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This work deals with adaptive predictive deconvolution of non-stationary channels. In particular, we investigate the use of a cascade of linear predictors in the recovering of sparse and antisparse original signals. To do so, we first discuss the behavior of the Lp Prediction Error Filter (PEF), with p different of 2, showing that it has a superior ability to deal with non-minimum phase channels in comparison with the classical L2 PEF, although it still presents intrinsic limitations due to its direct linear structure. The cascade structure emerges as a possible solution to circumvent this issue. We apply the proposed cascade structure in the deconvolution of non-stationary channels, with minimum-, maximum- , mixed- and variable-phase response, and also noise scenarios. From the simulation results we observed that, besides the duality relation between the Lp norms, they present different algorithmic behavior: the L1 norm attains a fast convergence, enhancing the cascade tracking capacity, but is more sensible to noise. The L4 norm, on the other hand, is more robust to noise, but presents slower convergence and tracking capability.

Purpose: to characterize the distribution and mechanisms involved in ceftolozane/tazobactam (C/T) resistance in Pseudomonas aeruginosa isolates recovered from intensive care units (ICUs) in Portugal as part of the STEP surveillance study. Materials and methods: a total of 226 P. aeruginosa isolates were collected from patients with low respiratory tract infections (LRTI) admitted to ICUs between June 2017 and July 2018. Susceptibility to antimicrobials including the recent C/T combination was determined by EUCAST-criteria. Whole genome sequencing (WGS) was performed in a subset of 17 isolates. Results: multidrug resistant (MDR) and extremely drug resistant (XDR) phenotypes accounted for 20.4% and 25.7% of cases, respectively. C/T showed the highest susceptibility rate in both MDR (100%) and XDR (93.1%) isolates, followed by amikacin (97.8% MDR and 79.3% XDR). blaKPC-3 (n = 2) and blaGES-13 (n = 1) carbapenemase genes were detected in 3 of the 17 sequenced isolates, but only the GES-13-producing isolate displayed resistance to C/T. Additionally, the C/T-resistant phenotype was also found in two non-carbapenemase producers that carried known ceftolozane/tazobactam resistance-associated mutations in the PBP3 gene. Conclusions: C/T was highly active against MDR/XDR-P. aeruginosa isolates causing LRTI in ICUs. Moreover, beyond carbapenemase-encoding genes, mutations in chromosomal PBP-encoding genes might also be involved in ceftolozane/tazobactam resistance in Portugal.

This letter introduces the concept of antisparse Blind Source Separation (BSS), proposing a suitable criterion based on the $\ell_\infty$ norm to explore the antisparsity feature. The effectiveness of the criterion is theoretically demonstrated and it is also evaluated by computational simulations, which consider up to ten distinct sources with different correlation levels. Moreover, we simulated a scenario in wireless communication with binary sources, comparing our approach to the Constant Modulus algorithm. Both the theoretical and the simulation results highlight the potentiality of using antisparsity as a prior in BSS.

CrpP enzymes have been recently described as a novel ciprofloxacin-resistance mechanism. We investigated by whole genome sequencing the presence of crpP-genes and other mechanisms involved in quinolone resistance in MDR/XDR-Pseudomonas aeruginosa isolates (n = 55) with both ceftolozane-tazobactam susceptible or resistant profiles recovered from intensive care unit patients during the STEP (Portugal) and SUPERIOR (Spain) surveillance studies. Ciprofloxacin resistance was associated with mutations in the gyrA and parC genes. Additionally, plasmid-mediated genes (qnrS2 and aac(6′)-Ib-cr) were eventually detected. Ten chromosomal crpP-like genes contained in related pathogenicity genomic islands and 6 different CrpP (CrpP1-CrpP6) proteins were found in 65% (36/55) of the isolates. Dissemination of CrpP variants was observed among non-related clones of both countries, including the CC175 (Spain) high-risk clone and CC348 (Portugal) clone. Interestingly, 5 of 6 variants (CrpP1-CrpP5) carried missense mutations in an amino acid position (Gly7) previously defined as essential conferring ciprofloxacin resistance, and decreased ciprofloxacin susceptibility was only associated with the novel CrpP6 protein. In our collection, ciprofloxacin resistance was mainly due to chromosomal mutations in the gyrA and parC genes. However, crpP genes carrying mutations essential for protein function (G7, I26) and associated with a restored ciprofloxacin susceptibility were predominant. Despite the presence of crpP genes is not always associated with ciprofloxacin resistance, the risk of emergence of novel CrpP variants with a higher ability to affect quinolones is increasing. Furthermore, the spread of crpP genes in highly mobilizable genomic islands among related and non-related P. aeruginosa clones alert the dispersion of MDR pathogens in hospital settings.

Economic and financial decision-making may cause a significant impact on government, society, and industries. Due to the increasing volume of data, decision science has become an interdisciplinary field of study, supported by efficient methods and models of data analysis. Our contributions lie exactly in the intersection of signal processing, tensorial algebra, and decision science. More precisely, we introduce a novel approach in which the data taken into account in the decision process is modeled as a tensor. Moreover, we apply adaptive prediction methods for aggregating the decision tensor. Results provided by numerical experiments with both synthetic and actual data attest to the efficacy of our proposal in better supporting economic and financial decisions.

A vast number of multicriteria decision making methods have been developed to deal with the problem of ranking a set of alternatives evaluated in a multicriteria fashion. Very often, these methods assume that the evaluation among criteria is statistically independent. However, in actual problems, the observed data may comprise dependent criteria, which, among other problems, may result in biased rankings. In order to deal with this issue, we propose a novel approach whose aim is to estimate, from the observed data, a set of independent latent criteria, which can be seen as an alternative representation of the original decision matrix. A central element of our approach is to formulate the decision problem as a blind source separation problem, which allows us to apply independent component analysis techniques to estimate the latent criteria. Moreover, we consider TOPSIS-based approaches to obtain the ranking of alternatives from the latent criteria. Results in both synthetic and actual data attest the relevance of the proposed approach.

In many ranking problems, some particular aspects of the addressed situation should be taken into account in the aggregation process. An example is the presence of correlations between criteria, which may introduce bias in the derived ranking. In these cases, aggregation functions based on a capacity may be used to overcome this inconvenience, such as the Choquet integral or the multilinear model. The adoption of such strategies requires a stage to estimate the parameters of these aggregation operators. This task may be difficult in situations in which we do not have either further information about these parameters or preferences given by the decision maker. Therefore, the aim of this paper is to deal with such situations through an unsupervised approach for capacity identification based on the multilinear model. Our goal is to estimate a capacity that can mitigate the bias introduced by correlations in the decision data and, therefore, to provide a fairer result. The viability of our proposal is attested by numerical experiments with synthetic data