Correlations Among First and Second Lactation Milk Yield and Calving Interval

Estimates of genetic correlations were .17 between first lactation milk yield and concurrent calving interval, .10 between second lactation milk yield and first calving interval, and .82 between first and second milk yields. Corresponding phenotypic correlations were .27, .16, and .58. Heritability estimates were .27 and .25 for first and second lactations and .15 for calving interval. Estimates were averages of two samples of 15 New York State herds averaging 144 Al-sired Holstein cows and 30 sires. Milk yields were 305-d, mature equivalent. Calving interval was days between first and second freshening. First milk records without a second freshening were included. Multiple- trait animal model included separate herd-year-season effects for first and second milk yields and calving interval. Numerator relationships among animals within herd, except for daughter-dam relationships, were included. The REML with the expectation-maximization algorithm was used to estimate (co)variance matrices among genetic values and environmental effects for the three traits. Results indicate a need to adjust milk records for the phenotypic effects of current and previous calving interval. The genetic association, however, between fertility and milk yield appears small. Genetic improvement of 450 kg of milk yield may result in 2 added d to first calving interval

Genetic parameters among weight, prolificacy, and wool traits of Columbia, Polypay, Rambouillet, and Targhee sheep

Genetic parameters for Columbia, Polypay, Rambouillet, and Targhee sheep were estimated using REML with animal models for prolificacy, weight, and wool traits. All bivariate analyses included a covariance between additive genetic effects for the two traits plus appropriate additional covariances. Number of observations by breed ranged from 5,140 to 7,095 for prolificacy traits, from 7,750 to 9,530 for weight traits, and from 4,603 to 34,746 for wool traits. Heritability estimates ranged from .03 to .11 for prolificacy traits (litter size at birth and litter size at weaning), from .09 to .26 for weight traits (birth weight and average daily gain), and from .25 to .53 for wool traits (fleece weight, fleece grade and staple length). Estimates of direct genetic correlations among prolificacy and among weight traits were positive and ranged from .58 to 1.00 and .18 to 1.00, respectively. Estimates of direct genetic correlation between fleece weight and staple length were positive (.50 to .70) but were negative between fleece weight and fleece grade (−.60 to −.34) and between staple length and fleece grade (−.72 and −.40). Prolificacy and wool traits were essentially uncorrelated. Weight and prolificacy traits were slightly positively correlated. Weight traits had a moderate positive direct genetic correlation with fleece weight and staple length, but were uncorrelated with fleece grade. These estimates of genetic parameters between prolificacy, weight, and wool traits can be used to construct multiple- trait selection indexes for dual-purpose sheep

Magnetic Coupling Between Non-Magnetic Ions: Eu3+ in EuN and EuP

We consider the electronic structure of, and magnetic exchange (spin) interactions between, nominally nonmagnetic Eu^3+ ions (4f^6, S=3, L=3, J=0) within the context of the rocksalt structure compounds EuN and EuP. Both compounds are ionic [Eu^3+; N^3- and P^3-] semimetals similar to isovalent GdN. Treating the spin polarization within the 4f shell, and then averaging consistent with the J=0 configuration, we estimate semimetallic band overlaps (Eu 5d with pnictide 2p or 3p) of ~0.1 eV (EuN) and ~1.0 eV (EuP) that increase (become more metallic) with pressure. The calculated bulk modulus is 130 (86) GPa for EuN (EuP). Exchange (spin-spin) coupling calculated from correlated band theory is small and ferromagnetic in sign for EuN, increasing in magnitude with pressure. Conversely, the exchange coupling is antiferromagnetic in sign for EuP and is larger in magnitude, but decreases with compression. Study of a two-site model with S_1*S_2 coupling within the J=0,1 spaces of each ion illustrates the dependence of the magnetic correlation functions on the model parameters, and indicates that the spin coupling is sufficient to alter the Van Vleck susceptibility. We outline a scenario of a spin-correlation transition in a lattice of S=3, L=3, J=0 nonmagnetic ions

Quantum Electrical Dipole in Triangular Systems: a Model for Spontaneous Polarity in Metal Clusters

Triangular symmetric molecules with mirror symmetry perpendicular to the 3-fold axis are forbidden to have a fixed electrical dipole moment. However, if the ground state is orbitally degenerate and lacks inversion symmetry, then a ``quantum'' dipole moment does exist. The system of 3 electrons in D_3h symmetry is our example. This system is realized in triatomic molecules like Na_3. Unlike the fixed dipole of a molecule like water, the quantum moment does not point in a fixed direction, but lies in the plane of the molecule and takes quantized values +/- mu_0 along any direction of measurement in the plane. An electric field F in the plane leads to a linear Stark splitting +/- mu_0 F}. We introduce a toy model to study the effect of Jahn-Teller distortions on the quantum dipole moment. We find that the quantum dipole property survives when the dynamic Jahn-Teller effect is included, if the distortion of the molecule is small. Linear Stark splittings are suppressed in low fields by molecular rotation, just as the linear Stark shift of water is suppressed, but will be revealed in moderately large applied fields and low temperatures. Coulomb correlations also give a partial suppression.Comment: 10 pages with 7 figures included; thoroughly revised with a new coauthor; final minor change

