The sperm-mediated gene transfer method is applicable to transgenesis in many species that use spermatozoa for reproduction recently, which has been shown various results. In the current study, we show that transgenic porcine embryos can be efficiently produced by employing a simple transfection method that uses magnetic nanoparticles (MNPs). The complexes formed between plasmid DNA and MNPs were bounded on ejaculated boar spermatozoa at a higher efficiency compared to methods using DNA alone or lipofection. Using confocal microscopy, rhodamine fluorophore-labelled MNPs were detected on external surfaces of the spermatozoa membrane, which were bounded on zona pellucida of in vitro maturated oocyte during in vitro fertilization. Electron microscopy revealed that clusters of MNPs were detected in inside of plasma membrane and nucleus of the spermatozoa head. Additionally, we found that magnetofected boar spermatozoa could be fertilized with oocytes in vitro and that the resulting gene of green fluorescent protein was detected in fertilized eggs by genomic PCR analysis. Taken together, these results suggest that MNPs can be used to efficiently introduce a transgene into embryo via spermatozoa.

Stereolithography has attracted more attention due to better part build accuracy than other rapid prototyping technologies. However, this build method still limits wider applications due to the unsatisfactory level of dimensional accuracy that remains with the current technology. To improve accuracy and reduce part distortion, understanding the physics involved in the relationship between the operating input parameters and the part dimensional accuracy is prerequisite. In this paper, this causality is identi®ed through a process model obtained via an arti®cial neural network based upon 140 actual build parts. The network is so constructed that it relates the process input parameters to part dimensional accuracy. The neural network model is found to predict the eVects of the input parameters on the accuracy with reasonable accuracy. The prediction performance is discussed in detail for various process parameter ranges.