Strategies for choosing core animals in the algorithm for proven and young and their impact on the accuracy of single-step genomic predictions in cattle.

Nowadays, in some populations, the number of genotyped animals is too large to obtain the inverse of the genomic relationship matrix. The algorithm for proven and young animals (APY) can be used to overcome this problem. In the present work, different strategies for defining core animals in APY were tested using either simulated or real data. In particular, core definitions based on random choice or on the contribution to the genomic relationship matrix (GCONTR) calculated using Principal Component Analysis were tested. Core sizes able to explain 90, 95, 98, and 99% of the total variance of the genomic relationship matrix (G) were used. Analyzed phenotypes were three simulated traits for 3 000 individuals, and milkability records for 136 406 Italian Simmental cows. The number of genotypes was 4 100 for the simulated dataset, and 11 636 for the Simmental data, respectively. The GCONTR values in Simmental dataset were moderately correlated with the analyzed phenotype, and they showed a decreasing trend according to the year of birth of genotyped animals. The accuracy increased as the size of the core increased in both datasets. The inclusion in the core of animals with largest GCONTR values led to the lowest accuracies (0.50 and 0.71 for the simulated and Simmental datasets, respectively; average across traits and core sizes). On the contrary, the selection of animals with the lowest rank according to their contribution to the G provided slightly higher accuracies, especially in the simulated dataset (0.68 for the simulated dataset, and 0.76 for the Simmental data; average across traits and core sizes). In real data, particularly for larger sizes of core animals, the criteria of choice appear less important, confirming the results of earlier studies. Anyway, the inclusion in the core of animals with the lowest values of GCONTR led to increases in accuracy. These are preliminary results based on a small sample size that need to be confirmed on a larger number of genotypes.

Genetic correlations between fertility traits and milk composition and fatty acids in Holstein-Friesian, Brown Swiss, and Simmental cattle using recursive models.

This study aimed to investigate the genetic and putative causal relationships between fertility traits [i.e., days open (DO) and calving rate (CR)] and milk quality, composition, and fatty acid contents (milk composition traits) in Holstein-Friesian, Brown Swiss, and Simmental cattle, using recursive models within a Bayesian framework. Trivariate animal models were run, each including one fertility trait, one milk composition trait, and, in all models, milk yield. The DO and CR data were merged with the test days closest to the insemination date for milk composition traits. After editing, 16,468 to 23,424 records for Holstein-Friesian, 23,424 to 46,660 for Brown Swiss, and 26,105 to 35,574 for Simmental were available for the analyses. Recursive animal models were applied to investigate the possible causal influences of milk composition traits on fertility and the genetic relationships among these traits. The results suggested a potential cause-and-effect relationship between milk composition traits and fertility traits, with the first trait influencing the latter. We also found greater recursive effects of milk composition traits on DO than on CR, the latter with some putative differences among breeds in terms of sensitivity. For instance, the putative causal effects of somatic cell score on CR (on the observed scale, %) varied from -0.96 to -1.39%, depending on the breed. Concerning fatty acids, we found relevant putative effects of C18:0 on CR, with estimates varying from -7.8 to -9.9%. Protein and casein percentages, and short-chain fatty acid showed larger recursive effects on CR, whereas fat, protein, and casein percentages, C16:0, C18:0, and long-chain fatty acid had larger effects on DO. The results obtained suggested that these milk traits could be considered as effective indicators of the effects of animal metabolic and physiological status on fertility.

Genomic information allows for more accurate breeding values for milkability in dual-purpose Italian Simmental cattle.

