Covariance Components Research Articles

The Use of Bioelectrical Impedance to Assess Shelf-Life of Beef Longissimus Dorsi

ObjectivesTo evaluate quality attributes of beef longissimus dorsi (LD) during 15 d of simulated retail display using surface and internal bioelectrical impedance analysis (BIA) measurement techniques.Materials and MethodsThe experiment was designed as a split-plot with loin as the whole-plot and pairekd steaks as the sub-plot. Display day (DD) was treated as the sub-plot treatment. Postmortem age time (PM) and DD were treated as fixed effects. Beef strip loins (N = 18; IMPS #180), obtained from 3 commercial processors (PM = 27, 34, or 37 d), were fabricated into 12 2.54-cm thick steaks (N = 216). Steaks were subdivided into 6 consecutively cut pairs and pairs were randomly assigned to one of 6 display days: 0, 3, 6, 9, 12, and 15. For all pairs, one steak was allocated to microbiological analysis and pH and the paired steak for BIA, objective color assessment, proximate composition, and TBARS. Surface BIA (S-BIA) and internal BIA (I-BIA) assessment were compared. Steaks were packaged on styrofoam trays with a moisture absorbent pad, overwrapped with polyvinyl chloride film, and displayed under fluorescent lighting at 0–4°C in coffin-style retail cases.ResultsThere was a PM × DD interaction (P < 0.05) for S-BIA values. From d 0 to 12 of display, steaks aged 27 d had higher (P < 0.05) S-BIA values than steaks aged 34 and 37 d; however, on d 15 of display, steaks aged 34 d had 22% higher (P < 0.05) S-BIA values than steaks aged 37 d, but had similar (P > 0.05) values compared to steaks aged 27 d. There was no PM × DD interaction (P < 0.05) for I-BIA values; however, an effect on PM and DD was found (P < 0.05). Steaks aged 27 d were 17% higher for I-BIA values (P < 0.05) than 37 d, but similar (P > 0.05) to steaks aged 34 d. For all PM aging times, d 0 had the lowest (P < 0.05) I-BIA values among all display days with 81.44. D 3 was the second lowest (P < 0.05) and 8% higher than d 0 for I-BIA values. D 6 was 16% higher (P < 0.05) than d 3 but similar (P > 0.05) to d 9 and d 12. D 12 and D 15 were similar (P > 0.05). There was a DD × BIA method interaction (P < 0.05). On d 0, 3, and 6, BIA values were different (P < 0.05); however, after d 6 onward, BIA values were similar (P > 0.05). Covariance component was smaller in I-BIA than S-BIA. There were no PM × DD interactions (P > 0.05) for a* and b* values; however, there was an interaction for L* values. Postmortem aging had no effect (P > 0.05) on L*; however, an effect on a* and b* was found (P < 0.05). For APC populations, there was a PM × DD interaction (P < 0.05). No PM × DD interaction or PM effect (P > 0.05) were found for TBARS; however, there was a DD effect (P < 0.05). There was no PM day × DD interaction (P > 0.05) or PM day (P > 0.05) for moisture content. Display day (P < 0.05) had an effect on moisture content. Moderate negative correlations occurred between S-BIA values and a*, b*, and moisture content with –0.48, –0.46, and –0.46, respectively; and –0.51, –0.48, and –0.43, respectively, for I-BIA. Conversely, moderate positive correlation was found between S-BIA values and APC and TBARS with 0.34 and 0.53, respectively; and 0.29 and 0.51, respectively, for I-BIA.ConclusionI-BIA has potential for use to assess shelf-life of retail steaks and it was more precise than S-BIA; however, I-BIA may translocate bacteria into the muscle. Protein degradation and WHC should be evaluated to better understand BIA changes over time.

