The Satisfaction With Life Scale (SWLS) is a widely used self-report measure of subjective well-being, but studies of its measurement invariance across a large number of nations remain limited. Here, we utilised the Body Image in Nature (BINS) dataset-with data collected between 2020 and 2022 -to assess measurement invariance of the SWLS across 65 nations, 40 languages, gender identities, and age groups (N = 56,968). All participants completed the SWLS under largely uniform conditions.
View Article and Find Full Text PDFEnvironmental variation often drives evolutionary processes like population differentiation, local adaptation and speciation. We used genome-scale data to investigate the contribution of environmental variation to evolution of the North Caribbean bark anole (Anolis distichus), a widespread common lizard that exhibits impressive phenotypic variation across varying habitats on the island of Hispaniola. We obtained new double-digest restriction-associated DNA sequence data (ddRADseq) from nearly 200 individuals and used 53 GIS data layers representing a range of environmental variables.
View Article and Find Full Text PDFGenome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions.
View Article and Find Full Text PDFDinucleases of the DEDD superfamily, such as oligoribonuclease, Rexo2 and nanoRNase C, catalyze the essential final step of RNA degradation, the conversion of di- to mononucleotides. The active sites of these enzymes are optimized for substrates that are two nucleotides long, and do not discriminate between RNA and DNA. Here, we identified a novel DEDD subfamily, members of which function as dedicated deoxydinucleases (diDNases) that specifically hydrolyze single-stranded DNA dinucleotides in a sequence-independent manner.
View Article and Find Full Text PDFBackground: Evidence is limited in gynecologic cancers on best practices for clinical trial screening, but the risk of ineffective screening processes and subsequent under-enrollment introduces significant cost to patient, healthcare systems, and scientific advancement. Absence of a defined screening process makes determination of who and when to screen potential patients inconsistent allowing inefficiency and potential introduction of biases. This is especially germane as generative artificial intelligence (AI), and electronic health record (EHR) integration is applied to trial screening.
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