CfLGBP, a pattern recognition receptor in Chlamys farreri involved in the immune response against various bacteria

Lipopolysaccharide and beta-1, 3-glucan binding protein (LGBP) is a kind of pattern recognition receptor, which can recognize and bind LPS and beta-1, 3-glucan, and plays curial roles in the innate immune defense against Gram-negative bacteria and fungi. In this study, the functions of LGBP from Zhikong scallop Chlamys farreri performed in innate immunity were analyzed. Firstly, the mRNA expression of CfLGBP in hemocytes toward three typical PAMPS stimulation was examined by realtime PCR. It was up-regulated extremely (P < 0.01) post stimulation of LPS and beta-glucan, and also exhibited a moderate up-regulation (P < 0.01) after PGN injection. Further PAMPs binding assay with the polyclonal antibody specific for CfLGBP proved that the recombinant CfLGBP (designated as rCfLGBP) could bind not only LPS and beta-glucan, but also PGN in vitro. More importantly, rCfLGBP exhibited obvious agglutination activity towards Gram-negative bacteria Escherichia coil, Gram-positive bacteria Bacillus subtilis and fungi Pichia pastoris. Taking the results of immunofluorescence assay into account, which displayed CfLGBP was expressed specifically in the immune cells (hemocytes) and vulnerable organ (gill and mantle), we believed that LGBP in C farreri, serving as a multi-functional PRR, not only involved in the immune response against Gram-negative and fungi as LGBP in other invertebrates, but also played significant role in the event of anti-Gram-positive bacteria infection. As the first functional research of LGBP in mollusks, our study provided new implication into the innate immune defense mechanisms of C. farreri and mollusks. (C) 2010 Elsevier Ltd. All rights reserved.

Peptidoglycan recognition protein of Chlamys farreri (CfPGRP-S1) mediates immune defenses against bacterial infection

Peptidoglycan recognition protein (PGRP) is an essential molecule in innate immunity for both invertebrates and vertebrates, owing to its prominent ability in detecting and eliminating the invading bacteria. Several PGRPs have been identified from mollusk, but their functions and the underlined mechanism are still unclear. In the present study, the mRNA expression profiles, location, and possible functions of PGRP-S1 from Zhikong scallop Chlamys farreri (CfPG RP-St) were analyzed. The CfPGRP-S1 protein located in the mantle, gill, kidney and gonad of the scallops. Its mRNA expression in hemocytes was up-regulated extremely after PGN stimulation (P < 0.01), while moderately after the stimulations of LPS (P < 0.01) and beta-glucan (P < 0.05). The recombinant protein of CfPGRP-S1 (designated as rCfPGRP-S1) exhibited high affinity to PGN and moderate affinity to LPS, but it did not bind beta-glucan. Meanwhile, rCfPGRP-S1 also exhibited strong agglutination activity to Gram-positive bacteria Micrococcus luteus and Bacillus subtilis and weak activity to Gram-negative bacteria Escherichia coli. More importantly, rCfPGRP-S1 functioned as a bactericidal amidase to degrade PGN and strongly inhibit the growth of E. coli and Staphyloccocus aureus in the presence of Zn2+. These results indicated that CfPGRP-S1 could not only serve as a pattern recognition receptor recognizing bacterial PGN and LPS, but also function as a scavenger involved in eliminating response against the invaders. (C) 2010 Elsevier Ltd. All rights reserved.

Cflec-5, a pattern recognition receptor in scallop Chlamys farreri agglutinating yeast Pichia pastoris

C-type lectins are a superfamily of carbohydrate-recognition proteins which play crucial roles as pattern recognition receptors (PRRs) in the innate immunity. In this study, the full-length cDNA of a C-type lectin was cloned from scallop Chlamys farreri (designated as Cflec-5) by expression sequence tag (EST) analysis and rapid amplification of cDNA ends (RACE) approach The full-length cDNA of Cflec-5 was of 1412 bp. The open reading frame encoded a polypeptide of 153 amino acids, including a signal sequence and a conserved carbohydrate-recognition domain with the EPN motif determining the mannose-binding specificity The deduced amino acid sequence of Cflec-5 showed high similarity to members of C-type lectin superfamily. The quantitative real-time PCR was performed to investigate the tissue distribution of Cflec-5 mRNA and its temporal expression profiles in hemocytes post pathogen-associated molecular patterns (PAMPs) stimulation. In healthy scallops, the Cflec-5 mRNA was mainly detected in gill and mantle, and marginally in other tissues The mRNA expression of Cflec-5 could be significantly induced by lipopolysaccharide (LPS) and glucan stimulation and reached the maximum level at 6 h and 12 h, respectively But its expression level did not change significantly during peptidoglycan (PGN) stimulation The function of Cflec-5 was investigated by recombination and expression of the cDNA fragment encoding its mature peptide in Escherichia coli Rosetta Gami (DE3) The recombinant Cflec-5 agglutinated Pichia pastoris in a calcium-independent way The agglutinating activity could be inhibited by D-mannose. LPS and glucan, but not by D-galactose or PGN. These results collectively suggested that Cflec-5 was involved in the innate Immune response of scallops and might contribute to nonself-recognition through its interaction with various PAMPs (C) 2010 Elsevier Ltd All rights reserved

