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   Going beyond the limited design freedoms of traditional photonic crystals, we experimentally show how photonic metacrystals exploit the inclusion of subwavelength dielectric scatterers in the unit cell to deterministically modify k-space and real space profiles. 

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   Li, Jixing; Lai, Marco; Pylkkänen, Liina (January 2024, Imaging Neuroscience)

   Abstract Naturalistic paradigms using movies or audiobooks have become increasingly popular in cognitive neuroscience, but connecting them to findings from controlled experiments remains rare. Here, we aim to bridge this gap in the context of semantic composition in language processing, which is typically examined using a “minimal” two-word paradigm. Using magnetoencephalography (MEG), we investigated whether the neural signatures of semantic composition observed in an auditory two-word paradigm can extend to naturalistic story listening, and vice versa. Our results demonstrate consistent differentiation between phrases and single nouns in the left anterior and middle temporal lobe, regardless of the context. Notably, this distinction emerged later during naturalistic listening. Yet this latency difference disappeared when accounting for various factors in the naturalistic data, such as prosody, word rate, word frequency, surprisal, and emotional content. These findings suggest the presence of a unified compositional process underlying both isolated and connected speech comprehension. 

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   Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022. 

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