Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.

Infection rate of Babesia spp. sporokinetes in engorged Boophilus microplus from an area of enzootic stability in the State of Minas Gerais, Brazil

The infection rates of Babesia sporokinetes in engorged Boophilus microplus were evaluated during a 2-year period in a dairy farm located in an area of enzootic stability. Every 14 days engorged females were collected from calves and from adult animals. Ticks were incubated at 27 ± 0.5ºC and 80-90% relative humidity and Babesia infection rates were determined by microscopic examination of Giemsa-stained hemolymph smears. After 52 collections, 2105 ticks were obtained, from which 982 were collected from calves and 1123 from cows. The total Babesia infection rate was 10%, however the incidence was higher (p < 0.05) in ticks collected from calves (17.5%) than in those collected from cows (3.6%). Females collected from cows showed the highest infection rates in January, March, and August, and absence of infection in April and May. Ticks feeding on calves were infected throughout the experimental period. The infection rates of engorged females collected from naturally infected calves that were artificially infested with Babesia-free-larvae of B. microplus gradually decreased until the calves were four months old. No differences were observed among infection rates of ticks collected from calves maintained under natural conditions.

Therapeutic drug monitoring of imatinib: Bayesian and alternative methods to predict trough levels

Background: The imatinib trough plasma concentration (C(min)) correlates with clinical response in cancer patients. Therapeutic drug monitoring (TDM) of plasma C(min) is therefore suggested. In practice, however, blood sampling for TDM is often not performed at trough. The corresponding measurement is thus only remotely informative about C(min) exposure. Objectives: The objectives of this study were to improve the interpretation of randomly measured concentrations by using a Bayesian approach for the prediction of C(min), incorporating correlation between pharmacokinetic parameters, and to compare the predictive performance of this method with alternative approaches, by comparing predictions with actual measured trough levels, and with predictions obtained by a reference method, respectively. Methods: A Bayesian maximum a posteriori (MAP) estimation method accounting for correlation (MAP-ρ) between pharmacokinetic parameters was developed on the basis of a population pharmacokinetic model, which was validated on external data. Thirty-one paired random and trough levels, observed in gastrointestinal stromal tumour patients, were then used for the evaluation of the Bayesian MAP-ρ method: individual C(min) predictions, derived from single random observations, were compared with actual measured trough levels for assessment of predictive performance (accuracy and precision). The method was also compared with alternative approaches: classical Bayesian MAP estimation assuming uncorrelated pharmacokinetic parameters, linear extrapolation along the typical elimination constant of imatinib, and non-linear mixed-effects modelling (NONMEM) first-order conditional estimation (FOCE) with interaction. Predictions of all methods were finally compared with 'best-possible' predictions obtained by a reference method (NONMEM FOCE, using both random and trough observations for individual C(min) prediction). Results: The developed Bayesian MAP-ρ method accounting for correlation between pharmacokinetic parameters allowed non-biased prediction of imatinib C(min) with a precision of ±30.7%. This predictive performance was similar for the alternative methods that were applied. The range of relative prediction errors was, however, smallest for the Bayesian MAP-ρ method and largest for the linear extrapolation method. When compared with the reference method, predictive performance was comparable for all methods. The time interval between random and trough sampling did not influence the precision of Bayesian MAP-ρ predictions. Conclusion: Clinical interpretation of randomly measured imatinib plasma concentrations can be assisted by Bayesian TDM. Classical Bayesian MAP estimation can be applied even without consideration of the correlation between pharmacokinetic parameters. Individual C(min) predictions are expected to vary less through Bayesian TDM than linear extrapolation. Bayesian TDM could be developed in the future for other targeted anticancer drugs and for the prediction of other pharmacokinetic parameters that have been correlated with clinical outcomes.

Differences in the stability of the plasmids of Yersinia pestis cultures in vitro: impact on virulence

Plasmid and chromosomal genes encode determinants of virulence for Yersinia pestis, the causative agent of plague. However, in vitro, Y. pestis genome is very plastic and several changes have been described. To evaluate the alterations in the plasmid content of the cultures in vitro and the impact of the alterations to their pathogenicity, three Y. pestis isolates were submitted to serial subculture, analysis of the plasmid content, and testing for the presence of characteristic genes in each plasmid of colonies selected after subculture. Different results were obtained with each strain. The plasmid content of one of them was shown to be stable; no apparent alteration was produced through 32 subcultures. In the other two strains, several alterations were observed. LD50 in mice of the parental strains and the derived cultures with different plasmid content were compared. No changes in the virulence plasmid content could be specifically correlated with changes in the LD50.

High constant incidence rates of second primary cancers of the head and neck: a pooled analysis of 13 cancer registries.

Scanty data are available on the incidence (i.e., the absolute risk) of second cancers of the head and neck (HN) and its pattern with age. We investigated this issue using data from a multicentric study of 13 population-based cancer registries from Europe, Canada, Australia and Singapore for the years 1943-2000. A total of 99,257 patients had a first primary HN cancer (15,985 tongue, 22,378 mouth, 20,758 pharyngeal, and 40,190 laryngeal cancer), contributing to 489,855 person-years of follow-up. A total of 1,294 of the patients (1.3%) were diagnosed with second HN cancers (342 tongue, 345 mouth, 418 pharynx and 189 larynx). Male incidence rates of first HN cancer steeply increased from 0.68/100,000 at age 30-34 to 46.2/100,000 at age 70-74, and leveled off at older age; female incidence increased from 0.50/100,000 at age 30-34 to 16.5/100,000 at age 80-84. However, age-specific incidence of second HN cancers after a first HN cancer in men was around 200-300/100,000 between age 40-44 and age 70-74 and tended to decline at subsequent ages (150/100,000 at age 80-84); in women, incidence of second HN cancers was around 200-300/100,000 between age 45-49 and 80-84. The patterns of age-specific incidence were consistent for different subsites of second HN cancer and sexes; moreover, they were similar for age-specific incidence of first primary HN cancer in patients who subsequently developed a second HN cancer. The incidence of second HN cancers does not increase with age, but remains constant, or if anything, decreases with advancing age.

Improving binding affinity and stability of peptide ligands by substituting glycines with D-amino acids.

Improving the binding affinity and/or stability of peptide ligands often requires testing of large numbers of variants to identify beneficial mutations. Herein we propose a type of mutation that promises a high success rate. In a bicyclic peptide inhibitor of the cancer-related protease urokinase-type plasminogen activator (uPA), we observed a glycine residue that has a positive ϕ dihedral angle when bound to the target. We hypothesized that replacing it with a D-amino acid, which favors positive ϕ angles, could enhance the binding affinity and/or proteolytic resistance. Mutation of this specific glycine to D-serine in the bicyclic peptide indeed improved inhibitory activity (1.75-fold) and stability (fourfold). X-ray-structure analysis of the inhibitors in complex with uPA showed that the peptide backbone conformation was conserved. Analysis of known cyclic peptide ligands showed that glycine is one of the most frequent amino acids, and that glycines with positive ϕ angles are found in many protein-bound peptides. These results suggest that the glycine-to-D-amino acid mutagenesis strategy could be broadly applied.