A Measure of Synergy Based on Union Information.

The partial information decomposition (PID) framework is concerned with decomposing the information that a set of (two or more) random variables (the sources) has about another variable (the target) into three types of information: unique, redundant, and synergistic. Classical information theory alone does not provide a unique way to decompose information in this manner and additional assumptions have to be made. One often overlooked way to achieve this decomposition is using a so-called measure of union information-which quantifies the information that is present in at least one of the sources-from which a synergy measure stems. In this paper, we introduce a new measure of union information based on adopting a communication channel perspective, compare it with existing measures, and study some of its properties. We also include a comprehensive critical review of characterizations of union information and synergy measures that have been proposed in the literature.

Orders between Channels and Implications for Partial Information Decomposition.

The partial information decomposition (PID) framework is concerned with decomposing the information that a set of random variables has with respect to a target variable into three types of components: redundant, synergistic, and unique. Classical information theory alone does not provide a unique way to decompose information in this manner, and additional assumptions have to be made. Recently, Kolchinsky proposed a new general axiomatic approach to obtain measures of redundant information based on choosing an order relation between information sources (equivalently, order between communication channels). In this paper, we exploit this approach to introduce three new measures of redundant information (and the resulting decompositions) based on well-known preorders between channels, contributing to the enrichment of the PID landscape. We relate the new decompositions to existing ones, study several of their properties, and provide examples illustrating their novelty. As a side result, we prove that any preorder that satisfies Kolchinsky's axioms yields a decomposition that meets the axioms originally introduced by Williams and Beer when they first proposed PID.

Screening of febrile patients with suspected malaria from the Brazilian Amazon for virus infection.

Arthropod-borne viruses (arboviruses) are a significant public health threat, especially in tropical and subtropical regions. More than 150 arboviruses can cause febrile illness following infection in humans. The Brazilian Amazon region has the highest number of arboviruses detected worldwide. In addition to arboviruses, malaria, caused by Plasmodium vivax, is endemic in the Amazon. Patients with malaria and arboviral disease frequently show similar clinical presentation and laboratory findings, making the diagnosis of the cause of the infection challenging. The aim of this study was to evaluate the potential for viral infections in patients with suspected malaria but without Plasmodium infection in the Brazilian Amazon. We recruited 200 subjects with suspected malaria in Manaus, Brazil. First, we tested for arboviruses in serum samples from 124 of the 200 participants using an arbovirus DNA microarray platform, which did not detect any virus. Then, we mixed the serum samples of the other 76 participants in 10 pools and subjected them to next-generation sequencing. Analysis of the sequencing data revealed the presence of only one arbovirus (Zika virus) in one sample pool. This analysis also detected the presence of primate erythroparvovirus 1 and pegivirus C. These results suggest that arboviruses are not the most frequent viral infections in patients with suspected malaria but without Plasmodium infection in the metropolitan region of Manaus. Implementation of specific viral surveillance tests will help in the early detection of viruses with epidemic potential.

A multicenter study on radiomic features from T2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics.

PURPOSE: To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. METHODS: T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. RESULTS: From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. CONCLUSION: We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.

CCR2 Plays a Protective Role in Rocio Virus-Induced Encephalitis by Promoting Macrophage Infiltration Into the Brain.

Rocio virus (ROCV) is a highly neuropathogenic mosquito-transmitted flavivirus responsible for an unprecedented outbreak of human encephalitis during 1975-1976 in Sao Paulo State, Brazil. Previous studies have shown an increased number of inflammatory macrophages in the central nervous system (CNS) of ROCV-infected mice, implying a role for macrophages in the pathogenesis of ROCV. Here, we show that ROCV infection results in increased expression of CCL2 in the blood and in infiltration of macrophages into the brain. Moreover, we show, using CCR2 knockout mice, that CCR2 expression is essential for macrophage infiltration in the brain during ROCV infection and that the lack of CCR2 results in increased disease severity and mortality. Thus, our findings show the protective role of CCR2-mediated infiltration of macrophages in the brain during ROCV infection.

Genetic characterization of Cacipacoré virus from ticks collected in São Paulo State, Brazil.

