We present a powerful method to enhance the magnetic plasmon (MP) resonances of metamaterials composed of periodic arrays of U-shaped metallic split-ring resonators (SRRs) for high-quality sensing. We show that by suspending the metamaterials to reduce the effect of the substrate, the strong diffraction coupling of MP resonances can be achieved, which leads to a narrow-band mixed MP mode with a large magnetic field enhancement. It is also shown that for such a diffraction coupling, the magnetic field component of the lattice resonance mode of periodic arrays must be parallel to the induced magnetic moment in the metallic SRRs. Importantly, the sensitivity and the figure of merit (FOM) of the suspended metamaterials can reach as high as 1300 nm/RIU and 40, respectively. These results suggest that the proposed metamaterials may find great potential applications in label-free biomedical sensing.

SUMMARY Aminoacyl-tRNA synthetases (AARSs) catalyze aminoacylation of tRNAs in the cytoplasm. Surprisingly, AARSs also have critical extracellular and nuclear functions. Evolutionary pressure for new functions might be manifested by splice variants that skip only an internal catalytic domain (CD) and link non-catalytic N- and C-terminal polypeptides. Using disease-associated histidyl-tRNA synthetase (HisRS) as an example, we discovered an expressed 171 amino acid protein (HisRSΔCD) that deleted the entire CD, and joined an N-terminal WHEP to the C-terminal anticodon-binding domain (ABD). X-ray crystallographic and 3-D NMR methods revealed the first structures of human HisRS and HisRSΔCD. In contrast to homodimeric HisRS, HisRSΔCD is monomeric, where rupture of the ABD’s packing with CD resulted in a dumbbell-like structure of flexibly linked WHEP and ABD domains. In addition, the ABD of HisRSΔCD presents a new local conformation. This natural internally deleted HisRS suggests evolutionary pressure to reshape AARS tertiary and quaternary structures for repurposing.

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The power of genotype–phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) represent a set of emerging methods that select variants based on their effect on gene expressions, providing pretrained linear combinations of variants for downstream association mapping. In contrast to this, kernel methods such as sequence kernel association test (SKAT) model genotypic and phenotypic variance use various kernel functions that capture genetic similarity between subjects, allowing nonlinear effects to be included. From the perspective of machine learning, these two methods cover two complementary aspects of feature engineering: feature selection/pruning and feature aggregation. Thus far, no thorough comparison has been made between these categories, and no methods exist which incorporate the advantages of TWAS- and kernel-based methods. In this work, we developed a novel method called kernel-based TWAS (kTWAS) that applies TWAS-like feature selection to a SKAT-like kernel association test, combining the strengths of both approaches. Through extensive simulations, we demonstrate that kTWAS has higher power than TWAS and multiple SKAT-based protocols, and we identify novel disease-associated genes in Wellcome Trust Case Control Consortium genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).