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"Cai, Juan"
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Gradient boosting for extreme quantile regression
Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in such cases since data in the tail region are too scarce. Extreme value theory is used for extrapolation beyond the range of observed values and estimation of conditional extreme quantiles. Based on the peaks-over-threshold approach, the conditional distribution above a high threshold is approximated by a generalized Pareto distribution with covariate dependent parameters. We propose a gradient boosting procedure to estimate a conditional generalized Pareto distribution by minimizing its deviance. Cross-validation is used for the choice of tuning parameters such as the number of trees and the tree depths. We discuss diagnostic plots such as variable importance and partial dependence plots, which help to interpret the fitted models. In simulation studies we show that our gradient boosting procedure outperforms classical methods from quantile regression and extreme value theory, especially for high-dimensional predictor spaces and complex parameter response surfaces. An application to statistical post-processing of weather forecasts with precipitation data in the Netherlands is proposed.
Journal Article
Estimation of the marginal expected shortfall under asymptotic independence
We study the asymptotic behavior of themarginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby.
Journal Article
Improving precipitation forecasts using extreme quantile regression
Aiming to estimate extreme precipitation forecast quantiles, we propose a nonparametric regression model that features a constant extreme value index. Using local linear quantile regression and an extrapolation technique from extreme value theory, we develop an estimator for conditional quantiles corresponding to extreme high probability levels. We establish uniform consistency and asymptotic normality of the estimators. In a simulation study, we examine the performance of our estimator on finite samples in comparison with a method assuming linear quantiles. On a precipitation data set in the Netherlands, these estimators have greater predictive skill compared to the upper member of ensemble forecasts provided by a numerical weather prediction model.
Journal Article
M6A-mediated upregulation of LINC00958 increases lipogenesis and acts as a nanotherapeutic target in hepatocellular carcinoma
Background
Long non-coding RNAs (lncRNAs) possess significant regulatory functions in multiple biological and pathological processes, especially in cancer. Dysregulated lncRNAs in hepatocellular carcinoma (HCC) and their therapeutic applications remain unclear.
Methods
Differentially expressed lncRNA profile in HCC was constructed using TCGA data. LINC00958 expression level was examined in HCC cell lines and tissues. Univariate and multivariate analyses were performed to demonstrate the prognostic value of LINC00958. Loss-of-function and gain-of-function experiments were used to assess the effects of LINC00958 on cell proliferation, motility, and lipogenesis. Patient-derived xenograft model was established for in vivo experiments. RNA immunoprecipitation, dual luciferase reporter, biotin-labeled miRNA pull-down, fluorescence in situ hybridization, and RNA sequencing assays were performed to elucidate the underlying molecular mechanisms. We developed a PLGA-based nanoplatform encapsulating LINC00958 siRNA and evaluated its superiority for systemic administration.
Results
We identified a lipogenesis-related lncRNA, LINC00958, whose expression was upregulated in HCC cell lines and tissues. High LINC00958 level independently predicted poor overall survival. Functional assays showed that LINC00958 aggravated HCC malignant phenotypes in vitro and in vivo. Mechanistically, LINC00958 sponged miR-3619-5p to upregulate hepatoma-derived growth factor (HDGF) expression, thereby facilitating HCC lipogenesis and progression. METTL3-mediated N
6
-methyladenosine modification led to LINC00958 upregulation through stabilizing its RNA transcript. A PLGA-based nanoplatform loaded with si-LINC00958 was developed for HCC systemic administration. This novel drug delivery system was controlled release, tumor targeting, safe, and presented satisfactory antitumor efficacy.
Conclusions
Our results delineate the clinical significance of LINC00958 in HCC and the regulatory mechanisms involved in HCC lipogenesis and progression, providing a novel prognostic indicator and promising nanotherapeutic target.