Cacipacoré virus (CPCV) is a potential emerging virus classified in the genus Flavivirus, family Flaviviridae. In the present study, we present the genetic characterization of a CPCV isolated from ticks (Amblyomma cajennense) collected from a sick capybara (Hydrochoerus hydrochaeris) in São Paulo State, Brazil. The CPCV isolate shares the typical genomic organization of flaviviruses with 10,857 nucleotides in length and a single open reading frame of 10,284 nucleotides encoding a polyprotein of 3,427 amino acids. Phylogenetic analysis revealed that CPCV is unique, as a potentially tick-borne virus, in the Japanese encephalitis virus serogroup.

Detection of DENV-4 genotype I from mosquitoes collected in the city of Manaus, Brazil.

BACKGROUND: Dengue epidemics have been reported in Brazil since 1981. In Manaus, a large city in the Amazon region, dengue is endemic with all four-virus serotypes (DENV-1, -2, -3, and -4) simultaneously causing human disease. In 2008, during a surveillance of dengue virus in mosquitoes in the district of Tancredo Neves in Manaus, 260 mosquitoes of Aedes genus were captured, identified and grouped into pools of 10 mosquitoes. FINDINGS: RNA extracts of mosquito pools were tested by a RT-Hemi-Nested-PCR for detection of flaviviruses. One amplicon of 222 bp, compatible with dengue virus serotype 4, was obtained from a pool of Aedes aegypti. The nucleotide sequence of the amplicon indicated that the mosquitoes were infected with DENV-4 of genotype I. This virus of Asian origin has been described in Manaus in 2008 infecting acute febrile illness patients. CONCLUSION: This is the first report of dengue virus serotype 4 genotype I infecting Aedes aegypti in the Americas.

Improving performance and generalizability in radiogenomics: a pilot study for prediction of IDH1/2 mutation status in gliomas with multicentric data.

Purpose: Radiogenomics offers a potential virtual and noninvasive biopsy. However, radiogenomics models often suffer from generalizability issues, which cause a performance degradation on unseen data. In MRI, differences in the sequence parameters, manufacturers, and scanners make this generalizability issue worse. Such image acquisition information may be used to define different environments and select robust and invariant radiomic features associated with the clinical outcome that should be included in radiomics/radiogenomics models. Approach: We assessed 77 low-grade gliomas and glioblastomas multiform patients publicly available in TCGA and TCIA. Radiomics features were extracted from multiparametric MRI images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery) and different regions-of-interest (enhancing tumor, nonenhancing tumor/necrosis, and edema). A method developed to find variables that are part of causal structures was used for feature selection and compared with an embedded feature selection approach commonly used in radiomics/radiogenomics studies, across two different scenarios: (1) leaving data from a center as an independent held-out test set and tuning the model with the data from the remaining centers and (2) use stratified partitioning to obtain the training and the held-out test sets. Results: In scenario (1), the performance of the proposed methodology and the traditional embedded method was AUC: 0.75 [0.25; 1.00] versus 0.83 [0.50; 1.00], Sens.: 0.67 [0.20; 0.93] versus 0.67 [0.20; 0.93], Spec.: 0.75 [0.30; 0.95] versus 0.75 [0.30; 0.95], and MCC: 0.42 [0.19; 0.68] versus 0.42 [0.19; 0.68] for center 1 as the held-out test set. The performance of both methods for center 2 as the held-out test set was AUC: 0.64 [0.36; 0.91] versus 0.55 [0.27; 0.82], Sens.: 0.00 [0.00; 0.73] versus 0.00 [0.00; 0.73], Spec.: 0.82 [0.52; 0.94] versus 0.91 [0.62; 0.98], and MCC: - 0.13 [ - 0.38 ; - 0.04 ] versus - 0.09 [ - 0.38 ; - 0.02 ] , whereas for center 3 was AUC: 0.80 [0.62; 0.95] versus 0.89 [0.56; 0.96], Sens.: 0.86 [0.48; 0.97] versus 0.86 [0.48; 0.97], Spec.: 0.72 [0.54; 0.85] versus 0.79 [0.61; 0.90], and MCC: 0.47 [0.41; 0.53] versus 0.55 [0.48; 0.60]. For center 4, the performance of both methods was AUC: 0.77 [0.51; 1.00] versus 0.75 [0.47; 0.97], Sens.: 0.53 [0.30; 0.75] versus 0.00 [0.00; 0.15], Spec.: 0.71 [0.35; 0.91] versus 0.86 [0.48; 0.97], and MCC: 0.23 [0.16; 0.31] versus. - 0.32 [ - 0.46 ; - 0.20 ] . In scenario (2), the performance of these methods was AUC: 0.89 [0.71; 1.00] versus 0.79 [0.58; 0.94], Sens.: 0.86 [0.80; 0.92] versus 0.43 [0.15; 0.74], Spec.: 0.87 [0.62; 0.96] versus 0.87 [0.62; 0.96], and MCC: 0.70 [0.60; 0.77] versus 0.33 [0.24; 0.42]. Conclusions: This proof-of-concept study demonstrated good performance by the proposed feature selection method in the majority of the studied scenarios, as it promotes robustness of features included in the models and the models' generalizability by making used imaging data of different scanners or with sequence parameters.

