Development of a new assay for quantification of parasite load of intracellular Leishmania sp. in macrophages using flow cytometry.

Finding novel methodologies that enhance the precision, agility, and standardization of drug discovery is crucial for studying leishmaniasis. The slide count is the technique most used to assess the leishmanicidal effect of a given drug in vitro. Despite being consolidated in the scientific environment, it presents several difficulties in its execution, assessment, and results. In addition to being laborious, this technique takes time, both for the preparation of the material for analysis and for the counting itself. Our research group suggests a fresh approach to address this requirement, which involves utilizing nuclear labeling with propidium iodide and flow cytometry to determine the quantity of Leishmania sp. parasites present in macrophages in vitro. Our results show that the fluorescence of infected samples increases as the infection rate increases. Using Pearson's Correlation analysis, it was possible to establish a correlation coefficient (Pearson r = 0.9473) that was strongly positive, linear, and directly proportional to the fluorescence and infection rate variables. Thus, it is possible to infer a mathematical equation through linear regression to estimate the number of parasites in each sample using the Relative Fluorescence Units (RFU) values. This new methodology opens space for the possibility of using this methodological resource in the in vitro quantification of Leishmania in macrophages.

Could combination chemotherapy be more effective than monotherapy in the treatment of visceral leishmaniasis? A systematic review of preclinical evidence.

From a systematic review framework, we assessed the preclinical evidence on the effectiveness of drug combinations for visceral leishmaniasis (VL) treatment. Research protocol was based on the PRISMA guideline. Research records were identified from Medline, Scopus and Web of Science. Animal models, infection and treatment protocols, parasitological and immunological outcomes were analysed. The SYRCLE's (SYstematic Review Center for Laboratory Animal Experimentation) toll was used to evaluate the risk of bias in all studies reviewed. Fourteen papers using mice, hamster and dogs were identified. Leishmania donovani was frequently used to induce VL, which was treated with 23 drugs in 40 different combinations. Most combinations allowed to reduce the effective dose, cost and time of treatment, in addition to improving the parasitological control of Leishmania spp. The benefits achieved from drug combinations were associated with an increased drug's half-life, direct parasitic toxicity and improved immune defences in infected hosts. Selection, performance and detection bias were the main limitations identified. Current evidence indicates that combination chemotherapy, especially those based on classical drugs (miltefosine, amphotericin B antimony-based compounds) and new drugs (CAL-101, PAM3Cys, tufisin and DB766), develops additive or synergistic interactions, which trigger trypanocidal and immunomodulatory effects associated with reduced parasite load, organ damage and better cure rates in VL.

Human predisposition to cognitive impairment and its relation with environmental exposure to potentially toxic elements.

New lines of evidence suggest that less than 10% of neurodegenerative diseases have a strict genetic aetiology and other factors may be prevalent. Environmental exposures to potentially toxic elements appear to be a risk factor for Parkinson's, Alzheimer's and sclerosis diseases. This study proposes a multidisciplinary approach combining neurosciences, psychology and environmental sciences while integrating socio-economic, neuropsychological, environmental and health data. We present the preliminary results of a neuropsychological assessment carried out in elderly residents of the industrial city of Estarreja. A battery of cognitive tests and a personal questionnaire were administered to the participants. Multivariate analysis and multiple linear regression analysis were used to identify potential relationships between the cognitive status of the participants and environmental exposure to potentially toxic elements. The results suggest a relationship between urinary PTEs levels and the incidence of cognitive disorders. They also point towards water consumption habits and profession as relevant factors of exposure. Linear regression models show that aluminium (R 2 = 38%), cadmium (R 2 = 11%) and zinc (R 2 = 6%) are good predictors of the scores of the Mini-Mental State Examination cognitive test. Median contents (µg/l) in groundwater are above admissible levels for drinking water for aluminium (371), iron (860), manganese (250), and zinc (305). While the World Health Organization does not provide health-based reference values for aluminium, results obtained from this study suggest that it may have an important role in the cognitive status of the elderly. Urine proved to be a suitable biomarker of exposure both to elements with low and high excretion rates.

Leishmania amazonensis impairs DC function by inhibiting CD40 expression via A2B adenosine receptor activation.