Milkability is a trait related to the milking efficiency of an animal, and it is a component of the herd profitability. Due to its economic importance, milkability is currently included in the selection index of the Italian Simmental cattle breed with a weight of 7.5%. This lowly heritable trait is measured on a subjective scale from 1 to 3 (1 = slow, 3 = fast), and genetic evaluations are performed by pedigree-based BLUP. Genomic information is now available for some animals in the Italian Simmental population, and its inclusion in the genetic evaluation system could increase accuracy of breeding values and genetic progress for milkability. The aim of this study was to test the feasibility and advantages of having a genomic evaluation for this trait in the Italian Simmental population. Phenotypes were available for 131,308 cows. A total of 9,526 animals had genotypes for 42,152 loci; among the genotyped animals, 2,455 were cows with phenotypes, and the other were their relatives. The youngest cows with both phenotypes and genotypes (n = 900) were identified as selection candidates. Variance components and heritability were estimated using pedigree information, whereas genetic and genomic evaluations were carried out using BLUP and single-step genomic BLUP (ssGBLUP), respectively. In addition, a weighted ssGBLUP was assessed using genomic regions from a genome-wide association study. Evaluation models were validated using theoretical and realized accuracies. The estimated heritability for milkability was 0.12 ± 0.01. The mean theoretical accuracies for selection candidates were 0.43 ± 0.08 (BLUP) and 0.53 ± 0.06 (ssGBLUP). The mean realized accuracies based on linear regression statistics were 0.29 (BLUP) and 0.40 (ssGBLUP). No genomic regions were significantly associated with milkability, thus no improvements in accuracy were observed when using weighted ssGBLUP. Results indicated that genomic information could improve the accuracy of breeding values and increase genetic progress for milkability in Italian Simmental.

Effects of the detailed protein composition of milk on curd yield and composition measured by model micro-cheese curd making of individual milk samples.

The effect of the contents of casein (CN) and whey protein fractions on curd yield (CY) and composition was estimated using 964 individual milk samples. Contents of αS1-CN, αS2-CN, ß-CN, γ-CN, glycosylated κ-CN (Gκ-CN), unglycosylated κ-CN, ß-LG, and α-LA of individual milk samples were measured using reversed-phase HPLC. Curd yield and curd composition were measured by model micro-cheese curd making using 25 mL of milk. Dry matter CY (DMCY) was positively associated with all casein fractions but especially with αS1-CN and ß-CN. Curd moisture decreased at increasing ß-CN content and increased at increasing γ-CN and Gκ-CN content. Due to their associations with moisture, Gκ-CN and ß-CN were the fractions with the greatest effect on raw CY, which decreased by 0.66% per 1-standard deviation (SD) increase in the content of ß-CN and increased by 0.62% per 1-SD increase in the content of Gκ-CN. The effects due to variation in percentages of the casein fractions in total casein were less marked than those exerted by contents. A 1-SD increase in ß-CN percentage in casein (+3.8% in casein) exerted a slightly negative effect on DMCY (ß = -0.05%). Conversely, increasing amounts of αS1-CN percentage were associated with a small increase in DMCY. Hence, results suggest that, at constant casein and whey protein contents in milk, the DMCY depends to a limited extent on the variation in the αS1-CN:ß-CN ratio. κ-Casein percentage did not affect DMCY, indicating that the positive relationship detected between the content of κ-CN and DMCY can be attributed to the increase in total casein resulting from the increased amount of κ-CN and not to variation in κ-CN relative content. However, milk with increased Gκ-CN percentage in κ-CN also shows increased raw CY and produces curds with increased moisture content. Curd yield increased at increasing content and relative proportion of ß-LG in whey protein, but this is attributable to an improved capacity of the curd to retain water. Results obtained in this study support the hypothesis that, besides variation in total casein and whey protein contents, variation in protein composition might affect the cheese-making ability of milk, but this requires further studies.

Influence of early-life nutritional stress on songbird memory formation.

In birds, vocal learning enables the production of sexually selected complex songs, dialects and song copy matching. But stressful conditions during development have been shown to affect song production and complexity, mediated by changes in neural development. However, to date, no studies have tested whether early-life stress affects the neural processes underlying vocal learning, in contrast to song production. Here, we hypothesized that developmental stress alters auditory memory formation and neural processing of song stimuli. We experimentally stressed male nestling zebra finches and, in two separate experiments, tested their neural responses to song playbacks as adults, using either immediate early gene (IEG) expression or electrophysiological response. Once adult, nutritionally stressed males exhibited a reduced response to tutor song playback, as demonstrated by reduced expressions of two IEGs (Arc and ZENK) and reduced neuronal response, in both the caudomedial nidopallium (NCM) and mesopallium (CMM). Furthermore, nutritionally stressed males also showed impaired neuronal memory for novel songs heard in adulthood. These findings demonstrate, for the first time, that developmental conditions affect auditory memories that subserve vocal learning. Although the fitness consequences of such memory impairments remain to be determined, this study highlights the lasting impact early-life experiences can have on cognitive abilities.

