No abstract

Long non-coding RNA metastasis associated with lung adenocarcinoma transcript 1 (MALAT1) contributes to chemotherapy resistance in some cancers, but the role of MALAT1 in sunitinib (SU) chemoresistance of carcinoma (RCC) is still unknown. In this study, MALAT1 expression in SUresistance tumor tissues and cells was tested by qRT-PCR. Then, CCK-8, Annexin V-FITC/PI, transwell, and Western blotting assays were used to evaluate cell viability and IC50, apoptosis, cell invasion, and resistance of SU-resistance RCC cells after transfected with small interfering RNA against MALAT1. Further, RNA pull-down and luciferase reporter assay were applied to investigate the underlying mechanism of MALAT1 in SU resistance. The results showed that MALAT1 expression was dramatically upregulated in SU-resistance RCC tissues and cell lines. Knockdown of MALAT1 inhibited proliferation, invasion, and SU chemoresistance, but induced apoptosis in RCC cells. The results of RNA pull-down and luciferase reporter assay indicated that MALAT1 could interact with miR-362-3p and miR-362-3p interact with RasGAP SH3-domain-Binding Protein 1 (G3BP1). Moreover, G3BP1 also played a role in SU chemoresistance of RCC cells, and MALAT1 could perform as a miR-362-3p sponge to modulate G3BP1 expression. Rescue experiments suggested that downregulation of miR-362-3p and overexpression of G3BP1 can reverse the SU chemosensitivity of MALAT1 knockdown in RCC cells. In conclusion, depletion of LncRNA MALAT1 inhibited SU chemoresistance through modulating G3BP1 via sponging miR-362-3p in RCC cells, suggesting that targeting MALAT1 may be a potential therapeutic strategy for SU-resistance RCC.

No abstract

Power quality problem, because of its various forms and occurrence frequency, has become one of the most critical challenges confronted by a power system. Meanwhile, the development of renewable energy has led to more demands for an integrated system that combines both merits of sustainable energy generation and power quality improvement. In this context, this paper discusses an integrated photovoltaic-unified power quality conditioner (PV-UPQC) and its control strategy. The system is composed of a series compensator, shunt compensator, dc-bus, and photovoltaic array, which conducts an integration of photovoltaic generation and power quality mitigation. The fuzzy adaptive PI controller and the improved Maximum Power Point Tracking (MPPT) technique are proposed to enhance the stability of dc-bus voltage, which is aimed at the power balance and steady operation of the whole system. Additionally, the coordinate control strategy is studied in order to ensure the normal operation and compensation performance of the system under severe voltage sag condition. In comparison to the existing PV-UPQC system, the proposed control method could improve the performance of dc-bus stability and the compensation ability. The dynamic behavior of the integrated system were verified by simulation in MATLAB and PLECS. Selected results are reported to show that the dc-bus voltage was stable and increased under severe situations, which validates the effectiveness of the proposed integrated PV-UPQC system and its control strategy.

No abstract

No abstract

No abstract

BackgroundModeling the dynamics of Alzheimer’s disease (AD) biomarkers over the entire continuum of AD progression is important, yet challenging due to limited resources to collect longitudinal biomarkers from the aging population with fully observed clinical spectrum of AD. This study proposed and applied a synchronized sigmoidal mixed‐effects model to characterize dynamics of longitudinal memory performance using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The model leveraged time to AD onset as the time scale and additionally allowed inclusion of participants without AD onset, drastically expanding future applications.MethodADNI participants with observed mild cognitive impairment (MCI) and/or AD onset (n = 312, mean (SD) baseline age 74.9 (6.44) years) were included (Table 1). A memory composite previously built in ADNI was leveraged for all analyses. A synchronized sigmoidal mixed‐effects model was constructed for dynamics of memory performance with parameters for initial memory level, magnitude of decline, and half‐life of decline. For participants with observed MCI but not AD onset, an additional parameter (t0 ) quantifying the time from MCI onset to AD was incorporated (Figure 1). We considered random effects for all parameters and allowed t0 to vary by age at MCI onset (nonlinearly), sex, apolipoprotein E (APOE)‐ε4 status and their interactions.ResultThe mean initial harmonized memory score is 0.24 (95% CI: 0.17‐0.32). The mean decline in the harmonized memory score is 1.53 (95% CI: 1.43‐1.64). The mean time when the harmonized memory score declined by half is 0.57 years before AD onset (95% CI: 0.32‐0.82). Female is associated with faster progression from MCI onset to AD (p = 0.002). Age at MCI onset is nonlinearly associated with MCI‐to‐AD progression (p < 0.001) and APOE‐ε4 status interacts with age at MCI onset on MCI‐to‐AD progression (p = 0.002) (Figure 2).ConclusionThe proposed synchronized sigmoidal mixed effect model can be used to characterize dynamics of AD biomarkers relative to AD onset using participants with and without AD onset. A model to estimate duration of MCI‐to‐AD progression can be simultaneously included for synchronization purpose, which identified gender, age at MCI onset and APOE‐ε4 status as factors associated with MCI‐to‐AD progression.