Dendritic cells (DCs) play an essential role in the modulation of immune responses and several studies have evaluated the interactions between Leishmania parasites and DCs. While extracellular ATP exhibits proinflammatory properties, adenosine is an important anti-inflammatory mediator. Here we investigated the effects of Leishmania infection on DC responses and the participation of purinergic signalling in this process. Bone marrow-derived dendritic cells (BMDCs) from C57BL/6J mice infected with Leishmania amazonensis, Leishmania braziliensis or Leishmania major metacyclic promastigotes showed decreased major histocompatibility complex (MHC) class II and CD86 expression and increased ectonucleotidase expression as compared with uninfected cells. In addition, L. amazonensis-infected DCs, which had lower CD40 expression, exhibited a decreased ability to induce T-cell proliferation. The presence of MRS1754, a highly selective A(2B) adenosine receptor antagonist at the time of infection increased MHC class II, CD86 and CD40 expression in L. amazonensis-infected DCs and restored the ability of the infected DCs to induce T-cell proliferation. Similar results were obtained through the inhibition of extracellular ATP hydrolysis using suramin. In conclusion, we propose that A(2B) receptor activation may be used by L. amazonensis to inhibit DC function and evade the immune response.

Comprehensive meta-analysis of QTL and gene expression studies identify candidate genes associated with Aspergillus flavus resistance in maize.

Aflatoxin (AF) contamination, caused by Aspergillus flavus, compromises the food safety and marketability of commodities, such as maize, cotton, peanuts, and tree nuts. Multigenic inheritance of AF resistance impedes conventional introgression of resistance traits into high-yielding commercial maize varieties. Several AF resistance-associated quantitative trait loci (QTLs) and markers have been reported from multiple biparental mapping and genome-wide association studies (GWAS) in maize. However, QTLs with large confidence intervals (CI) explaining inconsistent phenotypic variance limit their use in marker-assisted selection. Meta-analysis of published QTLs can identify significant meta-QTLs (MQTLs) with a narrower CI for reliable identification of genes and linked markers for AF resistance. Using 276 out of 356 reported QTLs controlling resistance to A. flavus infection and AF contamination in maize, we identified 58 MQTLs on all 10 chromosomes with a 66.5% reduction in the average CI. Similarly, a meta-analysis of maize genes differentially expressed in response to (a)biotic stresses from the to-date published literature identified 591 genes putatively responding to only A. flavus infection, of which 14 were significantly differentially expressed (-1.0 ≤ Log2Fc ≥ 1.0; p ≤ 0.05). Eight MQTLs were validated by their colocalization with 14 A. flavus resistance-associated SNPs identified from GWAS in maize. A total of 15 genes were physically close between the MQTL intervals and SNPs. Assessment of 12 MQTL-linked SSR markers identified three markers that could discriminate 14 and eight cultivars with resistance and susceptible responses, respectively. A comprehensive meta-analysis of QTLs and differentially expressed genes led to the identification of genes and makers for their potential application in marker-assisted breeding of A. flavus-resistant maize varieties.

Antigenic extracts of Leishmania braziliensis and Leishmania amazonensis associated with saponin partially protects BALB/c mice against Leishmania chagasi infection by suppressing IL-10 and IL-4 production.

This study evaluated two vaccine candidates for their effectiveness in protecting BALB/c mice against Leishmania chagasi infection. These immunogenic preparations were composed of Leishmania amazonensis or Leishmania braziliensis antigenic extracts in association with saponin adjuvant. Mice were given three subcutaneous doses of one of these vaccine candidates weekly for three weeks and four weeks later challenged with promastigotes of L. chagasi by intravenous injection. We observed that both vaccine candidates induced a significant reduction in the parasite load of the liver, while the L. amazonensis antigenic extract also stimulated a reduction in spleen parasite load. This protection was associated with a suppression of both interleukin (IL)-10 and IL-4 cytokines by spleen cells in response to L. chagasi antigen. No change was detected in the production of IFN-γ. Our data show that these immunogenic preparations reduce the type 2 immune response leading to the control of parasite replication.

Domain-Transformable Sparse Representation for Anomaly Detection in Moving-Camera Videos.

This paper presents a special matrix factorization based on sparse representation that detects anomalies in video sequences generated with moving cameras. Such representation is made by associating the frames of the target video, that is a sequence to be tested for the presence of anomalies, with the frames of an anomaly-free reference video, which is a previously validated sequence. This factorization is done by a sparse coefficient matrix, and any target-video anomaly is encapsulated into a residue term. In order to cope with camera trepidations, domaintransformations are incorporated into the sparse representation process. Approximations of the transformed-domain optimization problem are introduced to turn it into a feasible iterative process. Results obtained from a comprehensive video database acquired with moving cameras on a visually cluttered environment indicate that the proposed algorithm provides a better geometric registration between reference and target videos, greatly improving the overall performance of the anomaly-detection system.

