Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

GWAS and genetic and phenotypic correlations of plasma metabolites with complete blood count traits in healthy young pigs reveal implications for pig immune response

Introduction: In this study estimated genetic and phenotypic correlations between fifteen complete blood count (CBC) traits and thirty-three heritable plasma metabolites in young healthy nursery pigs. In addition, it provided an opportunity to identify candidate genes associated with variation in metabolite concentration and their potential association with immune response, disease resilience, and production traits.

Methods: The blood samples were collected from healthy young pigs and Nuclear Magnetic Resonance (NMR) was used to quantify plasma metabolites. CBC was determined using the ADVIA® 2120i Hematology System. Genetic correlations of metabolite with CBC traits and single step genome-wide association study (ssGWAS) were estimated using the BLUPF90 programs.

Results: Results showed low phenotypic correlation estimates between plasma metabolites and CBC traits. The highest phenotypic correlation was observed between lactic acid and plasma basophil concentration (0.36 ± 0.04; p < 0.05). Several significant genetic correlations were found between metabolites and CBC traits. The plasma concentration of proline was genetically positively correlated with hemoglobin concentration (0.94 ± 0.03; p < 0.05) and L-tyrosine was negatively correlated with mean corpuscular hemoglobin (MCH; −0.92 ± 0.74; p < 0.05). The genomic regions identified in this study only explained a small percentage of the genetic variance of metabolites levels that were genetically correlated with CBC, resilience, and production traits.

Discussion: The results of this systems approach suggest that several plasma metabolite phenotypes are phenotypically and genetically correlated with CBC traits, suggesting that they may be potential genetic indicators of immune response following disease challenge. Genomic analysis revealed genes and pathways that might interact to modulate CBC, resilience, and production traits.

Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system

Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R²_val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R²_val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R²_val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R²_val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.

Methane and carbon dioxide emissions and grazed forage intake from pregnant beef heifers previously classified for residual feed intake under drylot conditions

The objectives of this study were to quantify the effect of post-weaning residual feed intake (RFI) on subsequent grazed forage intake, methane (CH4), and carbon dioxide (CO2) emissions. Beef heifers classified for RFI adjusted for off-test backfat (RFIfat; 55 high and 56 low) at 9 mo of age were monitored 7 mo later for CH4 and CO2 emissions using the GreenFeed Emissions Monitoring system. About 56 of these heifers were also monitored as high and low RFIfat groups using open-path Fourier-transform infrared spectroscopy (OP-FTIR). Heifers were dosed with 1 kg of C32-labelled pellets once daily for 15 d, with twice daily fecal sampling the last 8 d to determine individual grazed forage intake using the n-alkane method. Low RFIfat pregnant heifers consumed less forage (10.25 vs. 10.81 kg dry matter d−1; P < 0.001), and emitted less daily CH4 (238.7 vs. 250.7 g d−1; P = 0.009) and CO2 (7578 vs. 8041 g d−1; P < 0.001) compared with high RFIfat animals. Results from the OP-FTIR further confirmed that low RFIfat heifers emitted 6.3% less (g d−1; P = 0.006) CH4 compared with their high RFIfat cohorts. Thus, selection for low RFIfat will decrease daily CH4 and CO2 emissions from beef cattle.

Methane and carbon dioxide emissions from yearling beef heifers and mature cows classified for residual feed intake under drylot conditions

