High-gain subwavelength resonant cavity antennas based on metamaterial ground planes

Planar metarnaterial Surfaces with negative reflection phase values are proposed as ground planes in a high-gain resonant cavity antenna configuration. The antenna is formed by the metarnaterial ground plane (MGP) and a superimposed metallodielectric electromagnetic band gap (MEBG) array that acts as a partially reflective surface (PRS). A single dipole positioned between the PRS and the ground IS utilised as the excitation. Ray analysis is employed to describe the functioning of the antennas and to qualitatively predict the effect of the MGP oil the antenna performance. By employing MGPs with negative reflection phase values, the planar antenna profile is reduced to subwavelength values (less than lambda/6) whilst maintaining high directivity. Full-wave simulations have been carried out with commercially available software (Microstripes (TM)). The effect of the finite PRS size on the antenna radiation performance (directivity and sidelobe level) is studied. A prototype has been fabricated and tested experimentally in order to validate the predictions.

Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses

BACKGROUND:

We have recently identified a number of Quantitative Trait Loci (QTL) contributing to the 2-fold muscle weight difference between the LG/J and SM/J mouse strains and refined their confidence intervals. To facilitate nomination of the candidate genes responsible for these differences we examined the transcriptome of the tibialis anterior (TA) muscle of each strain by RNA-Seq.

13,726 genes were expressed in mouse skeletal muscle. Intersection of a set of 1061 differentially expressed transcripts with a mouse muscle Bayesian Network identified a coherent set of differentially expressed genes that we term the LG/J and SM/J Regulatory Network (LSRN). The integration of the QTL, transcriptome and the network analyses identified eight key drivers of the LSRN (Kdr, Plbd1, Mgp, Fah, Prss23, 2310014F06Rik, Grtp1, Stk10) residing within five QTL regions, which were either polymorphic or differentially expressed between the two strains and are strong candidates for quantitative trait genes (QTGs) underlying muscle mass. The insight gained from network analysis including the ability to make testable predictions is illustrated by annotating the LSRN with knowledge-based signatures and showing that the SM/J state of the network corresponds to a more oxidative state. We validated this prediction by NADH tetrazolium reductase staining in the TA muscle revealing higher oxidative potential of the SM/J compared to the LG/J strain (p<0.03).

Thus, integration of fine resolution QTL mapping, RNA-Seq transcriptome information and mouse muscle Bayesian Network analysis provides a novel and unbiased strategy for nomination of muscle QTGs.