Near-infrared hyperspectral unmixing based on a minimum volume criterion for fast and accurate chemometric characterization of counterfeit tablets.

A rapid detection of the nonauthenticity of suspect tablets is a key first step in the fight against pharmaceutical counterfeiting. The chemical characterization of these tablets is the logical next step to evaluate their impact on patient health and help authorities in tracking their source. Hyperspectral unmixing of near-infrared (NIR) image data is an emerging effective technology to infer the number of compounds, their spectral signatures, and the mixing fractions in a given tablet, with a resolution of a few tens of micrometers. In a linear mixing scenario, hyperspectral vectors belong to a simplex whose vertices correspond to the spectra of the compounds present in the sample. SISAL (simplex identification via split augmented Lagrangian), MVSA (minimum volume simplex analysis), and MVES (minimum-volume enclosing simplex) are recent algorithms designed to identify the vertices of the minimum volume simplex containing the spectral vectors and the mixing fractions at each pixel (vector). This work demonstrates the usefulness of these techniques, based on minimum volume criteria, for unmixing NIR hyperspectral data of tablets. The experiments herein reported show that SISAL/MVSA and MVES largely outperform MCR-ALS (multivariate curve resolution-alternating least-squares), which is considered the state-of-the-art in spectral unmixing for analytical chemistry. These experiments are based on synthetic data (studying the effect of noise and the presence/absence of pure pixels) and on a real data set composed of NIR images of counterfeit tablets.

Mosquitoes infected with dengue viruses in Brazil.

Dengue epidemics have been reported in Brazil since 1985. The scenery has worsened in the last decade because several serotypes are circulating and producing a hyper-endemic situation, with an increase of DHF/DSS cases as well as the number of fatalities. Herein, we report dengue virus surveillance in mosquitoes using a Flavivirus genus-specific RT-Hemi-Nested-PCR assay. The mosquitoes (Culicidae, n = 1700) collected in the Northeast, Southeast and South of Brazil, between 1999 and 2005, were grouped into 154 pools. Putative genomes of DENV-1, -2 and -3 were detected in 6 mosquito pools (3.8%). One amplicon of putative DENV-1 was detected in a pool of Haemagogus leucocelaenus suggesting that this virus could be involved in a sylvatic cycle. DENV-3 was found infecting 3 pools of larvae of Aedes albopictus and the nucleotide sequence of one of these viruses was identified as DENV-3 of genotype III, phylogenetically related to other DENV-3 isolated in Brazil. This is the first report of a nucleotide sequence of DENV-3 from larvae of Aedes albopictus.

Statistical classification of road pavements using near field vehicle rolling noise measurements.

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

A classification-based approach to semi-supervised clustering with pairwise constraints.

In this paper, we introduce a neural network framework for semi-supervised clustering with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose semi-supervised clustering into two simpler classification tasks: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method. The proposed approach is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.

External Patch-Based Image Restoration Using Importance Sampling.

This paper introduces a new approach to patchbased image restoration based on external datasets and importance sampling. The minimum mean squared error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new method, which can be interpreted as a generalization of the external non-local means (NLM), uses self-normalized importance sampling to efficiently approximate the MMSE estimates. The use of self-normalized importance sampling endows the proposed method with great flexibility, namely regarding the statistical properties of the measurement noise. The effectiveness of the proposed method is shown in a series of experiments using both generic large-scale and class-specific external datasets.

Oropouche orthobunyavirus: Genetic characterization of full-length genomes and development of molecular methods to discriminate natural reassortments.