Genetic parameters of measures and population-wide infrared predictions of 92 traits describing the fine composition and technological properties of milk in Italian Simmental cattle.

The objectives of this study were to estimate, for the Italian Simmental cattle population, genetic parameters for 92 traits and their infrared predictions (IP) and to investigate the genetic relationship between measured traits (MT) and IP. Data for milk fat fatty acid composition (n = 1,040), detailed protein composition (n = 3,337), lactoferrin (n = 558), pH (n = 3,438), coagulation properties (n = 3,266), curd yield and composition obtained by a micro-cheese making procedure (n = 1,177), and content of Ca, P, Mg, and K (n = 689) were obtained using reference laboratory analysis. Infrared prediction for all the investigated traits was performed using 143,198 spectra records belonging to 17,619 Italian Simmental cows. (Co)variance components for MT and their IP were estimated in a set of bivariate animal model REML analyses and genetic correlations between MT and IP were estimated using all IP obtained at the population level. A significant positive relationship was observed between the coefficient of determination of the infrared prediction models and the phenotypic and genetic variation of the IP. The decrease in the estimated genetic variance of IP compared with MT was on average 64%. For traits exhibiting calibration models with coefficients of determination in cross-validation (R2CV) greater than 0.9, the decrease in the genetic variance ranged from approximately 20 to 50%. Most traits (88 out of 92) exhibited lower heritability estimates for IP than for the corresponding MT. The estimated genetic correlations between IP and MT (ra) were in general very high. A positive relationship (r = 0.57) between R2CV of calibration models and the estimated ra has been detected. For calibration models exhibiting R2CV higher than 0.75, ra were greater than 0.9. The variability in the estimated correlations increased when R2CV decreased, and for calibration models of moderate predictive ability, estimates of ra ranged from 0.2 to 1. Genetic parameter estimates suggested that IP can be used as indicator traits in breeding programs for the enhancement of fine composition and technological properties of milk. The genetic gain achievable selecting for IP is expected to be high for fatty acid composition, minerals, and for technological properties of milk, whereas it will be low for casein and whey protein composition and for the content of lactoferrin.

Comparison between direct and indirect methods for exploiting Fourier transform spectral information in estimation of breeding values for fine composition and technological properties of milk.

The aim of this study was to compare the common method of exploiting infrared spectral data in animal breeding; that is, estimating the breeding values for the traits predicted by infrared spectroscopy, and an alternative approach based on the direct use of spectral information (direct prediction, DP) to predict the estimated breeding values (EBV). Traits were pH, milk coagulation properties, contents of the main casein and whey protein fractions, cheese yield measured by micro-cheese making, lactoferrin, Ca, and fat composition. For the DP method, the number of spectral variables was reduced by principal components analysis to 8 latent traits that explained 99% of the original spectral variation. Restricted maximum likelihood was used to estimate variance components of the latent traits. (Co)variance components of the original spectral traits were obtained by back-transformation and EBV of all derived milk traits were then predicted as traits correlated with the genetic information of the spectra. The rank correlation between the EBV obtained for the infrared-predicted traits and those obtained from the DP method was variable across traits. Rank correlations ranged from 0.07 (for the content of saturated fatty acids expressed as g/100 g of fat) to 0.96 (for dry matter cheese yield, %) and, for most traits, was <0.5. This result can be explained by the nature of the principal components analysis: it does not take into account the covariance between the spectral variables and the reference traits but produces latent traits that maximize the spectral variance explained. Thus, the direct approach is more likely to be effective for traits more related to the main sources of spectral variation (i.e., protein and fat). More research is required to study spectral genetic variation and to determine the best way to choose spectral regions and the type and number of considered latent traits for potential applications.

Genome-wide association analysis in Italian Simmental cows for lactation curve traits using a low-density (7K) SNP panel.

