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  • 1
    Online-Ressource
    Online-Ressource
    Wiley ; 2020
    In:  Journal of Cellular Biochemistry Vol. 121, No. 12 ( 2020-12), p. 4908-4921
    In: Journal of Cellular Biochemistry, Wiley, Vol. 121, No. 12 ( 2020-12), p. 4908-4921
    Kurzfassung: Endometrial cancer (EC) is one of the most common malignancies in the female genital system, characterized by high mortality and recurrence rates. This study attempted to screen key genes and potential prognostic biomarkers for EC using bioinformatics analysis. Twenty‐seven normal endometrial tissues and 135 EC samples were collected from four Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein‐protein interaction (PPI) network. Finally, we assessed the prognostic values and molecular mechanism of the potential prognostic genes using the Kaplan‐Meier curve and Gene Set Enrichment Analysis (GSEA). As a result, 28 upregulated and 94 downregulated genes were determined after gene integration of these four GEO data sets. Gene Ontology analysis indicated that DEGs were mainly involved in transcriptional regulation and cell proliferation. The Kyoto Encyclopedia of Gene and Genome pathway analysis primarily related to transcriptional misregulation and apoptosis. Moreover, the PPI analysis revealed 10 hub genes (JUN, UBE2I, GATA2, WT1, PIAS1, FOXL2, RUNXI, EZR, TCF4, and NR2F2) with a high degree of connectivity, among them, the expression tendency of nine genes except UBE2I were consistent with messenger RNA level from The Cancer Genome Atlas data. Furthermore, only FOXL2, TCF4, and NR2F2 were significantly correlated with prognosis of EC patients, and their low expression associated biological pathways were enriched in the cell cycle and fatty acid metabolism. In conclusion, this study identified three key genes as biomarkers and potential therapeutic targets of EC on the basis of integrated bioinformatics analysis. The findings will improve our comprehension of the molecular mechanisms underlying the pathogenesis and prognosis of EC.
    Materialart: Online-Ressource
    ISSN: 0730-2312 , 1097-4644
    URL: Issue
    Sprache: Englisch
    Verlag: Wiley
    Publikationsdatum: 2020
    ZDB Id: 1479976-5
    SSG: 12
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    In: Frontiers in Endocrinology, Frontiers Media SA, Vol. 13 ( 2022-12-1)
    Kurzfassung: Chronic endometritis (CE) contributes to impaired endometrial receptivity and is closely associated with poor in vitro fertilization (IVF) outcomes. However, the mechanisms underlying CE are unclear. Here, we investigated the role of the hypoxic microenvironment and endometrial vascularization in the peri-implantation endometrium of infertile women with CE. Methods This retrospective study involved 15 fertile women and 77 infertile patients diagnosed with CE based on CD138+ ≥1/10 high-power fields (HPFs). The CE patients were divided into Group 1 (CD138+ 1–4/10 HPFs, 53 cases) and Group 2 (CD138+ ≥5/10 HPFs, 24 cases). The expression levels of hypoxia-inducible factor 1α (HIF1α), vascular endothelial growth factor A (VEGFA), and vascular endothelial growth factor receptor 2 (VEGFR2) in peri-implantation endometrium were assessed by qRT-PCR and western blot analyses. Spatial levels of HIF1α, VEGFA, and VEGFR2 in various endometrial compartments was determined using immunohistochemistry and H -score analysis. Microvascular density (MVD) was determined using CD34 staining and scored using Image J. Finally, we used qRT-PCR to assess changes in the expression of HIF1α, VEGFA, and VEGFR2 in CE patients after treatment with first-line antibiotics. Result(s) Relative to Group 1 and control group, during the implantation window, protein and mRNA levels of HIF1α, VEGFA, and VEGFR2 were markedly high in Group 2 ( P & lt;0.05). H -score analysis showed that HIF1α, VEGFA, and VEGFR2 in the luminal, glandular epithelium, and stromal compartments were markedly elevated in Group 2, comparing to control group and Group 1 ( P & lt;0.05). Moreover, markedly elevated MVD levels were observed in Group 2. Notably, the above indexes did not differ significantly in the control group versus Group 1. Treatment with antibiotics significantly suppressed the endometrial HIF1α and VEGFA levels in CE-cured patients. Conclusion(s) Here, we for the first time report the upregulation of HIF1α, VEGFA, and VEGFR2, as well as excessive endometrial vascularization in the peri-implantation endometrium of CE patients. Our findings offer new insights into reduced endometrial receptivity in CE-associated infertility.