This study quantified methane (CH4) and carbon dioxide (CO2) production from beef heifers and cows classified for residual feed intake adjusted for off-test backfat thickness (RFIfat) and reared in drylot during cold winter temperatures. Individual performance, daily feed intake, and RFIfat were obtained for 1068 crossbred and purebred yearling heifers (eight trials) as well as 176 crossbred mature cows (six trials) during the winters of 2015–2017 at two locations. A portion of these heifers (147 high RFIfat; 167 low RFIfat) and cows (69 high RFIfat; 70 low RFIfat) was monitored for enteric CH4 and CO2 emissions using the GreenFeed Emissions Monitoring (GEM) system (C-Lock Inc., Rapid City, SD, USA). Low RFIfat cattle consumed less feed [heifers, 7.80 vs. 8.48 kg dry matter (DM) d−1; cows, 11.64 vs. 13.16 kg DM d−1] and emitted less daily CH4 (2.5% for heifers; 3.7% for cows) and CO2 (1.4% for heifers; 3.4% for cows) compared with high RFIfat cattle. However, low RFIfat heifers and cows had higher CH4 (6.2% for heifers; 9.9% for cows) and CO2 yield (7.3% for heifers; 9.8% for cows) per kilogram DM intake compared with their high RFIfat pen mates. The GEM system performed at air temperatures between +20 and −30 °C. Feed intake of heifers and mature cows was differently affected by ambient temperature reduction between +20 and −15 °C and similarly increased their feed intake at temperatures below −15 °C. In conclusion, low RFIfat animals emit less daily enteric CH4 and CO2, due mainly to lower feed consumption at equal body weight, gain, and fatness.

Performance and optimization of an ear tag automated activity monitor for estrus prediction in dairy heifers

The objectives of this study were to evaluate the performance of the SCR eSense ear tag automated activity monitor (AAM) to detect estrus behavior in Holstein heifers and to determine the optimal time from estrus alert to artificial insemination (AI) using sex-sorted or conventional semen. In total, 281 heifers were fitted with the AAM once eligible for breeding (>13.5 m of age). For the first AI, estrus was synchronized using 500 μg of cloprostenol (PGF), given 14 d apart, and heifers were given estrus detection patches (Estrotect™) after the second PGF. Heifers were inseminated at randomly attributed times after high activity alert from the AAM system or if the estrus patch had ≥ 50% colour change. Most heifers received sex-sorted semen for the first AI and conventional semen for subsequent inseminations. Pregnancy diagnosis was performed at 30 d post AI and heifers had four opportunities to become pregnant. In a subset of heifers (n = 149), ovaries were scanned every 12 h from the time of AI until ovulation (OV). The system recorded a heat index (measure of estrus strength), maximum activity change, maximum rumination change and duration of high activity. The sensitivity was 91.0%, with a false positive and false negative rate of 8.0%, and the positive predictive value to detect true estrus events was 83.5%. Pregnancy per AI to first AI was 67.6% and 97.9% of heifers become pregnant after four inseminations. Most false positive estrus events had a heat index < 45 and a rumination change < -20, while false negative events had a rumination change ≥ -20. Odds of pregnancy was not associated with any estrus characteristics measured by the system. However, pre-ovulatory follicle diameter had a weak correlation (r < 0.25) with all estrus characteristics. The average (range) interval of onset of high activity, peak activity and end of high activity to OV was 28 h (16-46 h), 22 h (10-40 h) and 16 h (0-36 h), respectively. For conventional semen, each hour increase in interval from activity onset or peak activity to AI reduced the predicted probability of pregnancy by 3.8 and 4.2%, respectively. For sex-sorted semen, the relationship between activity onset or peak activity to AI and predicted probability of pregnancy was quadratic, but not significant. Overall, the SCR eSense ear tag AAM performed well and strategies to identify false positive and false negative estrus events, along with optimization of timing of AI, should further improve performance in Holstein heifers.

Invited review: Advances and challenges in application of feedomics to improve dairy cow production and health

Dairy cattle science has evolved greatly over the past century, contributing significantly to the improvement in milk production achieved today. However, a new approach is needed to meet the increasing demand for milk production and address the increased concerns about animal health and welfare. It is now easy to collect and access large and complex data sets consisting of molecular, physiological, and metabolic data as well as animal-level data (such as behavior). This provides new opportunities to better understand the mechanisms regulating cow performance. The recently proposed concept of feedomics could help achieve this goal by increasing our understanding of interactions between the different components or levels and their impact on animal production. Feedomics is an emerging field that integrates a range of omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and metatranscriptomics) to provide these insights. In this way, we can identify the best strategies to improve overall animal productivity, product quality, welfare, and health. This approach can help research communities elucidate the complex interactions among nutrition, environment, management, animal genetics, metabolism, physiology, and the symbiotic microbiota. In this review, we summarize the outcomes of the most recent research on omics in dairy cows and highlight how an integrated feedomics approach could be applied in the future to improve dairy cow production and health. Specifically, we focus on 2 topics: (1) improving milk yield and milk quality, and (2) understanding metabolic physiology in transition dairy cows, which are 2 important challenges faced by the dairy industry worldwide.

