Molecular analysis of the dengue virus type 1 and 2 in Brazil based on sequences of the genomic envelope-nonstructural protein 1 junction region

The genomic sequences of the Envelope-Non-Structural protein 1 junction region (E/NS1) of 84 DEN-1 and 22 DEN-2 isolates from Brazil were determined. Most of these strains were isolated in the period from 1995 to 2001 in endemic and regions of recent dengue transmission in São Paulo State. Sequence data for DEN-1 and DEN-2 utilized in phylogenetic and split decomposition analyses also include sequences deposited in GenBank from different regions of Brazil and of the world. Phylogenetic analyses were done using both maximum likelihood and Bayesian approaches. Results for both DEN-1 and DEN-2 data are ambiguous, and support for most tree bipartitions are generally poor, suggesting that E/NS1 region does not contain enough information for recovering phylogenetic relationships among DEN-1 and DEN-2 sequences used in this study. The network graph generated in the split decomposition analysis of DEN-1 does not show evidence of grouping sequences according to country, region and clades. While the network for DEN-2 also shows ambiguities among DEN-2 sequences, it suggests that Brazilian sequences may belong to distinct subtypes of genotype III.

AN UPWARD TREND IN DNA P16INK4A METHYLATION PATTERN AND HIGH RISK HPV INFECTION ACCORDING TO THE SEVERITY OF THE CERVICAL LESION

SUMMARY High-risk human papillomavirus (hr-HPV) infection is necessary but not sufficient for cervical cancer development. Recently, P16INK4A gene silencing through hypermethylation has been proposed as an important cofactor in cervical carcinogenesis due to its tumor suppressor function. We aimed to investigate P16INK4A methylation status in normal and neoplastic epithelia and evaluate an association with HPV infection and genotype. This cross-sectional study was performed with 141 cervical samples from patients attending Hospital Moncorvo Filho, Rio de Janeiro. HPV detection and genotyping were performed through PCR and P16INK4A methylation by nested-methylation specific PCR (MSP). HPV frequency was 62.4% (88/141). The most common HPV were HPV16 (37%), HPV18 (16.3%) and HPV33/45(15.2%). An upward trend was observed concerning P16INK4A methylation and lesion degree: normal epithelia (10.7%), low grade lesions (22.9%), high grade (57.1%) and carcinoma (93.1%) (p < 0.0001). A multivariate analysis was performed to evaluate an association between methylation, age, tobacco exposure, HPV infection and genotyping. A correlation was found concerning methylation with HPV infection (p < 0.0001), hr-HPV (p = 0.01), HSIL (p < 0.0007) and malignant lesions (p < 0.0001). Since viral infection and epigenetic alterations are related to cervical carcinoma, we suggest that P16INK4A methylation profile maybe thoroughly investigated as a biomarker to identify patients at risk of cancer.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Removal of ochratoxin A from contaminated white and red wines using oenological fining agents

Mycotoxins are toxic secondary metabolites produced by certain moulds, being ochratoxin A (OTA) one of the most relevant. Its chemical structure is a dihydro-isocoumarin connected at the 7-carboxy group to a molecule of L--phenylalanine via an amide bond. OTA contamination of wines might be a risk to consumer health, thus requiring treatments to achieve acceptable standards for human consumption [1]. According to the Regulation No. 1881/2006 of the European Commission, the maximum limit for OTA in wine is 2 µg/kg [2]. Therefore, the aim of this work was to know the effect of different fining agents on OTA removal, as well as their impact on white and red wine physicochemical characteristics. To evaluate their efficiency, 11 commercial fining agents (mineral, synthetic, animal and vegetable proteins) were used to get new approaches on OTA removal from white and red wines. Trials were performed in wines artificially supplemented (at a final concentration of 10 µg/L) with OTA. The most effective fining agent in removing OTA (80%) from white wine was a commercial formulation that contains gelatine, bentonite and activated carbon. Removals between 10-30% were obtained with potassium caseinate, yeast cell walls and pea protein. With bentonites, carboxymethylcellulose, polyvinylpolypyrrolidone and chitosan no considerable OTA removal was verified. In red wine, removals between 6-19% were obtained with egg albumin, yeast cell walls, pea protein, isinglass, gelatine, polyvinylpolypyrrolidone and chitosan. The most effective fining agents in removing OTA from red wine were an activated carbon (66%) followed again by the commercial formulation (55%), being activated carbon a well-known adsorbent of mycotoxins. These results may provide useful information for winemakers, namely for the selection of the most appropriate oenological product for OTA removal, reducing wine toxicity and simultaneously enhancing food safety and wine quality.

Analytical evaluation of fining treatments for white wines contaminated by Ochratoxin A

Mycotoxins are toxic secondary metabolites produced by certain molds. Ochratoxin A (OTA) is one of the most relevant. Its chemical structure is a dihydro-isocoumarin connected at the 7-carboxy group to a molecule of L--phenylalanine via an amide bond. OTA in wine is a risk to consumer health [1]. According to the Regulation No. 123/2005 of the European Commission, the maximum limit for OTA in wine is 2 µg/kg [2]. Then, it is important to control its occurrence. So, the aim of this work was to know the effect of different fining agents on OTA removal from white wine.