High-throughput cow genotyping has opened new perspectives for genome-wide association studies (GWAS). Directly recorded phenotypes and several records per animal could be used. In this study, a GWAS on lactation curve traits of 337 Italian Simmental cows genotyped with the Illumina (San Diego, CA) low-density BeadChip (7K) was carried out. Scores of the first 2 principal components extracted from test-day records (7 for each lactation) for milk yield, fat and protein percentages, and somatic cell score were used as phenotypes. The first component described the average level of the lactation curve, whereas the second summarized its shape. Data were analyzed with a mixed linear model that included fixed effects of herd, calving month, calving year, parity, SNP genotype, and random effects of animal and permanent environment. All statistically significant markers (Bonferroni corrected) were associated with the average level component (2 for milk yield, 9 for fat percentage, 6 for protein percentages, and 1 for somatic cell score). No markers were found to be associated with the lactation curve shape. Gene discovery was performed using windows of variable size, according to the linkage disequilibrium level of the specific genomic region. Several suggestive candidate genes were identified, some of which already reported to be associated with dairy traits, such as DGAT1. Others were involved in lipid metabolism, in protein synthesis, in the immune response, in cellular processes, and in early development. The large number of genes flagged in the present study suggests interesting perspectives for the use of low-density genotyped females for GWAS, also for novel phenotypes that are not currently considered as breeding goals.

Exposure to a novel stimulus environment alters patterns of lateralization in avian auditory cortex.

Perceptual filters formed early in development provide an initial means of parsing the incoming auditory stream. However, these filters may not remain fixed, and may be updated by subsequent auditory input, such that, even in an adult organism, the auditory system undergoes plastic changes to achieve a more efficient representation of the recent auditory environment. Songbirds are an excellent model system for experimental studies of auditory phenomena due to many parallels between song learning in birds and language acquisition in humans. In the present study, we explored the effects of passive immersion in a novel heterospecific auditory environment on neural responses in caudo-medial neostriatum (NCM), a songbird auditory area similar to the secondary auditory cortex in mammals. In zebra finches, a well-studied species of songbirds, NCM responds selectively to conspecific songs and contains a neuronal memory for tutor and other familiar conspecific songs. Adult male zebra finches were randomly assigned to either a conspecific or heterospecific auditory environment. After 2, 4 or 9 days of exposure, subjects were presented with heterospecific and conspecific songs during awake electrophysiological recording. The neural response strength and rate of adaptation to the testing stimuli were recorded bilaterally. Controls exposed to conspecific environment sounds exhibited the normal pattern of hemispheric lateralization with higher absolute response strength and faster adaptation in the right hemisphere. The pattern of lateralization was fully reversed in birds exposed to heterospecific environment for 4 or 9 days and partially reversed in birds exposed to heterospecific environment for 2 days. Our results show that brief passive exposure to a novel category of sounds was sufficient to induce a gradual reorganization of the left and right secondary auditory cortices. These changes may reflect modification of perceptual filters to form a more efficient representation of auditory space.

Imputation accuracy is robust to cattle reference genome updates.

Genotype imputation is routinely applied in a large number of cattle breeds. Imputation has become a need due to the large number of SNP arrays with variable density (currently, from 2900 to 777,962 SNPs). Although many authors have studied the effect of different statistical methods on imputation accuracy, the impact of a (likely) change in the reference genome assembly on imputation from lower to higher density has not been determined so far. In this work, 1021 Italian Simmental SNP genotypes were remapped on the three most recent reference genome assemblies. Four imputation methods were used to assess the impact of an update in the reference genome. As expected, the four methods behaved differently, with large differences in terms of accuracy. Updating SNP coordinates on the three tested cattle reference genome assemblies determined only a slight variation on imputation results within method.

Use of principal component approach to predict direct genomic breeding values for beef traits in Italian Simmental cattle.

In the current study, principal component (PC) analysis was used to reduce the number of predictors in the estimation of direct genomic breeding values (DGV) for meat traits in a sample of 479 Italian Simmental bulls. Single nucleotide polymorphism marker genotypes were determined with the 54K Illumina beadchip. After edits, 457 bulls and 40,179 SNP were retained. Principal component extraction was performed separately for each chromosome and 2466 new variables able to explain 70% of total variance were obtained. Bulls were divided into reference and validation population. Three scenarios of the ratio reference:validation were tested: 70:30, 80:20, 90:10. Effect of PC scores on polygenic EBV was estimated in the reference population using different models and methods. Traits analyzed were 7 beef traits: daily BW gain, size score, muscularity score, feet and legs score, beef index (economic index), calving ease direct effect, and cow muscularity. Accuracy was calculated as correlation between DGV and polygenic EBV in the validation bulls. Muscularity, feet and legs, and the beef index showed the greatest accuracies; calving ease, the least. In general, accuracies were slightly greater when reference animals were selected at random and the best scenario was 90:10 and no substantial differences in accuracy were found among different methods. Principal component analysis is entirely based on the factorization of the SNP (co)variance matrix and produced a reduced set of variables (6% of the original variables) which may be used for different phenotypic traits. In spite of this huge reduction in the number of independent variables, DGV accuracies resulted similar to those obtained by using the whole set of SNP markers. Accuracies of direct genomic values found in the present work were always greater than those of traditional parental average (PA). Thus, results of the present study may suggest a possible advantage of use of genomic indexes in the preselection of performance test candidates for beef traits. Moreover, the relevant reduction of variable space might allow genomic selection implementation also in small populations.

