5 research outputs found
Theoretical study of stress and strain distribution in coupled pyramidal InAs quantum dots embedded in GaAs by finite element method
Stress and strain distributions in and around a single or two-coupled pyramidal InAs quantum dots (QDs) embedded in GaAs are calculated by finite element methods according to the continuum elasticity theory. By changing the quantum dot spacing and thickness of cap layer, the results about strain and stress distributions show compressive strain and stress distribution in the QDs and relaxation undergoes two stages with different speeds for different quantum dot height, quantum width and thickness of cap layer. The stress and strain distributions of pyramidal QDs would not vary monotonously with geometric dimensions. The height of quantum dot and cap layer thickness can effectively adjust the vertical correlation of self-assembly QDs according to the calculation. The shape of stress distribution at surface of cap layer can be tuned from a quadrangle into a circle by increasing the thickness of cap layer or decreasing the height of quantum dot. Also, a new approach to grow quantum ring is found in this paper. The calculations of two-coupled QDs show that the self-assembly technology might fail if the horizontal distance between two QDs is not large enough. The stress induced by upper QDs will be relaxed to zero with a longer distance downwards is found in this paper
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Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma
Acknowledgements: The authors thank all the individuals for their participation in the parent studies and all the researchers, clinicians, technicians and administrative staff for their contribution to the studies.Funder: University of Hawai'i; DOI: https://doi.org/10.13039/100008782Funder: National Human Genome Research Institute; DOI: https://doi.org/10.13039/100000051: Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC. Significance: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions
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Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.
Acknowledgements: The authors thank all the individuals for their participation in the parent studies and all the researchers, clinicians, technicians and administrative staff for their contribution to the studies.Funder: University of Hawai'i; DOI: https://doi.org/10.13039/100008782Funder: National Human Genome Research Institute; DOI: https://doi.org/10.13039/100000051UNLABELLED: Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC. SIGNIFICANCE: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions