PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

Validating NCAR-CCSM last glacial maximum sea surface temperature in the tropical and South Atlantic with proxy-data

Last Glacial Maximum simulated sea surface temperature from the Paleo-Climate version of the National Center for Atmospheric Research Coupled Climate Model (NCAR-CCSM) are compared with available reconstructions and data-based products in the tropical and south Atlantic region. Model results are compared to data proxies based on the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface product (MARGO). Results show that the model sea surface temperature is not consistent with the proxy-data in all of the region of interest. Discrepancies are found in the eastern, equatorial and in the high-latitude South Atlantic. The model overestimates the cooling in the southern South Atlantic (near 50 degrees S) shown by the proxy-data. Near the equator, model and proxies are in better agreement. In the eastern part of the equatorial basin the model underestimates the cooling shown by all proxies. A northward shift in the position of the subtropical convergence zone in the simulation suggests a compression or/and an equatorward shift of the subtropical gyre at the surface, consistent with what is observed in the proxy reconstruction. (C) 2008 Elsevier B.V. All rights reserved

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Bivariate gamma-geometric law and its induced Levy process

In this article we introduce a three-parameter extension of the bivariate exponential-geometric (BEG) law (Kozubowski and Panorska, 2005) [4]. We refer to this new distribution as the bivariate gamma-geometric (BGG) law. A bivariate random vector (X, N) follows the BGG law if N has geometric distribution and X may be represented (in law) as a sum of N independent and identically distributed gamma variables, where these variables are independent of N. Statistical properties such as moment generation and characteristic functions, moments and a variance-covariance matrix are provided. The marginal and conditional laws are also studied. We show that BBG distribution is infinitely divisible, just as the BEG model is. Further, we provide alternative representations for the BGG distribution and show that it enjoys a geometric stability property. Maximum likelihood estimation and inference are discussed and a reparametrization is proposed in order to obtain orthogonality of the parameters. We present an application to a real data set where our model provides a better fit than the BEG model. Our bivariate distribution induces a bivariate Levy process with correlated gamma and negative binomial processes, which extends the bivariate Levy motion proposed by Kozubowski et al. (2008) [6]. The marginals of our Levy motion are a mixture of gamma and negative binomial processes and we named it BMixGNB motion. Basic properties such as stochastic self-similarity and the covariance matrix of the process are presented. The bivariate distribution at fixed time of our BMixGNB process is also studied and some results are derived, including a discussion about maximum likelihood estimation and inference. (C) 2012 Elsevier Inc. All rights reserved.

Low resolution structural characterization of the Hsp70-interacting protein - Hip - from Leishmania braziliensis emphasizes its high asymmetry

The Hsp70 is an essential molecular chaperone in protein metabolism since it acts as a pivot with other molecular chaperone families. Several co-chaperones act as regulators of the Hsp70 action cycle, as for instance Hip (Hsp70-interacting protein). Hip is a tetratricopeptide repeat protein (TPR) that interacts with the ATPase domain in the Hsp70-ADP state, stabilizing it and preventing substrate dissociation. Molecular chaperones from protozoans, which can cause some neglected diseases, are poorly studied in terms of structure and function. Here, we investigated the structural features of Hip from the protozoa Leishmania braziliensis (LbHip), one of the causative agents of the leishmaniasis disease. LbHip was heterologously expressed and purified in the folded state, as attested by circular dichroism and intrinsic fluorescence emission techniques. LbHip forms an elongated dimer, as observed by analytical gel filtration chromatography, analytical ultracentrifugation and small angle X-ray scattering (SAXS). With the SAXS data a low resolution model was reconstructed, which shed light on the structure of this protein, emphasizing its elongated shape and suggesting its domain organization. We also investigated the chemical-induced unfolding behavior of LbHip and two transitions were observed. The first transition was related to the unfolding of the TPR domain of each protomer and the second transition of the dimer dissociation. Altogether. LbHip presents a similar structure to mammalian Hip, despite their low level of conservation, suggesting that this class of eukaryotic protein may use a similar mechanism of action. (C) 2012 Elsevier Inc. All rights reserved.