On dictionary adaptation for recurrent pattern image coding.

In this paper, we exploit a recently introduced coding algorithm called multidimensional multiscale parser (MMP) as an alternative to the traditional transform quantization-based methods. MMP uses approximate pattern matching with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. We propose the use of predictive coding schemes that modify the source's probability distribution, in order to favour the efficiency of MMP's dictionary adaptation. Statistical conditioning is also used, allowing for an increased coding efficiency of the dictionaries' symbols. New dictionary design methods, that allow for an effective compromise between the introduction of new dictionary elements and the reduction of codebook redundancy, are also proposed. Experimental results validate the proposed techniques by showing consistent improvements in PSNR performance over the original MMP algorithm. When compared with state-of-the-art methods, like JPEG2000 and H.264/AVC, the proposed algorithm achieves relevant gains (up to 6 dB) for nonsmooth images and very competitive results for smooth images. These results strongly suggest that the new paradigm posed by MMP can be regarded as an alternative to the one traditionally used in image coding, for a wide range of image types.

Heavy Metals of Santiago Island (Cape Verde) Alluvial Deposits: Baseline Value Maps and Human Health Risk Assessment.

The chemical composition of surface geological materials may cause metabolic changes and promote endemic diseases (e.g., oncological, gastrointestinal, neurological or cardiovascular diseases). The results of a geochemical survey is presented following the guidelines proposed by the International Project IGCP 259 performed on the alluvium of Santiago Island (Cape Verde) and focused on public health issues. Geochemical mapping is the base knowledge needed to determine critical contents of potential toxic elements and the potentially harmful regions in the planet. This work presents maps of baseline values of potentially toxic elements (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, V, and Zn) in Santiago alluvium and the assessment of their human health risks. According to the results the Cd, Co, Cr, Ni and V baseline values are above the Canadian guidelines for stream sediments (for any proposal use) and for soils (for agricultural and residential proposal uses) and also above the target values of Dutch guidelines. Hazard indexes (HI) were calculated for children and adults. For children (HI) are higher than 1 for Co, Cr and Mn, indicating potential non-carcinogenic risk. For the other elements and for adults there is no potential non-carcinogenic risk. Cancer risk was calculated for Cd, Cr and Ni exposures, for adults and children, and the results are only slightly higher than the carcinogenic target risk of 1 × 10-6 for adults exposed to Cr by inhalation. However, these results may be underestimated because alluvial contaminants may be indirectly ingested by groundwater and by crop and vegetables consumption.

Evaluation of immune responses and protection induced by A2 and nucleoside hydrolase (NH) DNA vaccines against Leishmania chagasi and Leishmania amazonensis experimental infections.

Several antigens have been tested as vaccine candidates against Leishmania infections but controversial results have been reported when different antigens are co-administered in combined vaccination protocols. Immunization with A2 or nucleoside hydrolase (NH) antigens was previously shown to induce Th1 immune responses and protection in BALB/c mice against Leishmania donovani and L. amazonensis (A2) or L. donovani and L. mexicana (NH) infections. In this work, we investigated the protective efficacy of A2 and NH DNA vaccines, in BALB/c mice, against L. amazonensis or L. chagasi challenge infection. Immunization with either A2 (A2-pCDNA3) or NH (NH-VR1012) DNA induced an elevated IFN-gamma production before infection; however, only A2 DNA immunized mice were protected against both Leishmania species and displayed a sustained IFN-gamma production and very low IL-4 and IL-10 levels, after challenge. Mice immunized with NH/A2 DNA produced higher levels of IFN-gamma in response to both specific recombinant proteins (rNH or rA2), but displayed higher IL-4 and IL-10 levels and increased edema and parasite loads after L. amazonensis infection, as compared to A2 DNA immunized animals. These data extend the characterization of the immune responses induced by NH and A2 antigens as potential candidates to compose a defined vaccine and indicate that a highly polarized type 1 immune response is required for improvement of protective levels of combined vaccines against both L. amazonensis and L. chagasi infections.

Weak Classifier for Density Estimation in Eye Localization and Tracking.