Food safety in wine: removal of ochratoxin A in contaminated white wine using commercial fining agents

The presence of mycotoxins in foodstuff is a matter of concern for food safety. Mycotoxins are toxic secondary metabolites produced by certain molds, being ochratoxin A (OTA) one of the most relevant. Wines can also be contaminated with these toxicants. Several authors have demonstrated the presence of mycotoxins in wine, especially ochratoxin A (OTA) [1]. Its chemical structure is a dihydro-isocoumarin connected at the 7-carboxy group to a molecule of L--phenylalanine via an amide bond. As these toxicants can never be completely removed from the food chain, many countries have defined levels in food in order to attend health concerns. OTA contamination of wines might be a risk to consumer health, thus requiring treatments to achieve acceptable standards for human consumption [2]. The maximum acceptable level of OTA in wines is 2.0 g/kg according to the Commission regulation No. 1881/2006 [3]. Therefore, the aim of this work was to reduce OTA to safer levels using different fining agents, as well as their impact on white wine physicochemical characteristics. To evaluate their efficiency, 11 commercial fining agents (mineral, synthetic, animal and vegetable proteins) were used to get new approaches on OTA removal from white wine. Trials (including a control without addition of a fining agent) were performed in white wine artificially supplemented with OTA (10 µg/L). OTA analysis were performed after wine fining. Wine was centrifuged at 4000 rpm for 10 min and 1 mL of the supernatant was collected and added of an equal volume of acetonitrile/methanol/acetic acid (78:20:2 v/v/v). Also, the solid fractions obtained after fining, were centrifuged (4000 rpm, 15 min), the resulting supernatant discarded, and the pellet extracted with 1 mL of the above solution and 1 mL of H2O. OTA analysis was performed by HPLC with fluorescence detection according to Abrunhosa and Venâncio [4]. The most effective fining agent in removing OTA (80%) from white wine was a commercial formulation that contains gelatine, bentonite and activated carbon. Removals between 10-30% were obtained with potassium caseinate, yeast cell walls and pea protein. With bentonites, carboxymethylcellulose, polyvinylpolypyrrolidone and chitosan no considerable OTA removal was verified. Following, the effectiveness of seven commercial activated carbons was also evaluated and compared with the commercial formulation that contains gelatine, bentonite and activated carbon. The different activated carbons were applied at the concentration recommended by the manufacturer in order to evaluate their efficiency in reducing OTA levels. Trial and OTA analysis were performed as explained previously. The results showed that in white wine all activated carbons except one reduced 100% of OTA. The commercial formulation that contains gelatine, bentonite and activated carbon (C8) reduced only 73% of OTA concentration. These results may provide useful information for winemakers, namely for the selection of the most appropriate oenological product for OTA removal, reducing wine toxicity and simultaneously enhancing food safety and wine quality.

Crosscorrelation properties between perfect sequences

Binary sequence, perfect sequence, autocorrelation, crosscorrelation, Hadamard transform

Hand gesture spotting and recognition using HMMs and CRFs in color image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010

Vision-based representation and recognition of human activities in image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013

Low autocorrelation sequences and flat polynomials

Magdeburg, Univ., Fak. für Mathematik, Habil.-Schr., 2014

Pushing Upward

Combined media on paper. 90" x 40", Fire River Series

A nonlinear threshold model for the dependence of extremes of stationary sequences

One of the main implications of the efficient market hypothesis (EMH) is that expected future returns on financial assets are not predictable if investors are risk neutral. In this paper we argue that financial time series offer more information than that this hypothesis seems to supply. In particular we postulate that runs of very large returns can be predictable for small time periods. In order to prove this we propose a TAR(3,1)-GARCH(1,1) model that is able to describe two different types of extreme events: a first type generated by large uncertainty regimes where runs of extremes are not predictable and a second type where extremes come from isolated dread/joy events. This model is new in the literature in nonlinear processes. Its novelty resides on two features of the model that make it different from previous TAR methodologies. The regimes are motivated by the occurrence of extreme values and the threshold variable is defined by the shock affecting the process in the preceding period. In this way this model is able to uncover dependence and clustering of extremes in high as well as in low volatility periods. This model is tested with data from General Motors stocks prices corresponding to two crises that had a substantial impact in financial markets worldwide; the Black Monday of October 1987 and September 11th, 2001. By analyzing the periods around these crises we find evidence of statistical significance of our model and thereby of predictability of extremes for September 11th but not for Black Monday. These findings support the hypotheses of a big negative event producing runs of negative returns in the first case, and of the burst of a worldwide stock market bubble in the second example. JEL classification: C12; C15; C22; C51 Keywords and Phrases: asymmetries, crises, extreme values, hypothesis testing, leverage effect, nonlinearities, threshold models

Segmentation and Clustering of Textual Sequences: a Typological Approach

The long term goal of this research is to develop a program able to produce an automatic segmentation and categorization of textual sequences into discourse types. In this preliminary contribution, we present the construction of an algorithm which takes a segmented text as input and attempts to produce a categorization of sequences, such as narrative, argumentative, descriptive and so on. Also, this work aims at investigating a possible convergence between the typological approach developed in particular in the field of text and discourse analysis in French by Adam (2008) and Bronckart (1997) and unsupervised statistical learning.