Twin studies have suggested that there are genetic influences on inter-individual variation in terms of verbal abilities, and candidate genes have been identified by genome-wide association studies. However, the brain activities under genetic influence during linguistic processing remain unclear. In this study, we investigated neuromagnetic activities during a language task in a group of 28 monozygotic (MZ) and 12 dizygotic (DZ) adult twin pairs. We examined the spatio-temporal distribution of the event-related desynchronizations (ERDs) in the low gamma band (25-50Hz) using beamformer analyses and time-frequency analyses. Heritability was evaluated by comparing the respective MZ and DZ correlations. The genetic and environmental contributions were then estimated by structural equation modeling (SEM). We found that the peaks of the low gamma ERDs were localized to the left frontal area. The power of low gamma ERDs in this area exhibited higher similarity between MZ twins than that between DZ twins. SEM estimated the genetic contribution as approximately 50%. In addition, these powers were negatively correlated with the behavioral verbal scores. These results improve our understanding of how genetic and environmental factors influence cerebral activities during linguistic processes.
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http://dx.doi.org/10.1016/j.neuroimage.2016.05.066 | DOI Listing |
Plant Methods
January 2025
Faculty of Agriculture, Agriculture and Forestry University, Bharatpur, 13712, Nepal.
Background: Crossover interactions stemming from phenotypic plasticity complicate selection decisions when evaluating hybrid maize with superior grain yield and consistent performance. Consequently, a two-year, region-wide investigation of 45 hybrids maize across Nepal was performed with the aim of disclosing both site and wide adapted hybrids. Utilizing an innovative "ProbBreed" package, based on Bayesian probability analysis of randomized complete block designs with three replicated trials at each station, this study substantively streamlines hybrids maize selection.
View Article and Find Full Text PDFBMC Genom Data
January 2025
School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Office 101E, Ottawa, Ontario, K1G 5Z3, Canada.
High intraocular pressure (IOP) is an important risk factor for glaucoma, which is influenced by genetic and environmental factors. However, the etiology of high IOP remains uncertain. Metabolites are compounds involved in metabolism which provide a link between the internal (genetic) and external environments.
View Article and Find Full Text PDFBMC Genomics
January 2025
Department of Virology, Norwegian Institute of Public Health, Oslo, 0456, Norway.
The COVID-19 pandemic has underscored the importance of virus surveillance in public health and wastewater-based epidemiology (WBE) has emerged as a non-invasive, cost-effective method for monitoring SARS-CoV-2 and its variants at the community level. Unfortunately, current variant surveillance methods depend heavily on updated genomic databases with data derived from clinical samples, which can become less sensitive and representative as clinical testing and sequencing efforts decline.In this paper, we introduce HERCULES (High-throughput Epidemiological Reconstruction and Clustering for Uncovering Lineages from Environmental SARS-CoV-2), an unsupervised method that uses long-read sequencing of a single 1 Kb fragment of the Spike gene.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore, 641003, Tamil Nadu, India.
Magnesium (Mg) an essential plant nutrient is widespread deficient in the acidic soils of Nilgiris of Tamil nadu, India. The vegetable yield and quality is especially affected due to deficiency of nutrients like Mg. This study investigates soil characteristics and bacterial diversity in the Nilgiris district of Tamil Nadu, India, with respect to Mg deficiency.
View Article and Find Full Text PDFNat Ecol Evol
January 2025
Center for Ecosystem Sentinels, Department of Biology, University of Washington, Seattle, WA, USA.
The emergence of generative artificial intelligence (AI) models specializing in the generation of new data with the statistical patterns and properties of the data upon which the models were trained has profoundly influenced a range of academic disciplines, industry and public discourse. Combined with the vast amounts of diverse data now available to ecologists, from genetic sequences to remotely sensed animal tracks, generative AI presents enormous potential applications within ecology. Here we draw upon a range of fields to discuss unique potential applications in which generative AI could accelerate the field of ecology, including augmenting data-scarce datasets, extending observations of ecological patterns and increasing the accessibility of ecological data.
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