Genetic and Phenotypic Parameters and Trends for Milk Yield, Service Period and Calving Interval and The Economic Impact of Extending A Calving Interval on-Farm Profitability in Friesian Cows in Egypt

Genetic parameters for Friesian cows were estimated from 9155 lactation records for 3635 cows sired by 184 bulls covered 34 years (1982-2015) in two herds Sakha and El-Karada , belonging to the Animal Production Research Institute, Ministry of Agriculture and Land Reclamation, Egypt. Multi-trait repeated animal models were employed using the REML procedure to estimate covariance components of total milk yield (TMY), service period (SP) and calving interval (CI). There is sufficient genetic variation (indicated by the coefficient of genetic variation) in total milk yield, service period and calving interval traits (15.6, 3.6 and 3.7%, respectively). Estimates of heritability for TMY, SP and CI traits were 0.170, 0.010 and 0.044, respectively. Permanent environmental variance ratios were 0.162, 0.017 and 0.048 for TMY, SP, and CI, respectively. Genetic correlations among SP and CI traits were high (0.998), however genetic correlations of reproductive with productive traits were unfavorable (0.512-0.661). The results indicated the possibility of continued selection for increased milk production which has deleterious effects on reproductive traits. The annual genetic changes were positive for TMY, SP, and CI (7.76 kg/yr, 0.076, and 0.24 d/yr, respectively). These positive trends indicate that there has been success in choosing better sires.The corresponding annual phenotypic changes for the same traits were positive (13.9 kg/yr, 0.029 and 0.82 d/yr, respectively). Positive annual genetic and phenotypic changes for SP and CI indicated that with improving milk yield the SP and CI will be increased. The financial situation of the farm is affected by the extension of the calving interval. The lower the calving interval, the more profitable the farm becomes, and prolonging this period for one day causes a financial loss for the farm.

Using portfolio theory in spatial targeting of forest carbon payments: an effective strategy to address spatiotemporal variation in land-use opportunity costs?

The objective of our research is to extend current conservation applications of modern portfolio theory (MPT) to develop a framework for the cost-efficient budget distribution for a forest carbon payment program that optimizes risk–reward trade-offs in the presence of economic growth uncertainty over time. We consider correlation across space and time of the fluctuating opportunity costs of restoring forestland under changing future economic conditions using a case study of eight states in the central and southern Appalachian region of the United States. The findings suggest that optimal budget allocation decisions that ignore the covariance component of the spatial variance–covariance structure of forest carbon returns fail to minimize the true risk of conservation investment for any level of expected return. The importance of incorporating the spatial covariance in targeting conservation payments is made explicit through alternative approaches using multi-objective (mean–variance) optimization and an ex post analysis with and without the covariance component of the spatial variance–covariance structure of forest carbon return on investment (ROI). A comparison of these approaches against our MPT-based approach revealed misleading risk–return expectations if the ROI covariance is ignored in the spatial targeting of forest carbon payments under uncertainty.

Variance-covariance component estimation for structured errors-in-variables models with cross-covariances

In this contribution, an iterative algorithm for variance-covariance component estimation based on the structured errors-in-variables (EIV) model is proposed. We introduce the variable projection principle and derive alternative formulae for the structured EIV model by applying Lagrange multipliers, which take the form of a least-squares solution and are easy to implement. Then, least-squares variance component estimation (LS-VCE) is applied to estimate different (co)variance components in a structured EIV model. The proposed algorithm includes the estimation of covariance components, which is not considered in other recently proposed approaches. Finally, the estimability of the (co)variance components of the EIV stochastic model is discussed in detail. The efficacy of the proposed algorithm is demonstrated through two applications: multiple linear regression and auto-regression, on simulated datasets or on a real dataset with some assumptions.

Maternal effect on sports performance traits in horses

The aim of the study was to estimate the direct additive genetic effect and the additive maternal effect on the level of traits estimated during the Polish Jumping Championships for Young Horses. The investigations involved 541 stallions and 353 mares, which in total started in the Championships 1232 times. Variance and covariance components were estimated using the Gibbs sampling method. Heritability (h<sup>2</sup>) and repeatability (r<sup>2</sup>) coefficients as well as maternal effects (m<sup>2</sup>) were calculated for 7 sports performance traits. There was an additive maternal effect, ranging from 0.11 to 0.39, on the level of traits assessed based on achieved scores. The effect was particularly high in the case of traits the level of which was determined by the animal organism performance and stress resistance. It was also noted that the value of the maternal effect in some traits was similar or higher than the coefficient of heritability, which may indicate a high effect of the mare’s specific environment in determining sport predispositions in the offspring. There is a need to analyse the cause of trait variability in other equestrian disciplines.

