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411 results on '"Popovic, Jelena"'

2. Lessons learned in the practice of community-based participatory research with community partner collaboration in study design and implementation: the community scientist model

Author

Morrell, Megan A., Willis, Tabitha R., Brown, Denisha R., O’Brian, Catherine A., Post, Sharon L., Woloschak, Gayle E., Bonini, Marcelo G., Paunesku, Tatjana, Popovic, Jelena, Manning, Tarneka M., Henley, Candace, Girotti, Jorge, Rogers, Rosemarie, Velásquez, Carmen, López, José, Glenn, Joanne, and Simon, Melissa A.

Published
2023
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9. Environmental signals rather than layered ontogeny imprint the function of type 2 conventional dendritic cells in young and adult mice

Author

Papaioannou, Nikos E., Salei, Natallia, Rambichler, Stephan, Ravi, Kaushikk, Popovic, Jelena, Küntzel, Vanessa, Lehmann, Christian H. K., Fiancette, Remi, Salvermoser, Johanna, Gajdasik, Dominika W., Mettler, Ramona, Messerer, Denise, Carrelha, Joana, Ohnmacht, Caspar, Haller, Dirk, Stumm, Ralf, Straub, Tobias, Jacobsen, Sten Eirik W., Schulz, Christian, Withers, David R., Schotta, Gunnar, Dudziak, Diana, and Schraml, Barbara U.

Published
2021
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15. Store depletion-induced h-channel plasticity rescues a channelopathy linked to Alzheimer’s disease

Author

Musial, Timothy F., Molina-Campos, Elizabeth, Bean, Linda A., Ybarra, Natividad, Borenstein, Ronen, Russo, Matthew L., Buss, Eric W., Justus, Daniel, Neuman, Krystina M., Ayala, Gelique D., Mullen, Sheila A., Voskobiynyk, Yuliya, Tulisiak, Christopher T., Fels, Jasmine A., Corbett, Nicola J., Carballo, Gabriel, Kennedy, Colette D., Popovic, Jelena, Ramos-Franco, Josefina, Fill, Michael, Pergande, Melissa R., Borgia, Jeffrey A., Corbett, Grant T., Pahan, Kalipada, Han, Ye, Chetkovich, Dane M., Vassar, Robert J., Byrne, Richard W., Matthew Oh, M., Stoub, Travis R., Remy, Stefan, Disterhoft, John F., and Nicholson, Daniel A.

Published
2018
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21. Unraveling the Reaction Mystery of Li and Na with Dry Air

Author

Li, Yuqi, primary, Liu, Qiunan, additional, Wu, Siyuan, additional, Geng, Lin, additional, Popovic, Jelena, additional, Li, Yu, additional, Chen, Zhao, additional, Wang, Haibo, additional, Wang, Yuqi, additional, Dai, Tao, additional, Yang, Yang, additional, Sun, Haiming, additional, Lu, Yaxiang, additional, Zhang, Liqiang, additional, Tang, Yongfu, additional, Xiao, Ruijuan, additional, Li, Hong, additional, Chen, Liquan, additional, Maier, Joachim, additional, Huang, Jianyu, additional, and Hu, Yong-Sheng, additional

Published
2023
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24. Single‐nucleus RNA sequencing reveals hippocampal cell type‐specific transcriptional alterations in Alzheimer's‐linked ACE variant R1284Q knock‐in mice.

Author

Salvo, Miranda A, Cuddy, Leah K, Chandra, Sidhanth, Ramakrishnan, Abhirami, Piehl, Natalie, Watson, Thomas J, Gate, David, Popovic, Jelena, Prokopenko, Dmitry, Tanzi, Rudolph E., and Vassar, Robert J.

Abstract

Background: Angiotensin‐converting enzyme (ACE) is a validated risk locus for developing late‐onset Alzheimer's disease (LOAD). ACE1 controls blood pressure through the renin‐angiotensin system (RAS), but it is also present and acts locally in the brain. Hypertension is associated with an increased risk for developing AD, and people taking select RAS‐targeting therapeutics have reduced incidence of AD. The ACE variant rs4980 (R1284Q murine mutation) was discovered in LOAD families through WGS. Our group previously showed that ACE1 R1284Q caused age‐associated hippocampal neurodegeneration and gliosis in mutant knock‐in (KI) mice, which was more aggressive in females. Importantly, these phenotypes were rescued by treatment with anti‐hypertensive drugs. Our previous study showed that ACE1 R1284Q caused neuron death in mice, however the mechanism is still unknown. This work aims to identify vulnerable hippocampal cell populations and pathways which might clarify the mechanism of ACE1 R1284Q‐mediated neurodegeneration. Method: Single nuclei were extracted from 30mg of flash‐frozen hippocampi from 6‐, and 12‐month‐old R1284Q ACE+/+ (WT) and ACEKI/KI (KI) and processed using 10X Genomics 3' Dual‐Index chemistry. Libraries were sequenced using the NovaSeq 6000. Preprocessing, quality control, and integration was performed on reads before subjecting them to cluster, differential expression, and pathway analyses. Result: Together, the samples were represented by 143,000 nuclei in 23 clusters encompassing all neuronal and glial populations in the hippocampus. We identified the expression of every RAS component, excluding renin, in the hippocampus. Notably, inhibitory and excitatory neurons had the most differentially expressed genes (DEGs) in 12‐month KI samples compared to controls. Additionally, 12‐month KI females had upregulated microglial C1q genes compared to males. Conclusion: We found expression of RAS genes in the hippocampus, which to our knowledge has not yet been characterized by single‐nucleus RNA sequencing. Furthermore, inhibitory neuron transcriptomes are the most affected in 12‐month KI mice, suggesting altered neuron communication that may lead to neuron loss. Additionally, we identified sex‐specific differences in 12‐month KI female microglia, which may explain the more aggressive gliosis seen in female KIs compared to males. Future directions include performing ingenuity pathway analysis to identify upstream regulators of DEGs and experimental perturbations to test our hypotheses. [ABSTRACT FROM AUTHOR]

Published
2024
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40. High-Frequency Core Loss Modeling Based on Knowledge-Aware Artificial Neural Network

Author

Deng, Junyun, Wang, Wenbo, Ning, Zhansheng, Venugopal, Prasanth, Popovic, Jelena, and Rietveld, Gert

Abstract

High-frequency core loss modeling plays a critical role in the magnetics design of power electronics. However, existing modeling tools fail to achieve both high speed and high precision. The conventional analytical approach enables fast estimations but performs poorly in accuracy. Magnetic loss models aided by loss maps feature high accuracy, but their model parameterization relies on large data. The emerging approach of artificial neural networks (ANNs) provides a promising alternative since it can achieve high speed and accuracy. However, conventional implementations of ANN require a large and accurate dataset for training, which is hard to achieve in magnetic loss modeling. To solve this problem, a knowledge-aware artificial neural network (KANN) is proposed that can achieve high accuracy with small training datasets. After introducing the principle of the proposed KANN, it is applied to high-frequency core loss modeling using the improved generalized Steinmetz equation as additional knowledge. To validate the performance of the proposed KANN-based design method for core loss modeling, it is applied to predict the losses of two ferrite cores in the frequency range of 50–450 kHz. The results show that the proposed method greatly outperforms present loss modeling approaches in accuracy and speed, requiring only a limited training dataset. An automatic loss modeling tool based on the new method is provided together with its open-source code.

Published
2024
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