Hamilton-Jacobi Formulation of KS Entropy for Classical and Quantum Dynamics

A Hamilton-Jacobi formulation of the Lyapunov spectrum and KS entropy is developed. It is numerically efficient and reveals a close relation between the KS invariant and the classical action. This formulation is extended to the quantum domain using the Madelung-Bohm orbits associated with the Schroedinger equation. The resulting quantum KS invariant for a given orbit equals the mean decay rate of the probability density along the orbit, while its ensemble average measures the mean growth rate of configuration-space information for the quantum system.Comment: preprint, 8 pages (revtex

Spin-State Transition and Metal-Insulator Transition in La1−x_{1-x}Eux_xCoO3_3}

We present a study of the structure, the electric resistivity, the magnetic susceptibility, and the thermal expansion of La$_{1-x}$Eu$_x$CoO$_3$. LaCoO$_3$ shows a temperature-induced spin-state transition around 100 K and a metal-insulator transition around 500 K. Partial substitution of La$^{3+}$ by the smaller Eu$^{3+}$ causes chemical pressure and leads to a drastic increase of the spin gap from about 190 K in LaCoO$_3$ to about 2000 K in EuCoO$_3$, so that the spin-state transition is shifted to much higher temperatures. A combined analysis of thermal expansion and susceptibility gives evidence that the spin-state transition has to be attributed to a population of an intermediate-spin state with orbital order for $x<0.5$ and without orbital order for larger $x$. In contrast to the spin-state transition, the metal-insulator transition is shifted only moderately to higher temperatures with increasing Eu content, showing that the metal-insulator transition occurs independently from the spin-state distribution of the Co$^{3+}$ ions. Around the metal-insulator transition the magnetic susceptibility shows a similar increase for all $x$ and approaches a doping-independent value around 1000 K indicating that well above the metal-insulator transition the same spin state is approached for all $x$.Comment: 10 pages, 6 figure

New alleles in calpastatin gene are associated with meat quality traits in pigs

Suggestive QTL affecting raw firmness scores and average Instron force, tenderness, juiciness, and chewiness on cooked meat were mapped to pig chromosome 2 using a three-generation intercross between Berkshire and Yorkshire pigs. Based on its function and location, the calpastatin (CAST) gene was considered to be a good candidate for the observed effects. Several missense and silent mutations were identified in CAST and haplotypes covering most of the coding region were constructed and used for association analyses with meat quality traits. Results demonstrated that one CAST haplotype was significantly associated with lower Instron force and cooking loss and higher juiciness and, therefore, this haplotype is associated with higher eating quality. Some of the sequence variation identified may be associated with differences in phosphorylation of CAST by adenosine cyclic 3′, 5′-monophosphate- dependent protein kinase and may in turn explain the meat quality phenotypic differences. The beneficial haplotype was present in all the commercial breeds tested and may provide significant improvements for the pig industry and consumers because it can be used in marker-assisted selection to produce naturally tender and juicy pork without additional processing steps

Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP

[EN] Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A. Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A-inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A-inverse and in the computation of reliability from PEVs.FEDER; INRA; Universidad Nacional de Lomas de Zamora; European Unions' Horizon 2020 Research & Innovation Programme, Grant/Award Number: No772787Aguilar, I.; Fernandez, EN.; Blasco Mateu, A.; Ravagnolo, O.; Legarra, A. (2020). Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP. Journal of Animal Breeding and Genetics. 137(4):356-364. https://doi.org/10.1111/jbg.12470S3563641374Bijma, P. (2012). Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. Journal of Animal Breeding and Genetics, 129(5), 345-358. doi:10.1111/j.1439-0388.2012.00991.xChristensen, O. F., Madsen, P., Nielsen, B., Ostersen, T., & Su, G. (2012). Single-step methods for genomic evaluation in pigs. Animal, 6(10), 1565-1571. doi:10.1017/s1751731112000742Colleau, J.-J., Palhière, I., Rodríguez-Ramilo, S. T., & Legarra, A. (2017). A fast indirect method to compute functions of genomic relationships concerning genotyped and ungenotyped individuals, for diversity management. Genetics Selection Evolution, 49(1). doi:10.1186/s12711-017-0363-9Edel, C., Pimentel, E. C. G., Erbe, M., Emmerling, R., & Götz, K.-U. (2019). Short communication: Calculating analytical reliabilities for single-step predictions. Journal of Dairy Science, 102(4), 3259-3265. doi:10.3168/jds.2018-15707Fernández, E. N., Sánchez, J. P., Martínez, R., Legarra, A., & Baselga, M. (2017). Role of inbreeding depression, non-inbred dominance deviations and random year-season effect in genetic trends for prolificacy in closed rabbit lines. Journal of Animal Breeding and Genetics, 134(6), 441-452. doi:10.1111/jbg.12284Golden, B. L., Brinks, J. S., & Bourdon, R. M. (1991). A performance programmed method for computing inbreeding coefficients from large data sets for use in mixed-model analyses. Journal of Animal Science, 69(9), 3564-3573. doi:10.2527/1991.6993564xGroeneveld E. Kovac M. &Wang T.(1990).PEST a general purpose BLUP package for multivariate prediction and estimation. Proceedings of the 4th World Congress on Genetics Applied to Livestock Production Edinburgh 13 488–491.Henderson, C. R. (1975). Best Linear Unbiased Estimation and Prediction under a Selection Model. Biometrics, 31(2), 423. doi:10.2307/2529430Henderson, C. R. (1976). A Simple Method for Computing the Inverse of a Numerator Relationship Matrix Used in Prediction of Breeding Values. Biometrics, 32(1), 69. doi:10.2307/2529339Legarra, A., Aguilar, I., & Colleau, J. J. (2020). Short communication: Methods to compute genomic inbreeding for ungenotyped individuals. Journal of Dairy Science, 103(4), 3363-3367. doi:10.3168/jds.2019-17750Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92(9), 4656-4663. doi:10.3168/jds.2009-2061Legarra A. Lourenco D. A. L. &Vitezica Z. G.(2018).Bases for genomic prediction. Retrieved fromhttp://genoweb.toulouse.inra.fr/~alegarra/Masuda, Y., Aguilar, I., Tsuruta, S., & Misztal, I. (2015). Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements1,2. Journal of Animal Science, 93(10), 4670-4674. doi:10.2527/jas.2015-9395Matilainen, K., Strandén, I., Aamand, G. P., & Mäntysaari, E. A. (2018). Single step genomic evaluation for female fertility in Nordic Red dairy cattle. Journal of Animal Breeding and Genetics, 135(5), 337-348. doi:10.1111/jbg.12353Mehrabani-Yeganeh, H., Gibson, J. P., & Schaeffer, L. R. (2000). Including coefficients of inbreeding in BLUP evaluation and its effect on response to selection. Journal of Animal Breeding and Genetics, 117(3), 145-151. doi:10.1046/j.1439-0388.2000.00241.xMeyer, K. (2007). WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University SCIENCE B, 8(11), 815-821. doi:10.1631/jzus.2007.b0815Misztal, I., & Wiggans, G. R. (1988). Approximation of Prediction Error Variance in Large-Scale Animal Models. Journal of Dairy Science, 71, 27-32. doi:10.1016/s0022-0302(88)79976-2Mrode, R. A., & Thompson, R. (Eds.). (2005). Linear models for the prediction of animal breeding values. doi:10.1079/9780851990002.0000Pryce, J. E., Gonzalez-Recio, O., Nieuwhof, G., Wales, W. J., Coffey, M. P., Hayes, B. J., & Goddard, M. E. (2015). Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows. Journal of Dairy Science, 98(10), 7340-7350. doi:10.3168/jds.2015-9621Sargolzaei, M., Chesnais, J. P., & Schenkel, F. S. (2014). A new approach for efficient genotype imputation using information from relatives. BMC Genomics, 15(1), 478. doi:10.1186/1471-2164-15-478Strandén, I., Matilainen, K., Aamand, G. P., & Mäntysaari, E. A. (2017). Solving efficiently large single-step genomic best linear unbiased prediction models. Journal of Animal Breeding and Genetics, 134(3), 264-274. doi:10.1111/jbg.12257Ten Napel J. Vandenplas J. Lidauer M. Stranden I. Taskinen M. Mäntysaari E. Veerkamp R. F.(2017).MiXBLUP user‐friendly software for large genetic evaluation systems–Manual V2. Retrived from:https://www.mixblup.eu/documents/Manual%20MiXBLUP%202.1_June%202017_V2.pdfTier B. Schneeberger M. Hammond K. &Fuchs W. C.(1991).Determining the accuracy of estimated breeding values in multiple trait animal models. Proceedings of the 9th AAABG Conference 239–242Van Vleck, L. D. (1993). Variance of prediction error with mixed model equations when relationships are ignored. Theoretical and Applied Genetics, 85(5), 545-549. doi:10.1007/bf00220912VanRaden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91(11), 4414-4423. doi:10.3168/jds.2007-0980Xiang, T., Christensen, O. F., & Legarra, A. (2017). Technical note: Genomic evaluation for crossbred performance in a single-step approach with metafounders1. Journal of Animal Science, 95(4), 1472-1480. doi:10.2527/jas.2016.115

On Which Length Scales Can Temperature Exist in Quantum Systems?

We consider a regular chain of elementary quantum systems with nearest neighbor interactions and assume that the total system is in a canonical state with temperature $T$. We analyze under what condition the state factors into a product of canonical density matrices with respect to groups of $n$ subsystems each, and when these groups have the same temperature $T$. While in classical mechanics the validity of this procedure only depends on the size of the groups $n$, in quantum mechanics the minimum group size $n_{\text{min}}$ also depends on the temperature $T$! As examples, we apply our analysis to different types of Heisenberg spin chains.Comment: To appear in: Proceedings of the SPQS conference, J. Phys. Soc. Jpn. 74 (2005) Supp