EIGENVALUE DISTRIBUTIONS OF VARIANCE COMPONENTS ESTIMATORS IN HIGH-DIMENSIONAL RANDOM EFFECTS MODELS.

We study the spectra of MANOVA estimators for variance component covariance matrices in multivariate random effects models. When the dimensionality of the observations is large and comparable to the number of realizations of each random effect, we show that the empirical spectra of such estimators are well-approximated by deterministic laws. The Stieltjes transforms of these laws are characterized by systems of fixed-point equations, which are numerically solvable by a simple iterative procedure. Our proof uses operator-valued free probability theory, and we establish a general asymptotic freeness result for families of rectangular orthogonally-invariant random matrices, which is of independent interest. Our work is motivated in part by the estimation of components of covariance between multiple phenotypic traits in quantitative genetics, and we specialize our results to common experimental designs that arise in this application.

ACE of space: estimating genetic components of high-dimensional imaging data.

It is of great interest to quantify the contributions of genetic variation to brain structure and function, which are usually measured by high-dimensional imaging data (e.g., magnetic resonance imaging). In addition to the variance, the covariance patterns in the genetic effects of a functional phenotype are of biological importance, and covariance patterns have been linked to psychiatric disorders. The aim of this article is to develop a scalable method to estimate heritability and the nonstationary covariance components in high-dimensional imaging data from twin studies. Our motivating example is from the Human Connectome Project (HCP). Several major big-data challenges arise from estimating the genetic and environmental covariance functions of functional phenotypes extracted from imaging data, such as cortical thickness with 60 000 vertices. Notably, truncating to positive eigenvalues and their eigenfunctions from unconstrained estimators can result in large bias. This motivated our development of a novel estimator ensuring positive semidefiniteness. Simulation studies demonstrate large improvements over existing approaches, both with respect to heritability estimates and covariance estimation. We applied the proposed method to cortical thickness data from the HCP. Our analysis suggests fine-scale differences in covariance patterns, identifying locations in which genetic control is correlated with large areas of the brain and locations where it is highly localized.

Estimation of genetic parameters for chosen udder traits of different dairy ewes

Morphology of udder in ewes in one experimental herd was examined on the basis of linear evaluation (7 traits; 9 points scale; 1145 data for each trait) and exact measurements of udder (6 parameters; 1055 measurements at each trait) were taken. Estimation of covariance components and genetic parameters were done using REML and BLUP-AM (VCE 4.0 package) for 7 traits (linear evaluation) or 6 traits (udder measurements) . Heritability coefficients for individual traits of linear evaluation varied from 0.090–0.294. Heritability coefficient (h2) for teat position was 0.242, 0.275 for teat size , and 0.294 for cistern depth, which is related to teat position. The highest value of genetic correlation (rg=0.980) was found between cistern depth and teat position. Negative genetic correlation was detected between teat position and teat size (–0.381), and teat position and udder cleft (–0.404). Heritability coefficients and genetic correlations of exact measurements of udder were comparable with traits of linear evaluation. For the parameter, udder length and rear udder depth, the detected band-measures were h2 = 0.219 or 0.223. Highest h2 was found for cistern depth (0.480), teat length (0.358) and teat angle (0.290) similar to linear evaluation.