Oropouche orthobunyavirus (OROV) has significant impact in public health in Amazon region. This arbovirus is one of the most common causes of febrile illness in Brazil, and is responsible for several epidemics since 1960's. In this study, we sequenced and characterized the complete coding sequences (S-, M- and L-RNA) of 35 OROV isolates from Brazil. Here, we classified 20 strains in genotype I from Pará and Maranhão states, nine as genotype II from Pará and Rondônia states confirmed, four classified into genotype III from Acre, Maranhão, Minas Gerais and Rondônia states and two genotype IV from Amazonas State. Also, we did not observe reassortment events involving the OROV isolates. In addition, we developed novel RT-PCR tools to identify reassortment events among OROV strains. These data will be useful to better understand the molecular epidemiology and diagnostic of OROV infections.

A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising.

We propose a new approach to image fusion, inspired by the recent plug-and-play (PnP) framework. In PnP, a denoiser is treated as a black-box and plugged into an iterative algorithm, taking the place of the proximity operator of some convex regularizer, which is formally equivalent to a denoising operation. This approach offers flexibility and excellent performance, but convergence may be hard to analyze, as most state-of-the-art denoisers lack an explicit underlying objective function. Here, we propose using a scene-adapted denoiser (i.e., targeted to the specific scene being imaged) plugged into the iterations of the alternating direction method of multipliers (ADMM). This approach, which is a natural choice for image fusion problems, not only yields state-of-the-art results, but it also allows proving convergence of the resulting algorithm. The proposed method is tested on two different problems: hyperspectral fusion/sharpening and fusion of blurred-noisy image pairs.

Ilheus and Saint Louis encephalitis viruses elicit cross-protection against a lethal Rocio virus challenge in mice.

Rocio virus (ROCV) was the causative agent of an unprecedented outbreak of encephalitis during the 1970s in the Vale do Ribeira, Sao Paulo State, in the Southeast region of Brazil. Surprisingly, no further cases of ROCV infection were identified after this outbreak; however, serological surveys have suggested the circulation of ROCV among humans and animals in different regions of Brazil. Cross-protective immunity among flaviviruses is well documented; consequently, immunity induced by infections with other flaviviruses endemic to Brazil could potentially be responsible for the lack of ROCV infections. Herein, we evaluated the cross-protection mediated by other flaviviruses against ROCV infection using an experimental C57BL/6 mouse model. Cross-protection against ROCV infection was observed when animals had prior exposure to Ilheus virus or Saint Louis encephalitis virus, suggesting that cross-reactive anti-flavivirus antibodies may limit ROCV disease outbreaks.

A new twIst: two-step iterative shrinkage/thresholding algorithms for image restoration.

Iterative shrinkage/thresholding (IST) algorithms have been recently proposed to handle a class of convex unconstrained optimization problems arising in image restoration and other linear inverse problems. This class of problems results from combining a linear observation model with a nonquadratic regularizer (e.g., total variation or wavelet-based regularization). It happens that the convergence rate of these IST algorithms depends heavily on the linear observation operator, becoming very slow when this operator is ill-conditioned or ill-posed. In this paper, we introduce two-step IST (TwIST) algorithms, exhibiting much faster convergence rate than IST for ill-conditioned problems. For a vast class of nonquadratic convex regularizers (l(p) norms, some Besov norms, and total variation), we show that TwIST converges to a minimizer of the objective function, for a given range of values of its parameters. For noninvertible observation operators, we introduce a monotonic version of TwIST (MTwIST); although the convergence proof does not apply to this scenario, we give experimental evidence that MTwIST exhibits similar speed gains over IST. The effectiveness of the new methods are experimentally confirmed on problems of image deconvolution and of restoration with missing samples.

Majorization-minimization algorithms for wavelet-based image restoration.

Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using l1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

Oropouche Virus: Clinical, Epidemiological, and Molecular Aspects of a Neglected Orthobunyavirus.

AbstractOropouche virus (OROV) is an important cause of arboviral illness in Latin American countries, more specifically in the Amazon region of Brazil, Venezuela and Peru, as well as in other countries such as Panama. In the past decades, the clinical, epidemiological, pathological, and molecular aspects of OROV have been published and provide the basis for a better understanding of this important human pathogen. Here, we describe the milestones in a comprehensive review of OROV epidemiology, pathogenesis, and molecular biology, including a description of the first isolation of the virus, the outbreaks during the past six decades, clinical aspects of OROV infection, diagnostic methods, genome and genetic traits, evolution, and viral dispersal.