Statistical inference on D(d)(un)D^{(d)}(u_n) condition and estimation of the Extremal Index

Clustering of extreme events often has a destructive societal impact. The extremal index, a number in the unit interval, is a key parameter in modelling the clustering of extremes. While studying the extremal index, a local dependence condition referred to as $D^{(d)}(u_n)$ condition, is often assumed. In this paper, we develop a hypothesis test for $D^{(d)}(u_n)$ condition based on asymptotic results. Further we construct an estimator of the extremal index making use of the inference procedure on $D^{(d)}(u_n)$ condition and we prove this estimator is asymptotically normal. The finite sample performances of the hypothesis test and the estimation are examined in a simulation study, where we consider models fulfilling $D^{(d)}(u_n)$ condition as well as models that violate the condition. In a simple case study, our statistical procedure shows that daily temperature in summer shares a common clustering structure of extreme values based on the data observed in three weather stations in the Netherlands, Belgium and Spain

Parametric and non-parametric estimation of extreme earthquake event: the joint tail inference for mainshocks and aftershocks

In an earthquake event, the combination of a strong mainshock and damaging aftershocks is often the cause of severe structural damages and/or high death tolls. The objective of this paper is to provide estimation for the probability of such extreme events where the mainshock and the largest aftershocks exceed certain thresholds. Two approaches are illustrated and compared -- a parametric approach based on previously observed stochastic laws in earthquake data, and a non-parametric approach based on bivariate extreme value theory. We analyze the earthquake data from the North Anatolian Fault Zone (NAFZ) in Turkey during 1965-2018 and show that the two approaches provide unifying results

miR-1258: a novel microRNA that controls TMPRSS4 expression is associated with malignant progression of papillary thyroid carcinoma

Background: MicroRNA-1258 (miR-1258) has been shown to play an anti-cancer role in a variety of cancers, but its relationship with papillary thyroid cancer (PTC) has not been reported. The emphasis of this research was to reveal the biological function of miR-1258 in PTC and its potential mechanisms. Material and methods: We measured miR-1258 expression in PTC cells and the transfection efficiency of miR-1258 mimic and miR-1258 inhibitor by quantitative real-time PCR (qRT-PCR) assay. Cell Counting Kit-8 assay (CCK8) and Transwell experiments were conducted to examine the influences of altering miR-1258 expression on the viability, migration, and invasion of PTC cells. Bioinformatics prediction and dual-luciferase experiment were performed to verify the target gene of miR-1258. Finally, we carried out a rescue assay to verify whether the regulation of miR-1258 on the biological behaviour of PTC cells needs to be achieved by regulating TMPRSS4. Results: The outcomes revealed that miR-1258 was lowly expressed in PTC cell lines and miR-1258 showed the lowest expression in KTC-1 and the highest expression in B-CPAP among all tested PTC cell lines. Overexpression of miR-1258 inhibited KTC-1 cell viability and ability to migrate and invade, whereas inhibition of miR-1258 in B-CPAP cells has the opposite effect. Furthermore, we affirmed that miR-1258 can directly target TMPRSS4, and miR-1258 can reduce the biological malignant behaviour of PTC cells via regulation of TMPRSS4. Conclusion: Taken together, our research raised the possibility that miR-1258 was an anti-oncogene, which exerts its anti-cancer function by targeting TMPRSS4. Hence, it may be possible to treat PTC by targeting the miR-1258/TMPRSS4 axis in the future.