Genetic evaluation of a 15-year selection program for high growth in striped catfish Pangasianodon hypophthalmus

This study attempted to make a comprehensive genetic evaluation of a selective breeding program for striped catfish Pangasianodon hypophthalmus selected for high growth over 15 years from 2001 to 2015. Statistical analyses were carried out on 54,259 individual fish that were offspring of 554 sires and 926 dams (955 families in total). Both univariate and bivariate linear animal mixed models were used to estimate variance and covariance components for four groups of traits studied: (i) body traits (harvest body weight and standard length), (ii) carcass traits (fillet weight and fillet yield), (iii) fish body condition (condition index) and (iv) growth survival of each individual during grow-out phase. Medium to high heritability (0.31–0.54) were estimated for body traits, while carcass and condition index had medium heritability (0.17 to 0.20). Phenotypic and genetic correlations among body traits (weight and length) were high and close to one. The growth-related traits showed moderate and positive (i.e., favourable) genetic correlations with fillet yield (0.67–0.92) and condition index (0.28–0.49). Direct response to selection for high growth was 9.3% per generation. The selection program increased fillet weight and growth survival by 8.5% and 7.4%, respectively. However, there was insignificant correlated change in fillet yield (only 1.6%). Collectively, our results showed that there are substantial genetic variations in all traits studied and selection for high growth resulted in beneficial changes in fillet weight, fish body condition and survival during grow-out phase. The improved strain of striped catfish had 13.4% greater growth performance than their wild counterparts of the latest generation, suggesting that culture of the improved striped fish line may enhance productivity and economic return for small householder farmers and commercial producers.

Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome.

The integrated responses of biological systems to genetic and environmental variation result in substantial covariance in multiple phenotypes. The resultant pleiotropy, environmental effects, and genotype‐by‐environmental interactions (GxE) are foundational to our understanding of biology and genetics. Yet, the treatment of correlated characters, and the identification of the genes encoding functions that generate this covariance, has lagged. As a test case for analyzing the genetic basis underlying multiple correlated traits, we analyzed maize kernel ionomes from Intermated B73 x Mo17 (IBM) recombinant inbred populations grown in 10 environments. Plants obtain elements from the soil through genetic and biochemical pathways responsive to physiological state and environment. Most perturbations affect multiple elements which leads the ionome, the full complement of mineral nutrients in an organism, to vary as an integrated network rather than a set of distinct single elements. We compared quantitative trait loci (QTL) determining single‐element variation to QTL that predict variation in principal components (PCs) of multiple‐element covariance. Single‐element and multivariate approaches detected partially overlapping sets of loci. QTL influencing trait covariation were detected at loci that were not found by mapping single‐element traits. Moreover, this approach permitted testing environmental components of trait covariance, and identified multi‐element traits that were determined by both genetic and environmental factors as well as genotype‐by‐environment interactions. Growth environment had a profound effect on the elemental profiles and multi‐element phenotypes were significantly correlated with specific environmental variables.

Genetic evaluation of growth in Barki sheep using random regression models.

The objective of the current study was to estimate covariance components of growth at different ages from birth to yearling in Barki lambs. A total of 16,496 records for body weights at birth (W0), 3 (W3), 6 (W6), 9 (W9), and 12 (12) months of age for Barki lambs were available. Two statistical approaches were used; multi-trait (MT) and random regression (RR) animal models assuming two random effects only, additive genetic effect (σ2a) and permanent environmental effect (σ2pe) of the animal. Regarding the RR model, Legendre polynomials (LP) of different orders for the random parts were compared in order to evaluate the most appropriate model. Bayesian information and Akaike information criteria suggested that the optimal RR model included the third order for fixed effect of lamb age and σ2pe, and fourth order of LP for σ2a (LP343). Estimates of direct heritability (h2a) from LP343 showed an ascending pattern, as it was 0.06 ± 0.03 for birth weight and reached to the peak at 9months (0.42 ± 0.02). Thereafter, it declined again at the end of trajectory (12 months of age; 0.27 ± 0.03). The MT model showed a fluctuated pattern and lower estimates of h2a (0.19 ± 0.03, 0.11 ± 0.02, 0.12 ± 0.02, 0.11 ± 0.03, and 0.16 ± 0.04 for W0, W3, W6, W9, and W12, respectively). Considerably, similar ascending patterns of the ratio of σ2pe to phenotypic variance were reported from both RR (from 3 to 50%) and MT models (from 5 to 20%). Of interest, the RR model showed higher predicting ability of the breeding values compared with the MT model, which is an indicator for the suitability of RR models for analyzing the consecutive growth traits in sheep. Results suggested that the Barki sheep has a potential for genetic selection based on weight at different ages with selection likely to be more efficient at 9 months of age.