Genomic retained heterosis effects on fertility and lifetime productivity in beef heifers

This study evaluated the effects of three genomic indicators of heterosis on female fertility and lifetime productivity, and quantified changes over 11 production cycles in a crossbred cow herd. Pedigree-based breed composition (pBC) was determined and used to calculate retained heterozygosity for 412 replacement heifers born from 2004 to 2014 at the Lacombe Research and Development Centre (AB, Canada). Heifers were followed as cows over 1050 mating opportunities, 11 production cycles, and five parities. Heifers and their sires (51) were genotyped and these genotypes were used to predict each animal’s genomic breed composition (gBC) and three genomic indicators of heterosis: (1) retained heterozygosity (RHETg), (2) heterozygous proportion (H), and (3) retained heterosis (RHg). Correlations between pedigree and genomic breed fractions for Angus, Hereford, and Charolais were high (rp = 0.74–0.94; P < 0.001). Genomic indicators of heterosis were highly related (rp = 0.61 for RHETg vs. H; 0.71 for RHg vs. H; 0.96 for RHETg vs. RHg; P < 0.001). Each 10% change in RHETg resulted in 51 ± 20 d longer survival (P = 0.011) in the herd and 35.7 ± 15.2 kg more (P = 0.019) calf wean weight per cow exposed to breeding when summed over five parities. These differences resulted in an extra $161 per heifer in a year. Optimizing heterosis using genomic tools can be very beneficial for the cow herd if applied correctly.

Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo

Water buffalo is the second largest resource of milk supply around the world, and it is well known for its distinctive milk quality in terms of fat, protein, lactose, vitamin, and mineral contents. Understanding the genetic architecture of milk production traits is important for future improvement by the buffalo breeding industry. The advance of genome-wide association studies (GWAS) provides an opportunity to identify potential genetic variants affecting important economical traits. In the present study, GWAS was performed for 489 buffaloes with 1,424 lactation records using the 90K Affymetrix Buffalo SNP Array (Affymetrix/Thermo Fisher Scientific, Santa Clara, CA). Collectively, 4 candidate single nucleotide polymorphisms (SNP) in 2 genomic regions were found to associate with buffalo milk production traits. One region affecting milk fat and protein percentage was located on the equivalent of Bos taurus autosome (BTA)3, spanning 43.3 to 43.8 Mb, which harbored the most likely candidate genes MFSD14A, SLC35A3, and PALMD. The other region on the equivalent of BTA14 at 66.5 to 67.0 Mb contained candidate genes RGS22 and VPS13B and influenced buffalo total milk yield, fat yield, and protein yield. Interestingly, both of the regions were reported to have quantitative trait loci affecting milk performance in dairy cattle. Furthermore, we suggest that buffaloes with the C allele at AX-85148558 and AX-85073877 loci and the G allele at AX-85106096 locus can be selected to improve milk fat yield in this buffalo-breeding program. Meanwhile, the G allele at AX-85063131 locus can be used as the favorable allele for improving milk protein percentage. Genomic prediction showed that the reliability of genomic estimated breeding values (GEBV) of 6 milk production traits ranged from 0.06 to 0.22, and the correlation between estimated breeding values and GEBV ranged from 0.23 to 0.35. These findings provide useful information to understand the genetic basis of buffalo milk properties and may play a role in accelerating buffalo breeding programs using genomic approaches.

Comparative transcriptomic analysis of porcine peripheral blood reveals differentially expressed genes from the cytokine-cytokine receptor interaction pathway related to health status

While some research has looked into the host genetic response in pigs challenged with specific viruses or bacteria, few studies have explored the expression changes of transcripts in the peripheral blood of sick pigs that may be infected with multiple pathogens on farms. In this study, the architecture of the peripheral blood transcriptome of 64 Duroc sired commercial pigs, including 18 healthy animals at entry to a growing facility (set as a control) and 23 pairs of samples from healthy and sick pen mates, was generated using RNA-Seq technology. In total, 246 differentially expressed genes were identified to be specific to the sick animals. Functional enrichment analysis for those genes revealed that the over-represented gene ontology terms for the biological processes category were exclusively immune activity related. The cytokine-cytokine receptor interaction pathway was significantly enriched. Nine functional genes from this pathway encoding members (as well as their receptors) of the interleukins, chemokines, tumor necrosis factors, colony stimulating factors, activins, and interferons exhibited significant transcriptional alteration in sick animals. Our results suggest a subset of novel marker genes that may be useful candidate genes in the evaluation and prediction of health status in pigs under commercial production conditions.

Genomewide association of piglet responses to infection with one of two porcine reproductive and respiratory syndrome virus isolates

Porcine reproductive and respiratory syndrome (PRRS) is a devastating disease in the swine industry. Identification of host genetic factors that enable selection for improved performance during PRRS virus (PRRSV) infection would reduce the impact of this disease on animal welfare and production efficiency. We conducted genomewide association study (GWAS) analyses of data from 13 trials of approximately 200 commercial crossbred nursery-age piglets that were experimentally infected with 1 of 2 type 2 isolates of PRRSV (NVSL 97-7985 [NVSL] and KS2006-72109 [KS06]). Phenotypes analyzed were viral load (VL) in blood during the first 21 d after infection (dpi) and weight gain (WG) from 0 to 42 dpi. We accounted for the previously identified QTL in the region on SSC4 in our models to increase power to identify additional regions. Many regions identified by single-SNP analyses were not identified using Bayes-B, but both analyses identified the same regions on SSC3 and SSC5 to be associated with VL in the KS06 trials and on SSC6 in the NVSL trials ( < 5 × 10); for WG, regions on SSC5 and SSC17 were associated in the NVSL trials ( < 3 × 10). No regions were identified with either method for WG in the KS06 trials. Except for the region on SSC4, which was associated with VL for both isolates (but only with WG for NVSL), identified regions did not overlap between the 2 PRRSV isolate data sets, despite high estimates of the genetic correlation between isolates for traits based on these data. We also identified genomic regions whose associations with VL or WG interacted with either PRRSV isolate or with genotype at the SSC4 QTL. Gene ontology (GO) annotation terms for genes located near moderately associated SNP ( < 0.003) were enriched for multiple immunologically (VL) and metabolism- (WG) related GO terms. The biological relevance of these regions suggests that, although it may increase the number of false positives, the use of single-SNP analyses and a relaxed threshold also increased the identification of true positives. In conclusion, although only the SSC4 QTL was associated with response to both PRRSV isolates, genes near associated SNP were enriched for the same GO terms across PRRSV isolates, suggesting that host responses to these 2 isolates are affected by the actions of many genes that function together in similar biological processes.

Abstracts of the 24th international isotope society (UK group) symposium: synthesis and applications of labelled compounds 2015

The 24th annual symposium of the International Isotope Society's United Kingdom Group took place at the Møller Centre, Churchill College, Cambridge, UK on Friday 6th November 2015. The meeting was attended by 77 delegates from academia and industry, the life sciences, chemical, radiochemical and scientific instrument suppliers. Delegates were welcomed by Dr Ken Lawrie (GlaxoSmithKline, UK, chair of the IIS UK group). The subsequent scientific programme consisted of oral presentations, short 'flash' presentations in association with particular posters and poster presentations. The scientific areas covered included isotopic synthesis, regulatory issues, applications of labelled compounds in imaging, isotopic separation and novel chemistry with potential implications for isotopic synthesis. Both short-lived and long-lived isotopes were represented, as were stable isotopes. The symposium was divided into a morning session chaired by Dr Rebekka Hueting (University of Oxford, UK) and afternoon sessions chaired by Dr Sofia Pascu (University of Bath, UK) and by Dr Alan Dowling (Syngenta, UK). The UK meeting concluded with remarks from Dr Ken Lawrie (GlaxoSmithKline, UK).

