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Single-layer centrifugation (SLC) with a low-density colloid is an efficient method for removing contaminating microorganisms from boar semen while recovering most spermatozoa from the original sample. This study tested the performance of this technique, using 50-ml tubes, by spiking commercial semen doses prepared without antibiotics with selected bacterial species followed by storage at 17 degrees C. The doses were spiked up to 102/ml CFU (colony forming units) of the bacteria Burkholderia ambifaria, Pseudomonas aeruginosa, and Staphylococcus sim-ulans. The semen was processed by SLC (15 ml of sample and 15 ml of colloid) with the colloid Porcicoll at 20% (P20) and 30% (P30), with a spiked control (CTL) and an unspiked control (CTL0), analyzing microbiology and sperm quality on days 0, 3 and 7. SLC completely removed B. ambifaria and S. simulans, considerably reducing P. aeruginosa and overall contamination (especially P30, similar to 104 CFU/ml of total contamination on day 7, median). Sperm viability was lower in P20 and P30 samples at day 0, with higher cytoplasmic ROS. Still, results were similar in all groups on day 3 and reversed on day 7, indicating a protective effect of SLC (possibly directly by removal of damaged sperm and indirectly because of lower bacterial contamination). Sperm chromatin was affected by the treatment (lower DNA fragmentation and chromatin decondensation) and storage (higher overall condensation on day 7 as per chromomycin A3 and monobromobimane staining). In conclusion, SLC with low-density colloids can remove most bacteria in a controlled contamination design while potentially improving sperm quality and long-term storage at practical temperatures.

We propose a method for the classification of boar sperm heads based on their intracellular intensity distributions observed in microscopic images. The image pre-processing comprises segmentation of cell heads and normalization for brightness, contrast and size. Next, we define a model distribution of head intracellular intensity of an alive cell using a set of head images assumed to be alive by veterinary experts. We now consider two other sets of cell head images, one formed by heads assumed to be alive by experts and another formed by cells which present some abnormalities in their cytoplasm densities and are considered as dead by the experts. We define a measure of deviation from the model intensity distribution and for each head image of the two test sets we compute the deviation from the model. While the distributions of deviation values for alive and dead cells overlap, it is possible to choose an optimal value of a decision criterion for single cell classification in such a way that the error made in the estimation of the fraction of alive cells in a sample is minimal. In the range [0.7,1.0] of interesting values of the fraction of alive cells, the standard deviation of the fraction estimation error for samples of 100 head images is smaller than 0.04. Thus, in 95% of the cases the value of the fraction of alive cells in a sample estimated by a veterinary expert will be within 8% of the estimation made according to the proposed method. This result satisfies the requirements of veterinary practice.