Inheritance, genetic correlation and cluster analyses of fecal egg count, packed cell volume and body weight in different ages using random regression model in Santa Ines sheep

The objectives of this study were to estimate the genetic parameters for body weight (BW), packed cell volume (PCV) and fecal egg count (FEC) in Santa Ines sheep using random regression models in order to indicate which traits could be used as selection criteria and to explore the additive genetic pattern of the animals using cluster analysis in order to select animals that attend the breeding goals. The dataset had 4608 records. The covariance components for traits were estimated using the restricted maximum likelihood (REML) method, by means of single trait random regression models. The cluster analyzes was performed in the software R. The random regression models using Legendre polynomial with 3 parameters (intercept, linear and quadratic) to model the genetic additive effect and permanent environment presented the best fit for the three traits. Heritability estimates for BW ranged from 0.02 (0.02) to 0.40 (0.03). The selection of these animals for PCV and FEC would result in low efficiency due to the low estimates of heritability (0.01 ± 0.01–0.18 ± 0.02). Through of the non-hierarchical cluster analysis, only one group presented a genetic profile indicated for selection. It is recommended the selection of animals based on BW (h2 = 0.12 ± 0.05) and PCV (h2 = 0.18 ± 0.03) from 180 days of age, because although the low heritability estimate, the obtained gains will be permanent.

Estimation of co-variance components, genetic parameters and genetic trends of VIAscan carcass traits of New Zealand Romney lambs

The aim of this study was to estimate co-variance components, genetic parameters and direct genetic trends of carcass traits of New Zealand Romney lambs assessed by VIAscan technology. Data for VIAscan carcass traits of 3389 lambs that included hot carcass weight (VHW); subcutaneous fat depth near the 12th rib (VGR) and meat yield (expressed as a percentage of VHW in the shoulder (SHY%), loin (LNY%), hind leg (LGY%) and whole carcass (TY%) were analyzed by employing different animal models for univariate and multivariate analysis using restricted maximum likelihood (REML) procedures with ignoring or including maternal effects. Year of birth, parity of ewe and type of birth were included in the model as fixed factors, as well as the linear covariate of age of lamb at drafting was fitted into the model. Based on the most appropriate model, estimates of direct heritability (h2a) ranged from moderate to high for VHW (0.29 ± 0.02), VGR (0.56 ± 0.09), SHY% (0.34 ± 0.07), LNY% (0.37 ± 0.06), LGY% (0.38 ± 0.08) and TY% (0.31 ± 0.02), however, the estimates of maternal heritability (h2m) ranged from low to moderate for VHW (0.10 ± 0.03), VGR (0.38 ± 0.08), SHY% (0.18 ± 0.02), LNY% (0.15 ± 0.04), LGY% (0.19 ± 0.02) and TY% (0.17 ± 0.02). VHW was positively genetically correlated with all VIAscan carcass traits (0.151 to 0.563) except LGY% (−0.145). VGR was positively genetically correlated with both VHW (0.563) and LNY% (0.003), and negatively genetically correlated with the rest of carcass traits (−0.008 to −0.472). The genetic correlation of TY% was negative with VGR (−0.229) and positive with the other carcass traits (0.151 to 0.878). SHY%, LNY% and LGY% were moderately positively genetically correlated with each other with a range (0.538–0.601). Estimating the direct genetic trends of VIAscan carcass traits during the period of study indicated that a high genetic progress was achieved in the desired direction for VHW, LNY%, LGY% and TY%, and in the undesired direction for VGR. The moderate to high heritability estimates for VIAscan carcass traits indicated that these traits could respond to direct selection. Such information is important in designing breeding strategies for improving carcass traits in New Zealand Romney lambs.

