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This work proposes a novel scheme for speckle suppression on medical images acquired by ultrasound sensors. The proposed method is based on the block matching procedure by using mutual information as a similarity measure in grouping patches in a clustered area, originating a new despeckling method that integrates the statistical properties of an image and its texture for creating 3D groups in the BM3D scheme. For this purpose, the segmentation of ultrasound images is carried out considering superpixels and a variation of the local binary patterns algorithm to improve the performance of the block matching procedure. The 3D groups are modeled in terms of grouped tensors and despekled with singular value decomposition. Moreover, a variant of the bilateral filter is used as a post-processing step to recover and enhance edges' quality. Experimental results have demonstrated that the designed framework guarantees a good despeckling performance in ultrasound images according to the objective quality criteria commonly used in literature and via visual perception.

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This work investigates the impact of the Covid-19 outbreak on crude oil market efficiency. The approach is based on the singular value decomposition (SVD) entropy. Iso-distributional surrogate data test was used to contrast the results against random patterns, and phase randomization based on Fourier transform was used to assess nonlinearities. The analysis considered the WTI market and focused on the Covid-19 pandemic period January 2020-November 2021 and contrasted with the long preceding period from January 2000 to date. It was found that the crude oil market was informationally efficient most of the time with small sporadic deviations from efficiency in the pre-Covid-19 years. The Covid-19 period exhibited the largest deviations from efficiency, mainly in the first months of the outbreak, accompanied by a marked reduction of nonlinear components. The analysis was conducted for different scales, and the results showed that the deviations from efficiency were more pronounced for quarterly scales. For the sake of comparison, the analysis was also carried out on the trading volume dynamics and the results showed that the market activity is not fully random. The dynamics of the trading volume exhibited significant deviations from the randomness behavior when the crude oil market was efficient, and a behavior that was consistent with nonlinear patterns. The opposite behavior was noted for stages when the crude oil market showed strong deviations from efficiency. Overall, the findings of this study suggest an increasing opportunity for crude oil price predictions and abnormal returns during the Covid-19 pandemic.

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This paper describes strategies to reduce the possible effect of outliers on the quality of imputations produced by a method that uses a mixture of two least squares techniques: regression and lower rank approximation of a matrix. To avoid the influence of discrepant data and maintain the computational speed of the original scheme, pre-processing options were explored before applying the imputation method. The first proposal is to previously use a robust singular value decomposition, the second is to detect outliers and then treat the potential outliers as missing. To evaluate the proposed methods, a cross-validation study was carried out on ten complete matrices of real data from multi-environment trials. The imputations were compared with the original data using three statistics: a measure of goodness of fit, the squared cosine between matrices and the prediction error. The results show that the original method should be replaced by one of the options presented here because outliers can cause low quality imputations or convergence problems.•The imputation algorithm based on Gabriel's cross-validation method uses two least squares techniques that can be affected by the presence of outliers. The inclusion of a robust singular value decomposition allows both to robustify the procedure and to detect outliers and consider them later as missing. These forms of pre-processing ensure that the algorithm performs well on any dataset that has a matrix form with suspected contamination.

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Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data.

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Perhaps the most important recent advance in species delimitation has been the development of model-based approaches to objectively diagnose species diversity from genetic data. Additionally, the growing accessibility of next-generation sequence data sets provides powerful insights into genome-wide patterns of divergence during speciation. However, applying complex models to large data sets is time-consuming and computationally costly, requiring careful consideration of the influence of both individual and population sampling, as well as the number and informativeness of loci on species delimitation conclusions. Here, we investigated how locus number and information content affect species delimitation results for an endangered Mexican salamander species, Ambystoma ordinarium. We compared results for an eight-locus, 137-individual data set and an 89-locus, seven-individual data set. For both data sets, we used species discovery methods to define delimitation models and species validation methods to rigorously test these hypotheses. We also used integrated demographic model selection tools to choose among delimitation models, while accounting for gene flow. Our results indicate that while cryptic lineages may be delimited with relatively few loci, sampling larger numbers of loci may be required to ensure that enough informative loci are available to accurately identify and validate shallow-scale divergences. These analyses highlight the importance of striking a balance between dense sampling of loci and individuals, particularly in shallowly diverged lineages. They also suggest the presence of a currently unrecognized, endangered species in the western part of A. ordinarium's range.

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High-resolution (13)C solid-state NMR stands out as one of the most promising techniques to solve the structure of insoluble proteins featuring biological and technological importance. The simplest nuclear magnetic resonance (NMR) spectroscopy method to quantify the secondary structure of proteins uses the areas of carbonyl and alpha carbon peaks. The quantification obtained by fitting procedures depends on the assignment of the peaks to the structure, type of line shape, number of peaks to be used, and other parameters that are set by the operator. In this paper, we demonstrate that the analysis of (13)C NMR spectra by a pattern recognition method-based on the singular value decomposition (SVD) regression, which does not depend on the operator-shows higher correlation coefficients for α-helix and ß-sheet (0.96 and 0.91, respectively) than Fourier transform infrared spectroscopy (FTIR) method. Therefore, the use of (13)C solid-state NMR spectra and SVD is a simple and reliable method for quantifying the secondary structures of insoluble proteins in solid-state.

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AIM: To present a content-based image retrieval (CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification. METHODS: Breast density is characterized by image texture using singular value decomposition (SVD) and histograms. Pattern similarity is computed by a support vector machine (SVM) to separate the four BI-RADS tissue categories. The crucial number of remaining singular values is varied (SVD), and linear, radial, and polynomial kernels are investigated (SVM). The system is supported by a large reference database for training and evaluation. Experiments are based on 5-fold cross validation. RESULTS: Adopted from DDSM, MIAS, LLNL, and RWTH datasets, the reference database is composed of over 10 000 various mammograms with unified and reliable ground truth. An average precision of 82.14% is obtained using 25 singular values (SVD), polynomial kernel and the one-against-one (SVM). CONCLUSION: Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.

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In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

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In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80 percent. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

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