Genome-wide association analyses for growth and feed efficiency traits in beef cattle

A genome-wide association study using the Illumina 50K BeadChip included 38,745 SNP on 29 BTA analyzed on 751 animals, including 33 purebreds and 718 crossbred cattle. Genotypes and 6 production traits: birth weight (BWT), weaning weight (WWT), ADG, DMI, midtest metabolic BW (MMWT), and residual feed intake (RFI), were used to estimate effects of individual SNP on the traits. At the genome-wide level false discovery rate (FDR < 10%), 41 and 5 SNP were found significantly associated with BWT and WWT, respectively. Thirty-three of them were located on BTA6. At a less stringent significance level (P < 0.001), 277 and 27 SNP were in association with single traits and multiple traits, respectively. Seventy-three SNP on BTA6 and were mostly associated with BW-related traits, and heavily located around 30 to 50Mb. Markers that significantly affected multiple traits appeared to impact them in same direction. In terms of the size of SNP effect, the significant SNP (P < 0.001) explained between 0.26 and 8.06% of the phenotypic variation in the traits. Pairs of traits with low genetic correlation, such as ADG vs. RFI or DMI vs. BWT, appeared to be controlled by 2 groups of SNP; 1 of them affected the traits in same direction, the other worked in opposite direction. This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting growth and feed efficiency traits in beef cattle.

Optimizing a beef production system using specialized sire and dam lines

Tang, G., Stewart-Smith, J., Plastow, G., Moore, S., Basarab, J., MacNeil, M. D. and Wang, Z. 2011. Optimizing a beef production system using specialized sire and dam lines. Can. J. Anim. Sci. 91: 353–361. Crossbreeding is an effective method for improving the efficiency of production in commercial cow-calf operations. It exploits available heterosis (hybrid vigour) and complementarity between different breeds or populations (lines). Before adopting a crossbreeding system, commercial cattle producers should evaluate available genetic resources and feasible crossbreeding systems, and choose one that is most beneficial for their own environment, resources, and management. This study compared profitability of alternative crossbreeding systems based on Beefbooster beef cattle breeding strains through computer simulation. Biological and economic data were collected from commercial customers of Beefbooster in Montana and western Canada, and breeding records from the database of Beefbooster, Inc. Three maternal strains (M1, M2 and M4) and two specialized paternal strains (M3 and TX), were evaluated with two simulated crossbreeding systems. System 1 uses a rotational cross between M1 and M4 with yearling crossbred heifers bred to M3 sires. System 2 is based on a three-strain rotation of M1, M2 and M4 with yearling crossbred heifers bred to M3 to facilitate ease of calving and crossbred cows bred to a classical terminal sire strain TX. Simulated base profit from system 2 was $29.57 greater ($215.21 vs. $185.64 yr−1 per cow) than from system 1.

Structural analysis of MHC alleles in an RSV tumour regression chicken using a BAC library

The chicken major histocompatibility complex (MHC-B locus) has a strong association with resistance and susceptibility to numerous diseases. We have found a B haplotype designated WLA that associated with the regression of tumours caused by Rous sarcoma virus J strain (RSV-J). Haplotype WLA was identical to the regressive B6 haplotype when partial genotyping was performed (Poultry Science, 89, 2010, 651). We then constructed a bacterial artificial chromosome (BAC) library from a WLA homozygote chicken to evaluate the structure of this regression haplotype and compared it to those of the B6 haplotype. Comparison between WLA and B6 above 59 kb within the 167 kb, including 14 genes from BG1 to BF2, revealed 75 SNPs and 14 indels. However, several genes were identical between WLA and B6, including the BF1 and BF2 genes, which encode a class I molecule previously suggested to be related to the regression phenotype. The BLB2 gene encoding the MHC class II beta chain showed the greatest diversity, with 19 non-synonymous SNPs. A comparison of WLA and B6 haplotpyes that are associated with tumour regression and RIRa and B24 haplotypes associated with tumour progression suggests that DMA1, DMA2, BRD2, TAPBP and BLB2 genes are not involved in the intensity of RSV J tumour regression.

Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.