Spatial Incomplete Panel Data Models with Two-Way Error Components

When the panel is incomplete, which is the rule rather than the exception, standard estimation methods cannot be applied. This paper considers a model with spatial lag and two way-way error components regression with unbalanced data. The paper derives several estimators for structural parameters. It also develops more intensively ANOVA estimators for covariance components. The Monte Carlo experiments in which the design varies in, (a) the degree of unbalancedness in the data, (b) the variance components ratio, (c) the spatial matrix and (d) the spatial coefficient, compare the performance of theses estimators. Some of the basic findings are the following: (1) Better estimates of the variance components do not necessarily imply better estimates of the regression coefficients. (2) Making the data balanced, by dropping observations, worsens the performance of these estimators when compared to those from the entire unbalanced data.

Genetic analysis of skinfold thickness and its association with body condition score and milk production traits in Chinese Holstein population

The skin has many important roles in dairy cattle, and skinfold thickness could be used as an indicator of body fat deposition. The objectives of this study were to estimate genetic parameters of skinfold thickness and to explore its association with body condition score (BCS) and milk production traits in a Chinese Holstein population. Skinfold thicknesses over the neck (STN) and the last rib (STR), BCS, and test-day records of milk production traits were available for 6,416 lactating Holstein cows in the summers of 2015 and 2016 in Beijing, China. Multi-trait animal models were used to estimate variance and covariance components using the DMU software. The average STN was 7.15 ± 1.28 mm, and the average STR was 11.76 ± 1.95 mm (mean ± standard deviation). Estimated heritability was 0.13 ± 0.03 for STN and 0.26 ± 0.04 for STR. We detected a high genetic correlation (0.79 ± 0.08; heritability ± standard error) between STN and STR. Genetic correlations between skinfold thickness and BCS were low to moderate: 0.18 between STR and BCS, and 0.33 between STN and BCS. Genetic correlations between skinfold thickness and milk yield, milk fat percentage, and milk protein percentage were negligible, ranging from -0.02 to 0.15. Collectively, skinfold thickness is characterized as a trait with moderate heritability. Skinfold thickness is sensitive to changes in body condition or fat deposition across parities and lactation stages in milking cows, and we confirmed the complementary nature of skinfold thickness and BCS genetically as well as phenotypically by comparing their changing trends throughout lactation and across lactations. The use of skinfold thickness, together with BCS, can assist in the monitoring of changes in body fat deposition to achieve higher management precision.

Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources Based on Matrix Factorization.

Accurate estimation of the locations and extents of neural sources from electroencephalography and magnetoencephalography (E/MEG) is challenging, especially for deep and highly correlated neural activities. In this study, we proposed a new fully data-driven source imaging method, source imaging based on spatio-temporal basis function (SI-STBF), which is built upon a Bayesian framework, to address this issue. The SI-STBF is based on the factorization of a source matrix as a product of a sparse coding matrix and a temporal basis function (TBF) matrix, which includes a few TBFs. The prior of the TBF is set in the empirical Bayesian manner. Similarly, for the spatial constraint, the SI-STBF assumes the prior covariance of the coding matrix as a weighted sum of several spatial covariance components. Both the TBFs and the coding matrix are learned from E/MEG simultaneously through variational Bayesian inference. To enable inference on high-resolution source space, we derived a scalable algorithm using convex analysis. The performance of the SI-STBF was assessed using both simulated and experimental E/MEG recordings. Compared with L2-norm constrained methods, the SI-STBF is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the spatio-temporal factorization of source matrix, the SI-STBF also produces more accurate estimations than spatial-only constraint method for high correlated and deep sources.

The use of new technologies in horse selection

Received: 2018-09-12 | Accepted: 2018-11-27 | Available online: 2018-12-31 https://doi.org/10.15414/afz.2018.21.04.186-189 Most successful selection strategies are not in use in horse selection. This study describes the use of current strategies of selection in horse and tries to answers why well known, scientifically proven selection strategies are still not implemented. In the era of genomic selection (GS) in animal breeding, decision making in horse selection, even classical values (BV), are not fully taken advantage of. Breeding organizations (BO) which implement BVs as selection criteria in their programs show great success in genetic gain. However, a horse's achievements and the popularity of its relatives still plays an important role in selection level. Genomic based selection tools in horse have limitations in quantitative traits since it is difficult to establish reference population. Mendelian traits have been studied for several years, especially those related to horse health and functional longevity (long sport or race careers), yet there are still only a small number of validated mendelian traits offered for horses. The important benefit of GS is the prevention of related mating based on genomic data, in addition to pedigree data. The specialty in horse is the use of cloning. From the point of view of genetic variability, cloning is useful in enabling geldings or non-genetically infertile animals to carry genes to next generations. In sport and race BOs, stallions from foreign BOs are frequently used. In such cases a need for comparable BVs exists. These factors were the motivation behind the establishment of Interstallion 20 years ago. Due to difficulties in BO collaboration, Interstallion has not been as successful as planned. On the whole, it is expected that the sector of horse selection will need to change considerably in the future. 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Parameters and genetic trends for reproductive characteristics of a closed Angus herd