Journal Article
Mitophagy in Acute Kidney Injury and Kidney Repair
Acute kidney injury (AKI) is a major kidney disease characterized by rapid decline of renal function. Besides its acute consequence of high mortality, AKI has recently been recognized as an independent risk factor for chronic kidney disease (CKD). Maladaptive or incomplete repair of renal tubules after severe or episodic AKI leads to renal fibrosis and, eventually, CKD. Recent studies highlight a key role of mitochondrial pathology in AKI development and abnormal kidney repair after AKI. As such, timely elimination of damaged mitochondria in renal tubular cells represents an important quality control mechanism for cell homeostasis and survival during kidney injury and repair. Mitophagy is a selective form of autophagy that selectively removes redundant or damaged mitochondria. Here, we summarize our recent understanding on the molecular mechanisms of mitophagy, discuss the role of mitophagy in AKI development and kidney repair after AKI, and present future research directions and therapeutic potential.
Journal Article
Estimation of the marginal expected shortfall: the mean when a related variable is extreme
Denote the loss return on the equity of a financial institution as X and that of the entire market as Y. For a given very small value of p>0, the marginal expected shortfall (MES) is defined as E{X|Y>QY(1−p)}, where QY(1−p) is the (1−p)th quantile of the distribution of Y. The MES is an important factor when measuring the systemic risk of financial institutions. For a wide non‐parametric class of bivariate distributions, we construct an estimator of the MES and establish the asymptotic normality of the estimator when p↓0, as the sample size n→∞. Since we are in particular interested in the case p=O(1/n), we use extreme value techniques for deriving the estimator and its asymptotic behaviour. The finite sample performance of the estimator and the relevance of the limit theorem are shown in a detailed simulation study. We also apply our method to estimate the MES of three large US investment banks.
Journal Article
Four New Insecticidal Xanthene Derivatives from the Mangrove-Derived Fungus Penicillium sp. JY246
Four new xanthene derivatives, penicixanthenes A–D (1–4), and one known compound 5 were isolated from a marine mangrove endophytic fungus Penicillium sp. JY246 that was obtained from the stem of Ceriops tagal. Their structures were determined by detailed NMR, MS spectroscopic data, modified Mosher’s method, and calculated electronic circular dichroism data. All of the isolated compounds were examined for insecticidal activity. Compounds 2 and 3 showed growth inhibition activity against newly hatched larvae of Helicoverpa armigera Hubner with the IC50 values 100 and 200 μg/mL, respectively, and compounds 1, 3, and 4 showed insecticidal activity against newly hatched larvae of Culex quinquefasciatus with LC50 values of 38.5 (±1.16), 11.6 (±0.58), and 20.5 (±1) μg/mL, respectively. The four xanthene derivatives have the potential to be developed as new biopesticides.
Journal Article
Endocytosis-mediated entry of a caterpillar effector into plants is countered by Jasmonate
Insects and pathogens release effectors into plant cells to weaken the host defense or immune response. While the imports of some bacterial and fungal effectors into plants have been previously characterized, the mechanisms of how caterpillar effectors enter plant cells remain a mystery. Using live cell imaging and real-time protein tracking, we show that HARP1, an effector from the oral secretions of cotton bollworm (
Helicoverpa armigera
), enters plant cells via protein-mediated endocytosis. The entry of HARP1 into a plant cell depends on its interaction with vesicle trafficking components including CTL1, PATL2, and TET8. The plant defense hormone jasmonate (JA) restricts HARP1 import by inhibiting endocytosis and HARP1 loading into endosomes. Combined with the previous report that HARP1 inhibits JA signaling output in host plants, it unveils that the effector and JA establish a defense and counter-defense loop reflecting the robust arms race between plants and insects.
The authors find that cotton bollworms secrete effectors into plant to weaken JA signaling via host endocytosis system while JA inhibits endocytosis to restrict effector imports. The defense and counter-defense loop between effector and JA reflects a robust arms race between plant and insect.
Journal Article