Prediction of genomic breeding values for dairy traits in Italian Brown and Simmental bulls using a principal component approach.

The large number of markers available compared with phenotypes represents one of the main issues in genomic selection. In this work, principal component analysis was used to reduce the number of predictors for calculating genomic breeding values (GEBV). Bulls of 2 cattle breeds farmed in Italy (634 Brown and 469 Simmental) were genotyped with the 54K Illumina beadchip (Illumina Inc., San Diego, CA). After data editing, 37,254 and 40,179 single nucleotide polymorphisms (SNP) were retained for Brown and Simmental, respectively. Principal component analysis carried out on the SNP genotype matrix extracted 2,257 and 3,596 new variables in the 2 breeds, respectively. Bulls were sorted by birth year to create reference and prediction populations. The effect of principal components on deregressed proofs in reference animals was estimated with a BLUP model. Results were compared with those obtained by using SNP genotypes as predictors with either the BLUP or Bayes_A method. Traits considered were milk, fat, and protein yields, fat and protein percentages, and somatic cell score. The GEBV were obtained for prediction population by blending direct genomic prediction and pedigree indexes. No substantial differences were observed in squared correlations between GEBV and EBV in prediction animals between the 3 methods in the 2 breeds. The principal component analysis method allowed for a reduction of about 90% in the number of independent variables when predicting direct genomic values, with a substantial decrease in calculation time and without loss of accuracy.

Effects of beta-kappa-casein (CSN2-CSN3) haplotypes and beta-lactoglobulin (BLG) genotypes on milk production traits and detailed protein composition of individual milk of Simmental cows.

The aim of this study was to investigate the effects of CSN2-CSN3 (beta-kappa-casein) haplotypes and BLG (beta-lactoglobulin) genotypes on milk production traits, content of protein fractions, and detailed protein composition of individual milk of Simmental cows. Content of the major protein fractions was measured by reversed-phase HPLC in individual milk samples of 2,167 cows. Protein composition was measured as percentage of each casein (CN) fraction to total CN and as percentage of beta-lactoglobulin (beta-LG) to total whey protein. Genotypes at CSN2, CSN3, and BLG were ascertained by reversed-phase HPLC, and CSN2-CSN3 haplotype probabilities were estimated for each cow. Traits were analyzed by using a linear model including the fixed effects of herd-test-day, parity, days in milk, and somatic cell score class, linear regressions on haplotype probabilities, class of BLG genotype, and the random effect of the sire of the cow. Effects of haplotypes and BLG genotypes on yields were weak or trivial. Genotype BB at BLG and haplotypes carrying CSN2 B and CSN3 B were associated with increased CN content and CN number. Haplotypes including CSN3 B were associated with increased kappa-CN content and percentage of kappa-CN to total CN and with decreased percentages of alpha(S1)- and gamma-CN to total CN. Allele CSN2 B had the effect of increasing beta-CN content and decreasing content of alpha(S1)-CN. Haplotypes including allele CSN2 A(1) exhibited decreased beta-, alpha(S2)-, and gamma-CN concentrations and increased alpha(S1)- and kappa-CN contents, whereas CSN2 I had positive effects on beta-CN concentration and trivial effects on content of other protein fractions. Effects of haplotypes on CN composition were similar to those exerted on content of CN fractions. Allele BLG A was associated with increased beta-LG concentration and percentage of beta-LG to total whey protein and with decreased content of other milk proteins, namely beta-CN and alpha(S1)-CN. Estimated additive genetic variance for investigated traits ranged from 14 to 39% of total variance. Increasing the frequency of specific genotypes or haplotypes by selective breeding might be an effective way to change milk protein composition.

Use of multivariate analysis to extract latent variables related to level of production and lactation persistency in dairy cattle.