    Materialart: Online-Ressource
    ISSN: 1664-2392
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2022
    ZDB Id: 2592084-4
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    Online-Ressource
    Online-Ressource
    Elsevier BV ; 2021
    In:  Transportation Research Part D: Transport and Environment Vol. 93 ( 2021-04), p. 102768-
    In: Transportation Research Part D: Transport and Environment, Elsevier BV, Vol. 93 ( 2021-04), p. 102768-
    Materialart: Online-Ressource
    ISSN: 1361-9209
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2021
    ZDB Id: 2019964-8
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
    Online-Ressource
    Online-Ressource
    Elsevier BV ; 2021
    In:  Earth-Science Reviews Vol. 222 ( 2021-11), p. 103828-
    In: Earth-Science Reviews, Elsevier BV, Vol. 222 ( 2021-11), p. 103828-
    Materialart: Online-Ressource
    ISSN: 0012-8252
    RVK:
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2021
    ZDB Id: 1792-9
    ZDB Id: 2012642-6
    SSG: 13
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
    Online-Ressource
    Online-Ressource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2024
    In:  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 17 ( 2024), p. 3425-3437
    In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Institute of Electrical and Electronics Engineers (IEEE), Vol. 17 ( 2024), p. 3425-3437
    Materialart: Online-Ressource
    ISSN: 1939-1404 , 2151-1535
    Sprache: Unbekannt
    Verlag: Institute of Electrical and Electronics Engineers (IEEE)
    Publikationsdatum: 2024
    ZDB Id: 2457423-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    Maximum Academic Press ; 2024
    In:  Digital Transportation and Safety Vol. 0, No. 0 ( 2024), p. 1-17
    In: Digital Transportation and Safety, Maximum Academic Press, Vol. 0, No. 0 ( 2024), p. 1-17
    Materialart: Online-Ressource
    ISSN: 2837-7842
    Sprache: Unbekannt
    Verlag: Maximum Academic Press
    Publikationsdatum: 2024
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
    Online-Ressource
    Online-Ressource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE Transactions on Intelligent Transportation Systems Vol. 23, No. 6 ( 2022-6), p. 5765-5776
    In: IEEE Transactions on Intelligent Transportation Systems, Institute of Electrical and Electronics Engineers (IEEE), Vol. 23, No. 6 ( 2022-6), p. 5765-5776
    Materialart: Online-Ressource
    ISSN: 1524-9050 , 1558-0016
    Sprache: Unbekannt
    Verlag: Institute of Electrical and Electronics Engineers (IEEE)
    Publikationsdatum: 2022
    ZDB Id: 2034300-0
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
    Online-Ressource
    Online-Ressource
    Elsevier BV ; 2021
    In:  Agricultural and Forest Meteorology Vol. 310 ( 2021-11), p. 108657-
    In: Agricultural and Forest Meteorology, Elsevier BV, Vol. 310 ( 2021-11), p. 108657-
    Materialart: Online-Ressource
    ISSN: 0168-1923
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2021
    ZDB Id: 2012165-9
    SSG: 23
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
    Online-Ressource
    Online-Ressource
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 13 ( 2023-07-05), p. 3410-
    In: Remote Sensing, MDPI AG, Vol. 15, No. 13 ( 2023-07-05), p. 3410-
    Kurzfassung: Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas.
    Materialart: Online-Ressource
    ISSN: 2072-4292
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2023
    ZDB Id: 2513863-7
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
    Online-Ressource
    Online-Ressource
    Copernicus GmbH ; 2022
    In:  Hydrology and Earth System Sciences Vol. 26, No. 11 ( 2022-06-14), p. 2923-2938
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 26, No. 11 ( 2022-06-14), p. 2923-2938
    Kurzfassung: Abstract. Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation forecasting techniques could complement numerical prediction, such as precipitation nowcasting, monthly precipitation projection and extreme precipitation event identification. In data-driven precipitation forecasting, the predictive uncertainty arises mainly from data and model uncertainties. Current deep learning forecasting methods could model the parametric uncertainty by random sampling from the parameters. However, the data uncertainty is usually ignored in the forecasting process and the derivation of predictive uncertainty is incomplete. In this study, the input data uncertainty, target data uncertainty and model uncertainty are jointly modeled in a deep learning precipitation forecasting framework to estimate the predictive uncertainty. Specifically, the data uncertainty is estimated a priori and the input uncertainty is propagated forward through model weights according to the law of error propagation. The model uncertainty is considered by sampling from the parameters and is coupled with input and target data uncertainties in the objective function during the training process. Finally, the predictive uncertainty is produced by propagating the input uncertainty in the testing process. The experimental results indicate that the proposed joint uncertainty modeling framework for precipitation forecasting exhibits better forecasting accuracy (improving RMSE by 1 %–2 % and R2 by 1 %–7 % on average) relative to several existing methods, and could reduce the predictive uncertainty by ∼28 % relative to the approach of Loquercio et al. (2020). The incorporation of data uncertainty in the objective function changes the distributions of model weights of the forecasting model and the proposed method can slightly smooth the model weights, leading to the reduction of predictive uncertainty relative to the method of Loquercio et al. (2020). The predictive accuracy is improved in the proposed method by incorporating the target data uncertainty and reducing the forecasting error of extreme precipitation. The developed joint uncertainty modeling method can be regarded as a general uncertainty modeling approach to estimate predictive uncertainty from data and model in forecasting applications.
    Materialart: Online-Ressource
    ISSN: 1607-7938
    Sprache: Englisch
    Verlag: Copernicus GmbH
    Publikationsdatum: 2022
    ZDB Id: 2100610-6
    Standort Signatur Einschränkungen Verfügbarkeit
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