Quantification of bull sperm characteristics measured by computer-assisted sperm analysis (CASA) and the relationship to fertility.
ABSTRACT Two experiments were conducted to evaluate semen quality of bulls housed under controlled conditions at a large AI facility and relate results to fertility. In Experiment 1 semen was collected from six 6-yr-old bulls twice daily at 3- to 4-d intervals for 3 d. In Experiment 2 eleven 6- to 11-yr-old bulls were used. Extensive breeding information was available and semen was collected as in Experiment 1 but replicated 4 times. Standard semen analysis and computer-assisted sperm analysis (CASA) with the Hamilton Thorne IVOS, model 10 unit, were performed on 36 first and second ejaculates in Experiment 1 and on 44 first ejaculates in Experiment 2. Sixteen fields (2 chambers with 8 fields per chamber) were examined per sample. In Experiment 1 the correlation between estimated sperm concentration by spectrophotometry and CASA was 0.91 (P < 0.01). Among bulls the range in the percentage of motile spermatozoa was 52 to 82 for CASA versus 62 to 69 for subjective measurements made by highly experienced technicians. Thus, CASA, with high repeatability, provided a more discriminating estimate of the percentage of motile sperm cells than did the subjective procedure. Bull effect was much greater than any other variable in the experiments. Chamber differences were small and so the results for the 2 chambers with 8 fields each were combined. One to five CASA values were correlated with bull fertility, defined as 59-day nonreturn rates corrected for cow and herd effects. The percentage of motile spermatozoa accounted for a small fraction of the total variation in fertility (r2 = 0.34). However higher r2 values (0.68 to 0.98) were obtained for 2 to 5 variables used in the multiple regression equations. The results are promising, and further testing will determine more precisely which of these CASA variables are most useful in estimating bull fertility potential.
SourceAvailable from: Ondrej Simonik[Show abstract] [Hide abstract]
ABSTRACT: Present address: 1,3,7,8 Ph.D. student (email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org), 2 Post-doc (email@example.com), 5 Associated Professor (firstname.lastname@example.org). 6 Scientific worker (email@example.com), Czech University of life Sciences, Department of Animal Husbandry. 4 Associated Professor (firstname.lastname@example.org), Head of Department, Veterinary Sciences, CULS in Prague. For successful fertilization, both the male and the female gamete of every species is important. In males, this factor is currently represented by the quality of insemination dose (Beran et al. 2012, Beran et al. 2013) mainly in cattle, where artificial insemination (AI) is the biotechnological method used on a wide scale (Muino et al. 2008, Gravance et al. 2009, Sundararaman et al. 2012). Quality control of insemination doses before and after thawing is mostly carried out subjectively, mainly on the basis of an estimation of motile spermatozoa ratio, since sperm motility is considered as closely related to fertility (Puglisi et al. 2012). Results of this estimation can be affected by bias and inaccuracy (Amann and Waberski 2014); moreover there are discrepancies among results of different studies (Farrell et al. 1998, Januskauskas et al.The Indian journal of animal sciences 09/2014; 85(1):3 - 11. · 0.13 Impact Factor
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ABSTRACT: Early estimation of bull fertility is highly desirable for the conservation of male genetics of endangered species and for the exploitation of genetically superior sires in artificial insemination programs. The present work was conducted as a proof-of-principle study to identify fertility-associated metabolites in dairy bull seminal plasma and blood serum using proton nuclear magnetic resonance (1H NMR). Semen and blood samples were collected from high- and low-fertility breeding bulls (n = 5 each), stationed at Semex, Guelph, Canada. NMR spectra of serum and seminal plasma were recorded at a resonance frequency of 500.13 MHz on a Bruker Avance-500 spectrometer equipped with an inverse triple resonance probe (TXI, 5 mm). Spectra were phased manually, baseline corrected, and calibrated against 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid at 0.0 parts per million (ppm). Spectra were converted to an appropriate format for analysis using Prometab software running within MATLAB. Principal component analysis was used to examine intrinsic variation in the NMR data set, and to identify trends and to exclude outliers. Partial least square-discriminant analysis was performed to identify the significant features between fertility groups. The fertility-associated metabolites with variable importance in projections (VIP) scores >2 were citrate (2.50 ppm), tryptamine/taurine (3.34–3.38 ppm), isoleucine (0.74 ppm), and leucine (0.78 ppm) in the seminal plasma; and isoleucine (1.14 ppm), asparagine (2.90–2.94 ppm), glycogen (3.98 ppm), and citrulline (1.54 ppm) in the serum. These metabolites showed identifiable peaks, and thus can be used as biomarkers of fertility in breeding bulls. Mol. Reprod. Dev. 2015. © 2015 Wiley Periodicals, Inc.Molecular Reproduction and Development 01/2015; 82(2). DOI:10.1002/mrd.22450 · 2.68 Impact Factor
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ABSTRACT: The assessment of the state of the acrosome is a priority in artificial insemination centres since it is one of the main causes of function loss. In this work, boar spermatozoa present in gray scale images acquired with a phase-contrast microscope have been classified as acrosome-intact or acrosome-damaged, after using fluorescent images for creating the ground truth. Based on shape prior criteria combined with Otsu's thresholding, regional minima and watershed transform, the spermatozoa heads were segmented and registered. One of the main novelties of this proposal is that, unlike what previous works stated, the obtained results show that the contour information of the spermatozoon head is important for improving description and classification. Other of this work novelties is that it confirms that combining different texture descriptors and contour descriptors yield the best classification rates for this problem up to date. The classification was performed with a Support Vector Machine backed by a Least Squares training algorithm and a linear kernel. Using the biggest acrosome intact-damaged dataset ever created, the early fusion approach followed provides a 0.9913 F-Score, outperforming all previous related works. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.Computer methods and programs in biomedicine 03/2015; DOI:10.1016/j.cmpb.2015.03.005 · 1.09 Impact Factor