Abstract: A method to predict a propensity of an infant to develop atopic dermatitis. The method includes observing the expression of S100A8/A9 on a skin area of an infant; comparing the expression to determined standard, wherein the determined standard is ascertained by measuring a level of the biomarker in a subject or pool of subjects who have demonstrated an absence of atopic dermatitis; and determining the propensity of an infant to develop atopic dermatitis, wherein an increase in the expression as compared to the determined standard indicates propensity of the infant to develop atopic dermatitis.

Abstract: Methods, techniques, and systems for dynamically allocating graphics processing units among virtual machines are provided. Example embodiments provide a dynamic GPU allocation system (“DGAS”), which enables the efficient allocation of physical GPU resources to one or more virtual machines. In one embodiment, the DGAS comprises virtualization logic running on a server computing system that computes GPU benefit factors for the virtual machines on a dynamic basis, and combines the computed GBFs with static priorities to determine a ranked ordering of virtual machines. The available GPU resources are then allocated to some subset of these ranked virtual machines as physical GPU capacity is matched with the requirements of the subset. Physical GPU resources are then allocated to the subset of virtual machines that have the highest promise of GPU utilization.

Abstract: Methods, techniques, and systems for dynamically allocating graphics processing units among virtual machines are provided. Example embodiments provide a dynamic GPU allocation system (“DGAS”), which enables the efficient allocation of physical GPU resources to one or more virtual machines. In one embodiment, the DGAS comprises virtualization logic running on a server computing system that computes GPU benefit factors for the virtual machines on a dynamic basis, and combines the computed GBFs with static priorities to determine a ranked ordering of virtual machines. The available GPU resources are then allocated to some subset of these ranked virtual machines as physical GPU capacity is matched with the requirements of the subset. Physical GPU resources are then allocated to the subset of virtual machines that have the highest promise of GPU utilization.