A learning approach to 3d object representation for classification

Abstract. In this paper we describe our 3D object signature for 3D object classification. The signature is based on a learning approach that finds salient points on a 3D object and represent these points in a 2D spatial map based on a longitude-latitude transformation. Experimental results show high classification rates on both pose-normalized and rotated objects and include a study on classification accuracy as a function of number of rotations in the training set

Retrieval of 3D polygonal objects based on multiresolution signatures

In this paper we present a method for retrieving 3D polygonal objects by using two sets of multiresolution signatures. Both sets are based on the progressive elimination of object's details by iterative processing of the 3D meshes. The first set, with five parameters, is based on mesh smoothing. This mainly affects an object's surface. The second set, with three parameters, is based on difference volumes after successive mesh erosions and dilations. Characteristic feature vectors are constructed by combining the features at three mesh resolutions of each object. In addition to being invariant to mesh resolution, the feature vectors are invariant to translation, rotation and size of the objects. The method was tested on a set of 40 complex objects with mesh resolutions different from those used in constructing the feature vectors. By using all eight features, the average ranking rate obtained was 1.075: 37 objects were ranked first and only 3 objects were ranked second. Additional tests were carried out to determine the significance of individual features and all combinations. The same ranking rate of 1.075 can be obtained by using some combinations of only three features. © 2011 Springer-Verlag

Redox-Dependent Stability, Protonation, and Reactivity of Cysteine-Bound Heme Proteins

Cysteine-bound hemes are key components of many enzymes and biological sensors. Protonation (deprotonation) of the Cys ligand often accompanies redox transformations of these centers. To characterize these phenomena, we have engineered a series of Thr78Cys/Lys79Gly/Met80X mutants of yeast cytochrome c (cyt c) in which Cys78 becomes one of the axial ligands to the heme. At neutral pH, the protonation state of the coordinated Cys differs for the ferric and ferrous heme species, with Cys binding as a thiolate and a thiol, respectively. Analysis of redox-dependent stability and alkaline transitions of these model proteins, as well as comparisons to Cys binding studies with the minimalist heme peptide microperoxidase-8, demonstrate that the protein scaffold and solvent interactions play important roles in stabilizing a particular Cys–heme coordination. The increased stability of ferric thiolate compared with ferrous thiol arises mainly from entropic factors. This robust cyt c model system provides access to all four forms of Cys-bound heme, including the ferric thiol. Protein motions control the rates of heme redox reactions, and these effects are amplified at low pH, where the proteins are less stable. Thermodynamic signatures and redox reactivity of the model Cys-bound hemes highlight the critical role of the protein scaffold and its dynamics in modulating redox-linked transitions between thiols and thiolates

Group B Streptococcus pilus sortase regulation: a single mutation in the lid region induces pilin protein polymerization in vitro

Gram-positive bacteria build pili on their cell surface via a class C sortase-catalyzed transpeptidation mechanism from pilin protein substrates. Despite the availability of several crystal structures, pilusrelated C sortases remain poorly characterized to date, and their mechanisms of transpeptidation and regulation need to be further investigated. The available 3-dimensional structures of these enzymes reveal a typical sortase fold, except for the presence of a unique feature represented by an N-terminal highly flexible loop known as the "lid." This region interacts with the residues composing the catalytic triad and covers the active site, thus maintaining the enzyme in an autoinhibited state and preventing the accessibility to the substrate. It is believed that enzyme activation may occur only after lid displacement from the catalytic domain. In this work, we provide the first direct evidence of the regulatory role of the lid, demonstrating that it is possible to obtain in vitro an efficient polymerization of pilin subunits using an active C sortase lid mutant carrying a single residue mutation in the lid region. Moreover, biochemical analyses of this recombinant mutant reveal that the lid confers thermodynamic and proteolytic stability to the enzyme

Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components

TESTLoc: protein subcellular localization prediction from EST data

Abstract Background The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes. Results We developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%). Conclusions TESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html</p

Body Fat Free Mass Is Associated with the Serum Metabolite Profile in a Population-Based Study

To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study. Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs). We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism. A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network