In this paper, we propose a fast weak classifier that can detect and track eyes in video sequences. The approach relies on a least-squares detector based on the inner product detector (IPD) that can stimate a probability density distribution for a feature's location-which fits naturally with a Bayesian estimation cycle, such as a Kalman or particle filter. As a least-squares sliding window detector, it possesses tolerance to small variations in the desired pattern while maintaining good generalization capabilities and computational efficiency. We propose two approaches to integrating the IPD with a particle filter tracker. We use the BioID, FERET, LFPW, and COFW public datasets as well as five manually annotated high-definition video sequences to quantitatively evaluate the algorithms' performance. The video data set contains four subjects, different types of backgrounds, blurring due to fast motion, and occlusions. All code and data are available.

Image Coding Using Generalized Predictors Based on Sparsity and Geometric Transformations.

Directional intra prediction plays an important role in current state-of-the-art video coding standards. In directional prediction, neighbouring samples are projected along a specific direction to predict a block of samples. Ultimately, each prediction mode can be regarded as a set of very simple linear predictors, a different one for each pixel of a block. Therefore, a natural question that arises is whether one could use the theory of linear prediction in order to generate intra prediction modes that provide increased coding efficiency. However, such an interpretation of each directional mode as a set of linear predictors is too poor to provide useful insights for their design. In this paper, we introduce an interpretation of directional prediction as a particular case of linear prediction, which uses the first-order linear filters and a set of geometric transformations. This interpretation motivated the proposal of a generalized intra prediction framework, whereby the first-order linear filters are replaced by adaptive linear filters with sparsity constraints. In this context, we investigate the use of efficient sparse linear models, adaptively estimated for each block through the use of different algorithms, such as matching pursuit, least angle regression, least absolute shrinkage and selection operator, or elastic net. The proposed intra prediction framework was implemented and evaluated within the state-of-the-art high efficiency video coding standard. Experiments demonstrated the advantage of this predictive solution, mainly in the presence of images with complex features and textured areas, achieving higher average bitrate savings than other related sparse representation methods proposed in the literature.

Intra Predictive Depth Map Coding Using Flexible Block Partitioning.

A complete encoding solution for efficient intra-based depth map compression is proposed in this paper. The algorithm, denominated predictive depth coding (PDC), was specifically developed to efficiently represent the characteristics of depth maps, mostly composed by smooth areas delimited by sharp edges. At its core, PDC involves a directional intra prediction framework and a straightforward residue coding method, combined with an optimized flexible block partitioning scheme. In order to improve the algorithm in the presence of depth edges that cannot be efficiently predicted by the directional modes, a constrained depth modeling mode, based on explicit edge representation, was developed. For residue coding, a simple and low complexity approach was investigated, using constant and linear residue modeling, depending on the prediction mode. The performance of the proposed intra depth map coding approach was evaluated based on the quality of the synthesized views using the encoded depth maps and original texture views. The experimental tests based on all intra configuration demonstrated the superior rate-distortion performance of PDC, with average bitrate savings of 6%, when compared with the current state-of-the-art intra depth map coding solution present in the 3D extension of a high-efficiency video coding (3D-HEVC) standard. By using view synthesis optimization in both PDC and 3D-HEVC encoders, the average bitrate savings increase to 14.3%. This suggests that the proposed method, without using transform-based residue coding, is an efficient alternative to the current 3D-HEVC algorithm for intra depth map coding.

Multiscale image fusion using the undecimated wavelet transform with spectral factorization and nonorthogonal filter banks.

Multiscale transforms are among the most popular techniques in the field of pixel-level image fusion. However, the fusion performance of these methods often deteriorates for images derived from different sensor modalities. In this paper, we demonstrate that for such images, results can be improved using a novel undecimated wavelet transform (UWT)-based fusion scheme, which splits the image decomposition process into two successive filtering operations using spectral factorization of the analysis filters. The actual fusion takes place after convolution with the first filter pair. Its significantly smaller support size leads to the minimization of the unwanted spreading of coefficient values around overlapping image singularities. This usually complicates the feature selection process and may lead to the introduction of reconstruction errors in the fused image. Moreover, we will show that the nonsubsampled nature of the UWT allows the design of nonorthogonal filter banks, which are more robust to artifacts introduced during fusion, additionally improving the obtained results. The combination of these techniques leads to a fusion framework, which provides clear advantages over traditional multiscale fusion approaches, independent of the underlying fusion rule, and reduces unwanted side effects such as ringing artifacts in the fused reconstruction.

Recursive algorithms for bias and gain nonuniformity correction in infrared videos.

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity.

Production of lactobionic acid and sorbitol from lactose/fructose substrate using GFOR/GL enzymes from Zymomonas mobilis cells: a kinetic study.