Background: Closed breeding populations are usefull to conduct basic and applied research. The Wye Angus herd is one of them. It was founded using only a few animals. The pedigree of the descendants of the original herd can be completely described by historical records resulting from strong selection. Wye Angus genetics has influenced that of Aberdeen Angus, Red Angus, and Brangus cattle worldwide. Objective: To evaluate parameters and genetic trends associated with the reproduction traits of the Wye Angus herd between the years 1937 and 2012. Methods: We used pedigree information of 11,692 individuals. The reproductive traits assessed were age at first calving (AFC), calving interval (CI), and scrotal circumference (SC). The covariance components were estimated by Bayesian inference. The genetic trends were obtained by linear regression of the genetic values over birth years of the animals. Results: The heritability estimates for AFC, and CI were negligible, although a small genetic gain was associated with CI. Because the AFC and CI values of the herd are small, past reproductive management has produced favourable results for the heifers. Conclusion: The Wye Angus herd has enough genetic variability for genetic gain through selection on SC.

Asymptotic justification of the intrinsic equations of Koiter's model of a linearly elastic shell

We show that the intrinsic equations of Koiter's model of a linearly elastic shell can be derived from the intrinsic formulation of the three-dimensional equations of a linearly elastic shell, by using an appropriate a priori assumption regarding the three-dimensional strain tensor fields appearing in these equations. To this end, we recast in particular the Dirichlet boundary conditions satisfied by any admissible displacement field as boundary conditions satisfied by the covariant components of the corresponding strain tensor field expressed in the natural curvilinear coordinates of the shell. Then we show that, when restricted to strain tensor fields satisfying a specific a priori assumption, these new boundary conditions reduce to those of the intrinsic equations of Koiter's model of a linearly elastic shell.

Effect of First-Line Antiretroviral Treatment in HIV- Positive Patients on Cd4 Cell Count Response in Boru Meda Hospital, Amhara Regional State, Deisse, Ethiopia, 2013-2018

In limited resource countries, the effectiveness of first-line antiretroviral treatment in HIV-positive patients could be measured by its strong predictor, the CD4 count for the initiation of antiretroviral therapy and proper management of disease progress. Even though, in addition to HIV, there are many factors which can influence the CD4 cell count considered to be constant. Methods : A retrospective cohort study was conducted to examine the response of first-line antiretroviral treatment on CD4 count in HIV-positive patients at 0, 6, 12 and 24 months, who enrolled in the first month of 2013 and followed up to begining-2018.The covariance components model was employed to determine the CD4 count changes over time. Results : A total of 320 ART attendants were used to analyze their data. The majority 294(92.4%) of the respondents were started AZT based ART regimens, but there was no a significance difference among ART regimens for CD4 cell count. The mean baseline of CD4 cell count (166 cells/ mm 3 ) was positive associated (p<0.001) with CD4 count increment at time of follow ups and was increased to 274 cells/mm 3 significantly (p<0.001) at six months of initiation of ART. Working functional status and younger age, also contribute for CD4 count significant change. Conclusion : The change in CD4 count was high at the first 6 month than 12 and 24 month. All ART regimens without significance difference that increased mean CD4 count at the study period. Hence, it can be concluded that ART is effective enough in improving the immune system and slowing the progression of HIV infection to AIDS. Keywords : CD4 count, ART, Boru meda hospital