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13 results on '"Zhan, Denghuang"'

1. Priors from Envisioned Posterior Judgments: A Novel Elicitation Approach With Application to Bayesian Clinical Trials

Author

Ouyang, Yongdong, Eng, Janice J, Zhan, Denghuang, and Wong, Hubert

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

The uptake of formalized prior elicitation from experts in Bayesian clinical trials has been limited, largely due to the challenges associated with complex statistical modeling, the lack of practical tools, and the cognitive burden on experts required to quantify their uncertainty using probabilistic language. Additionally, existing methods do not address prior-posterior coherence, i.e., does the posterior distribution, obtained mathematically from combining the estimated prior with the trial data, reflect the expert's actual posterior beliefs? We propose a new elicitation approach that seeks to ensure prior-posterior coherence and reduce the expert's cognitive burden. This is achieved by eliciting responses about the expert's envisioned posterior judgments under various potential data outcomes and inferring the prior distribution by minimizing the discrepancies between these responses and the expected responses obtained from the posterior distribution. The feasibility and potential value of the new approach are illustrated through an application to a real trial currently underway., Comment: minor update

Published
2024

2. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Mental health, Neurological, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

6. Brain-age prediction:Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S., New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I., Breier, Alan, Brodaty, Henry, Buckner, Randy L., Buitelaar, Jan K., Cannon, Dara M., Caseras, Xavier, Clark, Vincent P., Conrod, Patricia J., Crivello, Fabrice, Crone, Eveline A., Dannlowski, Udo, Davey, Christopher G., de Haan, Lieuwe, de Zubicaray, Greig I., Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E., Franke, Barbara, Glahn, David C., Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E., Gur, Ruben C., Hahn, Tim, Harrison, Ben J., Hatton, Sean, Hickie, Ian B., Hulshoff Pol, Hilleke E., Jamieson, Alec J., Jernigan, Terry L., Jiang, Jiyang, Kalnin, Andrew J., Kang, Sim, Kochan, Nicole A., Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C., McDonald, Colm, McMahon, Katie L., Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S., Satterthwaite, Theodore D., Saykin, Andrew J., Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W., Soares, Jair C., Spalletta, Gianfranco, Tamnes, Christian K., Trollor, Julian N., Van't Ent, Dennis, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M., Williams, Steven C. R., Wu, Mon‐Ju, Zunta‐Soares, Giovana B., Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S., New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I., Breier, Alan, Brodaty, Henry, Buckner, Randy L., Buitelaar, Jan K., Cannon, Dara M., Caseras, Xavier, Clark, Vincent P., Conrod, Patricia J., Crivello, Fabrice, Crone, Eveline A., Dannlowski, Udo, Davey, Christopher G., de Haan, Lieuwe, de Zubicaray, Greig I., Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E., Franke, Barbara, Glahn, David C., Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E., Gur, Ruben C., Hahn, Tim, Harrison, Ben J., Hatton, Sean, Hickie, Ian B., Hulshoff Pol, Hilleke E., Jamieson, Alec J., Jernigan, Terry L., Jiang, Jiyang, Kalnin, Andrew J., Kang, Sim, Kochan, Nicole A., Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C., McDonald, Colm, McMahon, Katie L., Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S., Satterthwaite, Theodore D., Saykin, Andrew J., Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W., Soares, Jair C., Spalletta, Gianfranco, Tamnes, Christian K., Trollor, Julian N., Van't Ent, Dennis, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M., Williams, Steven C. R., Wu, Mon‐Ju, Zunta‐Soares, Giovana B., Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, and Ge, Ruiyang

Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

11. Nanostructured biosensor for tear glucose detection based on bioluminescence resonance energy transfer (BRET) mechanism

Author

Zhan, Denghuang

Subjects
Tear glucose, Glucose binding protein, Quantum dots, Biochemical and Biomolecular Engineering, Luciferase, Bioimaging and Biomedical Optics, Bioluminescence resonance energy transfer (BRET)
Abstract

Bioluminescence Resonance Energy Transfer (BRET), a sensitive, non-destructive and self-illuminated method, has been now commonly used to test protein interactions. Here, we describe a BRET sensor for non-invasively detecting glucose molecules. The sensor is made by the bioconjugation of quantum dots and recombinant protein. The recombinant protein contains the bacterial glucose binding protein (GBP) and a bioluminescent protein, Renilla luciferase (Rluc), used as the donor with the emission peak at 470 nm, which is able to excite the acceptor of BRET sensor made of cadmium tellurium quantum dots ( CdTe QDs) with the emission peak at 570 nm. The distance between the BRET pair depends on the shape of GBP. In the presence of glucose, the conformational change of GBP leads to the enhanced BRET phenomenon due to the short distance between the BRET pair. To optimal the BRET sensor, the ratios of the acceptor (QDs) to the donor (GBP-Rluc), the bioconjugation process, and the performance of recombinant protein have been thoroughly investigated. The results indicate a linear relationship between luminescence intensity ratio and the concentration of glucose in the two ranges, from 0 mM to 0.1 mM and from 0.2mM to 0.8mM, respectively. The limit of detection (LOD) is calculated as15 nM. Meanwhile, tear glucose of animal model were measured by this designed sensor. This study demonstrates a proof-of-concept of a non-invasive measurement system for monitoring glucose.

Published
2017

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