Multivariate factor analysis and principal component analysis were used to decompose the correlation matrix of test-day milk yields of 48,374 lactations of 21,721 Italian Simmental cows. Two common latent factors related to level of production in early lactation and lactation persistency, and 2 principal components associated with the whole lactation yield and persistency were obtained. Factor and principal component scores were treated as new quantitative phenotypes related to prominent features of lactation curve shape. Genetic parameters were estimated by univariate and bivariate animal models. Estimates of heritability were moderately low for both latent factors (0.13 for persistency and yield early in lactation). Heritabilities of the principal component related to total lactation yield and 305-d yield were similar (0.19 and 0.20, respectively). Finally, heritability was quite low for the principal component related to lactation persistency (0.07). Repeatabilities between lactations were about 0.27 for both latent factors, around 0.4 for the first principal component and 305-d yield, and 0.11 for the second principal component. Moderate genetic correlation among common factors (0.26) and their high genetic correlation with total lactation yield (>0.60) suggest that selection can be used to change the shape of lactation curve as well as improve yield. Scores of the second principal component can be used to genetically improve persistency while maintaining constant total lactation yield.

Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models.

The study of relationships between mathematical properties of functions used to model lactation curves is usually limited to the evaluation of the goodness of fit. Problems related to the existence of different lactation curve shapes are usually neglected or solved drastically by considering shapes markedly different from the standard as biologically atypical. A deeper investigation could yield useful indications for developing technical tools aimed at modifying the lactation curve in a desirable fashion. Relationships between mathematical properties and lactation curve shapes were analyzed by fitting several common functions (Wood incomplete gamma, Wilmink's exponential, Ali and Schaeffer's polynomial regression, and fifth-order Legendre polynomials) to 229,518 test-day records belonging to 27,837 lactations of Italian Simmental cows. Among the best fits (adjusted r(2) higher than 0.75), the 3-parameter models (Wood and Wilmink) were able to detect 2 main groups of curve shape: standard and atypical. Five-parameter models (Ali and Schaeffer function and the Legendre polynomials) were able to recognize a larger number of curve shapes. The higher flexibility of 5-parameter models was accompanied by increased sensitivity to local random variation as evidenced by the bias in estimated test-day yields at the beginning and end of lactation (border effect). Meaning of parameters, range of their values and of their (co) variances are clearly different among groups of curves. Our results suggest that analysis based on comparisons between parameter values and (co)variances should be done carefully. Comparisons among parameter values and (co)variances could yield more robust, reliable, and easy to interpret results if performed within groups based on curve shape.

A multivariate approach to modeling shapes of individual lactation curves in cattle.

Milk test-day records of 5728 lactations of Italian Simmental cows were analyzed with multivariate factor analysis in order to extract 2 common factors, whose scores were used as quantitative measures of 2 main features of lactation curve shape-i.e., the increasing rate of yield in the first part of lactation and the rate of decline of milk yield after the lactation peak. The 2 indices, objectively derived from the correlation matrix of original test-day records, showed a high discriminant power in separating lactation curves with different shapes. The weak correlation between the 2 factors (0.11), together with the high correlation of factors and the total 305-d yield (about 0.70), suggests that an increase in lactation yield could be achieved by acting only on one of the 2 factors related to lactation-curve shape, with the other kept constant at a medium or low value. The suitability of the 2 factors as descriptors of lactation patterns has been confirmed by the relationships found between factor scores and the main environmental effects known to affect the shape of the lactation curve, such as parity and season of calving.

The relationship between perception and production in songbird vocal imitation: what learned calls can teach us.

Songbirds produce calls as well as song. This paper summarizes four studies of the zebra finch long call, used by both sexes in similar behavioral contexts. Female long calls are acoustically simpler than male long calls, which include acoustic features learned during development. Production of these male-typical features requires an intact nucleus robustus archistriatalis, the sexually-dimorphic source of the telencephalic projection to brainstem vocal effectors. In experiments that quantified the long calls produced in response to long call playbacks, intact adult zebra finch males, but not females, show a categorical preference for the long calls of females over those of males. Experiments with synthetic stimuli showed that males classify long call stimuli that they hear by gender, using both spectral and temporal information, but that females use only temporal information. Juvenile males (<45 days) did not show the categorical preference, but it emerged during the same period when the robustus archistriatalis matures anatomically and the first male-typical vocalizations are produced. Adult males with robustus archistriatalis lesions lost the categorical preference for female long calls, suggesting that the robustus archistriatalis plays a role in long call discrimination. These results demonstrate that calls complement song as a potent tool for studying the neurobiology of vocal communication.

Test day and lactation yield predictions in Italian simmental cows by ARMA methods.

Autoregressive Moving Average (ARMA) models, originally developed in the contest of time series analysis, were used to predict Test Day (TD) yields of milk production traits in dairy cows. ARMA models areable to take into account both the average lactation curve of homogeneous groups of animals and the residual individual variability that may be explained in terms of probability models, such as Autoregressive (AR) and Moving Average (MA) processes. Milk, fat, and protein yields of 6000 Italian Simmental cows with 8 TD records per lactation were analyzed. Data were grouped according to parity (1st, 2nd, and 3rd calving) and fitted to a Box-Jenkins ARMA model in order to predict TD yields in five situations of incomplete lactations. Reasonable accuracies have been obtained for a limited horizon of prediction: average correlations among actual and predicted data were 0.85, 0.72, and 0.80 for milk, fat and protein yields when the first predicted TD was one step ahead (on average 42d) of the last actual record available. Cumulative 305-d yields were calculated using all actual (actual yields) or actual plus forecasted (estimated yields) daily yields. Accuracy of lactation predictions was remarkable even when only a few actual TD records were available, with values of 0.88 for milk and protein and 0.84 for fat for the correlations between actual and estimated yields when 6 out of 8 TD records were predicted. Accuracy rapidly increases with the number of actual TD available: correlations were about 0.96 for milk and protein and 0.93 for fat when 4 out of 8 TD records were predicted. In comparison with other prediction methods, ARMA modelsare very simple and can be easily implemented in data recording software, even at the farm level.

Behavioral discrimination of sexually dimorphic calls by male zebra finches requires an intact vocal motor pathway.

Vocal communication between zebra finches includes the exchange of long calls (LCs) as well as song. By using this natural call behavior and quantifying the LCs emitted in response to playbacks of LCs of other birds, we have previously shown that adult male zebra finches have a categorical preference for the LCs of females over those of males. Female LCs are acoustically simpler than male LCs, which include complex acoustic features that are learned during development. Production of these male-typical features requires an intact nucleus RA, the sexually dimorphic source of the main telencephalic projection to brainstem vocal effectors. We have now made bilateral lesions of RA in 17 adult males and tested their discrimination behavior in the call response situation. Lesioned birds continue to call, but lose the male-typical preference for female LCs. The degree of loss is correlated with the extent of RA damage. Further, the simplified LCs of males with RA lesions have a variable duration that is correlated with stimulus features. In effect, the call response behavior of lesioned males becomes like that of females. Apparently, in the absence of RA, the remaining intact structures receive different call information than RA normally does, and/or process it differently. This suggests that the vocal motor nucleus RA could play a role in the transformation of a signal encoding the salience of stimulus parameters into a control signal that modulates the probability and strength of responding.

Possible roles for GABAergic inhibition in the vocal control system of the zebra finch.

The final common output from the telencephalic vocal control system in songbirds is the projection from nucleus RA, which drives respiratory and syringeal muscles via medullary nuclei. We examined the possible role of GABAergic inhibition in RA of adult male zebra finches by micro-injecting bicuculline, an antagonist of inhibitory GABA(A) receptors, while recording simultaneously with multiple microelectrodes. Following bicuculline injection, the normally high spontaneous activity of RA neurons exhibited a pattern of rhythmic bursting lasting up to 30 min. The bursts were often accompanied by involuntary vocalizations: monosyllabic notes resembling calls. Other experiments used microinjections that were below threshold for involuntary vocalization. When the bird sang to a female during a period after the injection, song structure was degraded: song duration was lengthened, noisiness increased, and novel syllables appeared. The results suggest that GABA normally contributes to regulating excitability in RA. When this regulation is blocked, activity increases sufficiently to engage the respiratory and vocal musculature. Synaptic inputs that affect GABAergic interneurons in RA could thus play a role in initiation and control of vocalization. The abnormal vocalizations produced in the presence of bicuculline suggest that GABAergic inhibition may normally help to shape the pattern of learned vocalizations, as well as to regulate overall RA activity.