Frequency of TERT promoter mutations in human cancers

Reactivation of telomerase has been implicated in human tumorigenesis, but the underlying mechanisms remain poorly understood. Here we report the presence of recurrent somatic mutations in the TERT promoter in cancers of the central nervous system (43%), bladder (59%), thyroid (follicular cell-derived, 10%) and skin (melanoma, 29%). In thyroid cancers, the presence of TERT promoter mutations (when occurring together with BRAF mutations) is significantly associated with higher TERT mRNA expression, and in glioblastoma we find a trend for increased telomerase expression in cases harbouring TERT promoter mutations. Both in thyroid cancers and glioblastoma, TERT promoter mutations are significantly associated with older age of the patients. Our results show that TERT promoter mutations are relatively frequent in specific types of human cancers, where they lead to enhanced expression of telomerase.

Fractional derivatives: probability interpretation and frequency response of rational approximations

The theory of fractional calculus (FC) is a useful mathematical tool in many applied sciences. Nevertheless, only in the last decades researchers were motivated for the adoption of the FC concepts. There are several reasons for this state of affairs, namely the co-existence of different definitions and interpretations, and the necessity of approximation methods for the real time calculation of fractional derivatives (FDs). In a first part, this paper introduces a probabilistic interpretation of the fractional derivative based on the Grünwald-Letnikov definition. In a second part, the calculation of fractional derivatives through Padé fraction approximations is analyzed. It is observed that the probabilistic interpretation and the frequency response of fraction approximations of FDs reveal a clear correlation between both concepts.

Low frequency of TERT promoter mutations in gastrointestinal stromal tumors (GISTs)

Somatic mutations in the promoter region of telomerase reverse transcriptase (TERT) gene, mainly at positions c.-124 and c.-146 bp, are frequent in several human cancers; yet its presence in gastrointestinal stromal tumor (GIST) has not been reported to date. Herein, we searched for the presence and clinicopathological association of TERT promoter mutations in genomic DNA from 130 bona fide GISTs. We found TERT promoter mutations in 3.8% (5/130) of GISTs. The c.-124C>T mutation was the most common event, present in 2.3% (3/130), and the c.-146C>T mutation in 1.5% (2/130) of GISTs. No significant association was observed between TERT promoter mutation and patient's clinicopathological features. The present study establishes the low frequency (4%) of TERT promoter mutations in GISTs. Further studies are required to confirm our findings and to elucidate the hypothetical biological and clinical impact of TERT promoter mutation in GIST pathogenesis.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.