In this work, we have investigated the kinetics of the biotechnological production of lactobionic acid (LBA) and sorbitol by the catalytic action of glucose-fructose oxidoreductase (GFOR) and glucono-δ-lactonase (GL) enzymes. The cells of bacterium Zymomonas mobilis ATCC 29191 containing this enzymatic complex were submitted to permeabilization and reticulation procedures. The effect of the concentration of substrates on the rate of product formation using a mobilized cell system was investigated. The application of higher fructose concentration seems to not affect the initial rate of formation of the bionic acid. Under conditions of low initial concentration of lactose, the experimental kinetic data of the bi-substrate reaction were modelled by assuming a rate equation of the classical ping-pong mechanism. The found kinetic parameters displayed a low affinity of the GFOR enzyme for both substrates. The enzymatic system did not exhibit normal Michaelis-Menten kinetics in response to a change of concentration of lactose, when fructose was held constant, presenting a sigmoid relationship between initial velocity and substrate concentration. A rate equation based on Hill kinetics was used to describe the kinetic behaviour of this enzyme-substituted reaction at higher lactose concentrations. The results from batch experiments using immobilized cells within Ca-alginate beads revealed that there is no pronounced occurrence of mass transfer limitations on LBA production for beads with 1.2 mm in average diameter. This discussion aids for defining the best operating conditions to maximize the productivity for LBA and sorbitol in this bioconversion and also for reducing the complexity of downstream separation processes.

No-reference blur assessment of digital pictures based on multifeature classifiers.

In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios.

Scanned compound document encoding using multiscale recurrent patterns.

In this paper, we propose a new encoder for scanned compound documents, based upon a recently introduced coding paradigm called multidimensional multiscale parser (MMP). MMP uses approximate pattern matching, with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. These features give MMP the ability to adjust to the input image's characteristics, resulting in high coding efficiencies for a wide range of image types. This versatility makes MMP a good candidate for compound digital document encoding. The proposed algorithm first classifies the image blocks as smooth (texture) and nonsmooth (text and graphics). Smooth and nonsmooth blocks are then compressed using different MMP-based encoders, adapted for encoding either type of blocks. The adaptive use of these two types of encoders resulted in performance gains over the original MMP algorithm, further increasing the performance advantage over the current state-of-the-art image encoders for scanned compound images, without compromising the performance for other image types.

Application of the Nernst-Planck approach to lead ion exchange in Ca-loaded Pelvetia canaliculata.

Ca-loaded Pelvetia canaliculata biomass was used to remove Pb(2+) in aqueous solution from batch and continuous systems. The physicochemical characterization of algae Pelvetia particles by potentiometric titration and FTIR analysis has shown a gel structure with two major binding groups - carboxylic (2.8 mmol g(-1)) and hydroxyl (0.8 mmol g(-1)), with an affinity constant distribution for hydrogen ions well described by a Quasi-Gaussian distribution. Equilibrium adsorption (pH 3 and 5) and desorption (eluents: HNO(3) and CaCl(2)) experiments were performed, showing that the biosorption mechanism was attributed to ion exchange among calcium, lead and hydrogen ions with stoichiometry 1:1 (Ca:Pb) and 1:2 (Ca:H and Pb:H). The uptake capacity of lead ions decreased with pH, suggesting that there is a competition between H(+) and Pb(2+) for the same binding sites. A mass action law for the ternary mixture was able to predict the equilibrium data, with the selectivity constants alpha(Ca)(H)=9+/-1 and alpha(Ca)(Pb)=44+/-5, revealing a higher affinity of the biomass towards lead ions. Adsorption (initial solution pH 4.5 and 2.5) and desorption (0.3M HNO(3)) kinetics were performed in batch and continuous systems. A mass transfer model using the Nernst-Planck approximation for the ionic flux of each counter-ion was used for the prediction of the ions profiles in batch systems and packed bed columns. The intraparticle effective diffusion constants were determined as 3.73x10(-7)cm(2)s(-1) for H(+), 7.56x10(-8)cm(2)s(-1) for Pb(2+) and 6.37x10(-8)cm(2)s(-1) for Ca(2+).

On ECG signal compression with 1-D multiscale recurrent patterns allied to preprocessing techniques.

This paper presents the results of a multiscale pattern-matching-based ECG encoder, which employs simple preprocessing techniques for adapting the input signal. Experiments carried out with records from the Massachusetts Institute of Technology-Beth Israel Hospital database show that the proposed scheme is effective, outperforming some state-of-the-art schemes described in the literature.