Marta Susana Ferreira

Extreme value Birnbaum–Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

Tail dependence between order statistics

In this work, we introduce the s,k-extremal coefficients for studying the tail dependence between the s-th lower and k-th upper order statistics of a normalized random vector. If its margins have tail dependence then so do their order statistics, with the strength of bivariate tail dependence decreasing as two order statistics become farther apart. Some general properties are derived for these dependence measures which can be expressed via copulas of random vectors. Its relations with other extremal dependence measures used in the literature are discussed, such as multivariate tail dependence coefficients, the coefficient @h of tail dependence, coefficients based on tail dependence functions, the extremal coefficient @e, the multivariate extremal index and an extremal coefficient for min-stable distributions. Several examples are presented to illustrate the results, including multivariate exponential and multivariate Gumbel distributions widely used in applications.

Combined Influence of EGF+61G>A and TGFB+869T>C Functional Polymorphisms in Renal Cell Carcinoma Progression and Overall Survival: The Link to Plasma Circulating MiR-7 and MiR-221/222 Expression

The epidermal growth factor (EGF) is responsible for the activation of intracellular signal transducers that act on cell-cycle progression, cell motility, angiogenesis and inhibition of apoptosis. However, cells can block these effects activating opposite signaling pathways, such as the transforming growth factor beta 1 (TGFβ1) pathway. Thus changes in expression levels of EGF and TGFB1 in renal cells might modulate the renal cell carcinoma (RCC) development, in consequence of changes in regulatory elements of signaling networks such as the microRNAs (miRNAs). Our purpose was to investigate the synergic role of EGF+61G>A and TGFB1+869T>C polymorphisms in RCC development. Genetic polymorphisms were studied by allelic discrimination using real-time PCR in 133 RCC patients vs. 443 healthy individuals. The circulating EGF/EGFR-MAPK-related miR-7, miR-221 and miR-222 expression was analyzed by a quantitative real-time PCR in plasma from 22 RCC patients vs. 27 healthy individuals. The intermediate/high genetic proliferation profile patients carriers present a significantly reduced time-to-progression and a higher risk of an early relapse compared with the low genetic proliferation profile carriers (HR = 8.8, P = 0.038) with impact in a lower overall survival (Log rank test, P = 0.047). The RCC patients presented higher circulating expression levels of miR-7 than healthy individuals (6.1-fold increase, P<0.001). Moreover, the intermediate/high genetic proliferation profile carriers present an increase in expression levels of miR-7, miR-221 and miR-222 during the RCC development and this increase is not observed in low genetic proliferation profile (P<0.001, P = 0.004, P<0.001, respectively). The stimulus to angiogenesis, cell-cycle progression and tumoral cells invasion, through activation of EGFR/MAPK signaling pathway in intermediate/high proliferation profile carriers is associated with an early disease progression, resulting in a poor overall survival. We also demonstrated that the intermediate/high proliferation profile is an unfavorable prognostic factor of RCC and miR-7, miR-221 and miR-222 expressions may be useful phenotype biomarkers of EGFR/MAPK activation.

Plasmatic miR-210, miR-221 and miR-1233 profile: potential liquid biopsies candidates for renal cell carcinoma

Renal cell carcinoma (RCC) represents a challenge for clinicians since the nonexistence of screening and monitoring tests contributes to the fact that one-third of patients are diagnosed with metastatic disease and 20–40% of the remaining patients will also develop metastasis. Modern medicine is now trying to establish circulating biomolecules as the gold standard of biomarkers. Among the molecules that can be released from tumor cells we can find microRNAs. The aim of this study was to evaluate the applicability of cancer-related miR-210, miR-218, miR-221 and miR-1233 as prognostic biomarkers for RCC. Patients with higher levels of miR-210, miR-221 and miR-1233 presented a higher risk of specific death by RCC and a lower cancer-specific survival. The addition of miR-210, miR-221 and miR-1233 plasma levels information improved the capacity to predict death by cancer in 8, 4% when compared to the current variables used by clinicians. We also verified that hypoxia stimulates the release of miR-210 and miR-1233 from HKC-8, RCC-FG2 and 786-O cell lines. These results support the addition of circulating microRNAs as prognostic biomarkers for RCC.

An Analysis of a Heuristic Procedure to Evaluate Tail (in)dependence

Measuring tail dependence is an important issue in many applied sciences in order to quantify the risk of simultaneous extreme events. A usual measure is given by the tail dependence coefficient. The characteristics of events behave quite differently as these become more extreme, whereas we are in the class of asymptotic dependence or in the class of asymptotic independence. The literature has emphasized the asymptotic dependent class but wrongly infers that tail dependence will result in the overestimation of extreme value dependence and consequently of the risk. In this paper we analyze this issue through simulation based on a heuristic procedure.

Estimating the extremal index through local dependence

The extremal index is an important parameter in the characterization of extreme values of a stationary sequence. Our new estimation approach for this parameter is based on the extremal behavior under the local dependence condition D

Hypoxia and renal cell carcinoma: The influence of HIF1A+1772C/T functional genetic polymorphism on prognosis

^{(k)}

On tail dependence: A characterization for first-order max-autoregressive processes

(

Estimating the Extremal Coefficient: A Simulation Comparison of Methods

u_n

Study of the Electroreactivity of Amoxicillin on Carbon Nanotube-Supported Metal Electrodes

). We compare a process satisfying one of this hierarchy of increasingly weaker local mixing conditions with a process of cycles satisfying the D