| 1. |
- Thomas, HS, et al.
(author)
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- 2019
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swepub:Mat__t
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| 2. |
- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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| 3. |
- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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| 4. |
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| 5. |
- Jones, Benedict C, et al.
(author)
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To which world regions does the valence-dominance model of social perception apply?
- 2021
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In: Nature Human Behaviour. - : Springer Science and Business Media LLC. - 2397-3374. ; 5:1, s. 159-169
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Journal article (peer-reviewed)abstract
- Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .
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| 6. |
- Lee, Sunjae, et al.
(author)
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Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance
- 2016
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In: Cell Metabolism. - : Elsevier BV. - 1550-4131 .- 1932-7420. ; 24:1, s. 172-184
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Journal article (peer-reviewed)abstract
- To investigate the biological processes that are altered in obese subjects, we generated cell-specific integrated networks (INs) by merging genome-scale metabolic, transcriptional regulatory and protein-protein interaction networks. We performed genome-wide transcriptomics analysis to determine the global gene expression changes in the liver and three adipose tissues from obese subjects undergoing bariatric surgery and integrated these data into the cell-specific INs. We found dysregulations in mannose metabolism in obese subjects and validated our predictions by detecting mannose levels in the plasma of the lean and obese subjects. We observed significant correlations between plasma mannose levels, BMI, and insulin resistance (IR). We also measured plasma mannose levels of the subjects in two additional different cohorts and observed that an increased plasma mannose level was associated with IR and insulin secretion. We finally identified mannose as one of the best plasma metabolites in explaining the variance in obesity-independent IR.
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| 7. |
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| 8. |
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| 9. |
- Piening, B. D., et al.
(author)
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Integrative Personal Omics Profiles during Periods of Weight Gain and Loss
- 2018
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In: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 6:2
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Journal article (peer-reviewed)abstract
- Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.
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| 10. |
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