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PurposePatients with Fanconi anaemia (FA), a rare DNA repair genetic disease, exhibit chromosome fragility, bone marrow failure, malformations and cancer susceptibility. FA molecular diagnosis is challenging since FA is caused by point mutations and large deletions in 22 genes following three heritability patterns. To optimise FA patients’ characterisation, we developed a simplified but effective methodology based on whole exome sequencing (WES) and functional studies.Methods68 patients with FA were analysed by commercial WES services. Copy number variations were evaluated by sequencing data analysis with RStudio. To test FANCA missense variants, wt FANCA cDNA was cloned and variants were introduced by site-directed mutagenesis. Vectors were then tested for their ability to complement DNA repair defects of a FANCA-KO human cell line generated by TALEN technologies.ResultsWe identified 93.3% of mutated alleles including large deletions. We determined the pathogenicity of three FANCA missense variants and demonstrated that two FANCA variants reported in mutations databases as ‘affecting functions’ are SNPs. Deep analysis of sequencing data revealed patients’ true mutations, highlighting the importance of functional analysis. In one patient, no pathogenic variant could be identified in any of the 22 known FA genes, and in seven patients, only one deleterious variant could be identified (three patients each with FANCA and FANCD2 and one patient with FANCE mutations)ConclusionWES and proper bioinformatics analysis are sufficient to effectively characterise patients with FA regardless of the rarity of their complementation group, type of mutations, mosaic condition and DNA source.

In the context of real-time detection of transient signals, a likelihood ratio (LR) test is evaluated at every sampling interval. Performing the LR tests at a lower rate reduces significantly the computational complexity of the detection algorithm. However, in general, this simplification also leads to a strong degradation of the detector performance. For example, with a small shift error, an arriving transient may be in quadrature with its model. This degradation is particularly noticeable when the signals to detect are deterministic and sampled at a frequency close to the Nyquist rate. This letter proposes a method to overcome this limitation by using locally stationary models of the signals to detect. The resulting detectors are robust to shift errors and computationally efficient.

In a previous paper W. Gardner (see IEEE Trans. Inform. Theory, vol.IT-18, p.808-9, 1972) developed a sampling theorem for nonstationary random processes, under the condition that the two-dimensional (2-D) power spectrum (2DPS) of the process has compact support. In this letter, it is shown that, for L/sup 2/(R) processes, only a one-dimensional (1-D) restriction on the marginal along time of the time-frequency distribution is necessary to guarantee the compactness of the 2DPS in the 2-D plane. As a direct consequence, it is observed that under mild conditions, a nonstationary autocorrelation function of a bandpass L/sup 2/(R) process is nearly stationary in small time intervals, The influence of this result in real-time detection of nonstationary stochastic signals is discussed.

Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein-protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.

This paper studies the application of Wavelet Packet Transform in identifying harmonics in both the voltage and current waveforms of AC electrical arc furnaces. It takes into account the special behavior of this type of devices in terms of load that leads to voltage and current fluctuations, which usually are a source of interharmonics. Possible inaccuracy in harmonics identifying process in applying IEC criterion using the FFT is studied and the results are compared with those obtained by applying the wavelet transform. The study was conducted on both simulated signals and voltage and current waveforms in the meltdown arc furnace of a steel mill.

Local stationarity of an L/sup 2/(/spl Ropf/) bandpass random process reflects in specific regions of either the frequency plane of its 2 dimensional power spectrum or the time-frequency plane of its Wigner distribution. The paper addresses the problem of estimating from data a covariance matrix that satisfies the constraint of being locally stationary. We also show, with a real-data case study, the improvement in performance achieved by using locally stationary covariance matrices in the development of low cost quadratic detectors.

The paper presents a wavelet transform based frequency classifier for stochastic bandpass transient signals. Using compactly supported bases of wavelets, continuous nonstationary bandpass processes can be described by reduced dimension discrete time signal representations. We develop an optimization procedure to adapt both the sampling frequency and the discrete time wavelet filters to the continuous classes of signals involved. When compared to the classical Bayesian structures, the proposed receiver strongly reduces the required computational load. The receiver performance is accessed by Monte Carlo simulation, the probabilities of detection for each signal class, and the probability of false alarm, being computed.

In underwater acoustics, the modeling of impulsive noise ambients by symmetric-/spl alpha/-stable laws is motivated by the generalized central limit theorem. However, detection of stochastic signals under such additive noise is a difficult task to implement, due to the lack of a closed-form expression of the a-posteriori probability density function. We present a suboptimal detector for Gaussian bandpass transients in impulsive noise that uses a nonlinear, memoryless prefilter followed by a discrete wavelet transform. The resulting signals present a Gaussian-like behavior and the decision is achieved by the comparison of a quadratic likelihood ratio with a threshold. The tuning of the nonlinearity parameter is performed either by looking at the receiver operating characteristic or using the Chernoff distance, that, although resulting in an approximate solution, is easier to compute. Simulation results are presented by Monte-Carlo simulation.

Compactly supported bases of wavelets with bandpass Fourier transform give time-domain localization of short-duration stochastic signals over large observation intervals. Using the wavelet transform, we develop an estimator of the arrival time of a transient stochastic signal that significantly reduces the computational load of the classical maximum likelihood estimator. The estimator relies on a simplified model of the received delayed signal. Under low signal to noise ratio, Monte Carlo simulation experiments are conducted with the proposed algorithm.

This paper shows how the sampling theorem relates with the variations along time of the second order statistics of L2(ℝ) nonstationary processes. As a consequence, and mainly due to the positive semidefiniteness of autocorrelation functions, it is possible to conclude if a nonstationary process is locally stationary (i.e., if its second order statistics vary slowly along time) by the direct observation of its 2-dimension power spectrum or its Wigner distribution. A simple example illustrates how two different strategies for the estimation of autocorrelation functions from a small number of data can lead to opposite results in terms of local stationarity.

Vaidyanathan (1999) and Ferreira (1999) reported on nonlinear systems with exponential eigenfunctions. In this letter, we introduce a larger class of such systems, generated from a special family of exponential-input/constant-output operators that capture exponential signals from general inputs. In particular, we extend the classes of systems with exponential eigenfunctions defined in Vaidyanathan and Ferreira to include causal nonhomogeneous time-varying systems.

Meneven, an affiliate of Petroleos de Venezuela, S.A., has been conducting a natural-gas-injection project in a heavy-oil reservoir (14 to 20 API), located in E. Venezuela, immediately north of the Orinoco oil belt, since 1963. Laboratory tests were made to investigate recovery mechanisms, and a black-oil simulator was used to match performance history and predict future performance under continued gas injection. According to the results obtained, the high viscosity (low API gravity) of a reservoir crude is not necessarily a limiting factor in the successful application of conventional gas injection. This unorthodox scheme has proven to be quite successful even in a relatively high oil-gas viscosity ratio reservoir, where cumulative recovery has been 32% of original oil-in-place (OOIP), and final recovery is estimated at 45% of OOIP.