicates that the dam has influence on these traits (Mandal et al 2008). These environmental influences (epigenetic effects) are possibly due to uterine capacity, feeding level at late gestation, and maternal behaviour of the dam (Maria et al 1993).  The negative covariance between direct and maternal effects agrees with studies by Mandal et al (2011).

The high direct heritability obtained for all morphological traits means that genetic selection will be effective in improving performance levels of these traits (Rhees and Atchley 2000; Janssens and Vandepitte 2004; Gizaw et al 2008). Medium to high (0.30-0.72) direct heritability has been reported for morphological traits in farm animals (Janssens and Vandepitte 2004; Gizaw et al 2008) and rodents (Cheung and Parke 1974). Maternal heritability estimates for all traits were high. This indicates that maternal effects are of significant importance in morphological traits (Reinhold 2002), and must be accounted for in breeding value estimation (Mandal et al 2008). The negative genetic correlation between direct and maternal effects indicates antagonistic effects (Bryner et al 1992; Madal et al 2008; Mandal et al 2011).

Phenotypic and genetic correlations

Medium to high positive genetic correlations obtained among morphological traits indicates that selection for any one of them will improve the other. In addition, high positive phenotypic correlation among the same traits indicates that any one of them can be used to predict the other (Hohenboken, 1985). Similar results were reported in the grasscutter (Ikpeze and Ebenebe 2004b; Jayeola et al 2009) and sheep (Abbasi and Ghafouri-Kesbi 2011).

The low genetic correlations between live weight and morphological traits indicate that selection in any of the morphological traits may not improve live weight and vice versa (Hohenboken 1985). The high phenotypic correlation between live weight and morphological traits indicates that one trait can be used to predict the other (Ikpeze and Ebenebe, 2004b and Jayeola et al 2009; Salako 2006; Abbasi and Ghafouri-Kesbi 2011). Heart girth was the best predictor of body weight because it is part of tissue measurements, while other measurements are related to skeletal measurements (Blackmore et al 1958).


Conclusion


Acknowledgement

The authors are grateful to the Teaching and Learning Innovative Fund (TALIF), Ghana for providing grasscutter facilities for this research.


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Received 13 May 2011; Accepted 15 June 2011; Published 3 August 2011

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