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James J. Lee

Researcher at University of Minnesota

Publications -  86
Citations -  21706

James J. Lee is an academic researcher from University of Minnesota. The author has contributed to research in topics: Genome-wide association study & Behavioural genetics. The author has an hindex of 38, co-authored 86 publications receiving 17084 citations. Previous affiliations of James J. Lee include Georgetown University & Harvard University.

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Effects of Preventive, Early, and Late Antifungal Chemotherapy with Fluconazole in Different Granulocytopenic Models of Experimental Disseminated Candidiasis

TL;DR: Fluconazole was most effective against disseminated candidiasis in persistently granulocytopenic rabbits when used for prevention or early treatment.
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Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.

TL;DR: The results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction, and no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake is found.
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General Intelligence in Another Primate: Individual Differences across Cognitive Task Performance in a New World Monkey (Saguinus oedipus)

TL;DR: Individual differences in cognitive abilities within at least one other primate species can be characterized by a general intelligence factor, supporting the hypothesis that important aspects of human cognitive function most likely evolved from ancient neural substrates.
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The Moral-Conventional Distinction in Mature Moral Competence

TL;DR: This paper examined the role of moral and conventional transgressions in mature moral cognition using a battery of adult-appropriate cases (e.g., vehicular and sexual assault, reckless behavior, and violations of etiquette and social contracts).
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Why It Is Hard to Find Genes Associated With Social Science Traits: Theoretical and Empirical Considerations

TL;DR: The challenge for social science genomics is the likelihood that genes are connected to behavioral variation by lengthy, nonlinear, interactive causal chains, and unraveling these chains requires allying with personal genomics to take advantage of the potential for large sample sizes as well as continuing with traditional epidemiological studies.