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

Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses

An interlaboratory comparison (ILC) was organized with the aim to set up quality control indicators suitable for multicomponent quantitative analysis by nuclear magnetic resonance (NMR) spectroscopy. A total of 36 NMR data sets (corresponding to 1260 NMR spectra) were produced by 30 participants using 34 NMR spectrometers. The calibration line method was chosen for the quantification of a five-component model mixture. Results show that quantitative NMR is a robust quantification tool and that 26 out of 36 data sets resulted in statistically equivalent calibration lines for all considered NMR signals. The performance of each laboratory was assessed by means of a new performance index (named Qp-score) which is related to the difference between the experimental and the consensus values of the slope of the calibration lines. Laboratories endowed with a Qp-score falling within the suitable acceptability range are qualified to produce NMR spectra that can be considered statistically equivalent in terms of relative intensities of the signals. In addition, the specific response of nuclei to the experimental excitation/relaxation conditions was addressed by means of the parameter named NR. NR is related to the difference between the theoretical and the consensus slopes of the calibration lines and is specific for each signal produced by a well-defined set of acquisition parameters

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