| 1. |
- Wang, Fei, et al.
(författare)
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Endothelial cell heterogeneity and microglia regulons revealed by a pig cell landscape at single-cell level
- 2022
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Pigs are valuable large animal models for biomedical and genetic research, but insights into the tissue- and cell-type-specific transcriptome and heterogeneity remain limited. By leveraging single-cell RNA sequencing, we generate a multiple-organ single-cell transcriptomic map containing over 200,000 pig cells from 20 tissues/organs. We comprehensively characterize the heterogeneity of cells in tissues and identify 234 cell clusters, representing 58 major cell types. In-depth integrative analysis of endothelial cells reveals a high degree of heterogeneity. We identify several functionally distinct endothelial cell phenotypes, including an endothelial to mesenchymal transition subtype in adipose tissues. Intercellular communication analysis predicts tissue- and cell type-specific crosstalk between endothelial cells and other cell types through the VEGF, PDGF, TGF-beta, and BMP pathways. Regulon analysis of single-cell transcriptome of microglia in pig and 12 other species further identifies MEF2C as an evolutionally conserved regulon in the microglia. Our work describes the landscape of single-cell transcriptomes within diverse pig organs and identifies the heterogeneity of endothelial cells and evolutionally conserved regulon in microglia.
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| 2. |
- Ahearn, Thomas U., et al.
(författare)
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Common variants in breast cancer risk loci predispose to distinct tumor subtypes
- 2022
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Ingår i: Breast Cancer Research. - : Springer Nature. - 1465-5411 .- 1465-542X. ; 24:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundGenome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear.MethodsAmong 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes.ResultsEighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions.ConclusionThis report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.
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| 3. |
- Ju, Ying, et al.
(författare)
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DRL-based Beam Allocation in Relay-aided Multi-user MmWave Vehicular Networks
- 2022
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Ingår i: IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Millimeter wave (mmWave) communication can realize high transmission rates in vehicular networks. Nevertheless, severe blocking effects and high mobility of vehicles would seriously affect downlink services for vehicles. To ensure communication quality and stability, this paper jointly explores beam allocation and relay selection in mmWave vehicular networks from the perspective of artificial intelligence-driven model. We utilize queuing theory to simulate dynamic distributions of vehicles and firstly propose a deep reinforcement learning (DRL) based joint beam allocation and relay selection scheme to mitigate the blocking effects and optimize the total communication capacity. When the expected downlink is blocked, mmWave base station (mmBS) can select appropriate idle vehicles as the relay nodes for service. Besides, we set the capacity threshold when designing the scheme to guarantee each target vehicle can obtain the ideal service. Through proper training, mmBS can intelligently find an optimal solution for the constantly updated vehicular networks based on the location of vehicles. Simulation results demonstrate the effectiveness of our scheme, which can restrain the transmission outage caused by random blockage and improve the total communication capacity of vehicular networks.
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