Vitamin C [HRP]

We have developed a vortex-core magnetic force microscope (VC MFM) for magnetic field imaging at the nanoscale for many research fields-physics, biology, materials science, and metrology. The method solves principally quantitative scanning by increasing magnetic tip durability and introducing its calibration. We show that nature itself gives us a sharp, durable, and calibrated magnetic probe. It is represented by a narrow magnetic vortex core located in the center of a ferromagnetic disk placed at the apex of a scanning tip. Such a tip offers potentially high spatial resolution-the vortex core is magnetically sharp (the vortex diameter is<20nm for Permalloy), but at the same time, the disk is geometrically blunt and therefore durable. The magnetic moment of the vortex core is independent of the disk diameter and can be tuned smoothly by the disk thickness. We describe here the basic properties of the VC tip, its technology, and sensitivity to the magnetic field and show its durability. The first results obtained on hard disk drive are promising-from the analysis of data tracks, the spatial resolution of the VC tip is only a bit worse than the one of the standard MFM tips. We believe that the VC tip could be a sensor of choice for magnetic field imaging for scientific areas mentioned above.

Background The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses anHmatrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and H-AM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population. Results VC estimates from H-AM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in H-AM estimates arises when selective genotyping is used to construct theH-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of genetic variance from A-AM were also significantly overestimated when the effect of genomic selection was strong. Our results suggest that VC estimates from H-AM under random genotyping have the expected values. Predicted breeding values from H-AM were inflated when VC estimates were biased, and inflation differed between genotyped and ungenotyped animals, which can lead to suboptimal selection decisions. Conclusions We conclude that VC estimates from H-AM are biased with selective genotyping, but are close to expected values with random genotyping.VC estimates from A-AM in populations under genomic selection are also biased but to a much lesser degree. Therefore, we recommend the use of H-AM with random genotyping to estimate VC for populations under genomic selection. Our results indicate that it is still possible to use selective genotyping in selection, but then VC estimation should avoid the use of genotypes from one side only of the distribution of phenotypes. Hence, a dual genotyping strategy may be needed to address both selection and VC estimation.