Computer-assisted automatic peak recognition and result evaluation for analysis of chlorinated hydrocarbons in environmental samples

A data manipulation method has been developed for automatic peak recognition and result evaluation in the analysis of organic chlorinated hydrocarbons with dual-column gas chromatography. Based on the retention times of two internal standards, pentachlorotoluene and decachlorobiphenyl, the retention times of chlorinated hydrocarbons can be calibrated automatically and accurately. It is very convenient to identify the peaks by comparing the retention times of samples with the calibrated retention times calculated from the relative retention indices of standards. Meanwhile, with a suggested two-step evaluation method the evaluation coefficients and the suitable quantitative results of each component can be automatically achieved for practical samples in an analytical system using two columns with different polarities and two internal standards. (C) 2002 Elsevier Science B.V. All rights reserved.

Stimulus Familiarity Determines Recognition Strategy for Novel 3-D Objects

We describe a psychophysical investigation of the effects of object complexity and familiarity on the variation of recognition time and recognition accuracy over different views of novel 3D objects. Our findings indicate that with practice the response times for different views become more uniform and the initially orderly dependency of the response time on the distance to a "good" view disappears. One possible interpretation of our results is in terms of a tradeoff between memory needed for storing specific-view representations of objects and time spent in recognizing the objects.

Recognition by Linear Combinations of Models

Visual object recognition requires the matching of an image with a set of models stored in memory. In this paper we propose an approach to recognition in which a 3-D object is represented by the linear combination of 2-D images of the object. If M = {M1,...Mk} is the set of pictures representing a given object, and P is the 2-D image of an object to be recognized, then P is considered an instance of M if P = Eki=aiMi for some constants ai. We show that this approach handles correctly rigid 3-D transformations of objects with sharp as well as smooth boundaries, and can also handle non-rigid transformations. The paper is divided into two parts. In the first part we show that the variety of views depicting the same object under different transformations can often be expressed as the linear combinations of a small number of views. In the second part we suggest how this linear combinatino property may be used in the recognition process.

Grouping For Recognition

This paper presents a new method of grouping edges in order to recognize objects. This grouping method succeeds on images of both two- and three- dimensional objects. So that the recognition system can consider first the collections of edges most likely to lead to the correct recognition of objects, we order groups of edges based on the likelihood that a single object produced them. The grouping module estimates this likelihood using the distance that separates edges and their relative orientation. This ordering greatly reduces the amount of computation required to locate objects and improves the system's robustness to error.

Automatic Recognition of Tractability in Inference Relations

A procedure is given for recognizing sets of inference rules that generate polynomial time decidable inference relations. The procedure can automatically recognize the tractability of the inference rules underlying congruence closure. The recognition of tractability for that particular rule set constitutes mechanical verification of a theorem originally proved independently by Kozen and Shostak. The procedure is algorithmic, rather than heuristic, and the class of automatically recognizable tractable rule sets can be precisely characterized. A series of examples of rule sets whose tractability is non-trivial, yet machine recognizable, is also given. The technical framework developed here is viewed as a first step toward a general theory of tractable inference relations.

The Effect of Indexing on the Complexity of Object Recognition

Many current recognition systems use constrained search to locate objects in cluttered environments. Previous formal analysis has shown that the expected amount of search is quadratic in the number of model and data features, if all the data is known to come from a sinlge object, but is exponential when spurious data is included. If one can group the data into subsets likely to have come from a single object, then terminating the search once a "good enough" interpretation is found reduces the expected search to cubic. Without successful grouping, terminated search is still exponential. These results apply to finding instances of a known object in the data. In this paper, we turn to the problem of selecting models from a library, and examine the combinatorics of determining that a candidate object is not present in the data. We show that the expected search is again exponential, implying that naﶥ approaches to indexing are likely to carry an expensive overhead, since an exponential amount of work is needed to week out each of the incorrect models. The analytic results are shown to be in agreement with empirical data for cluttered object recognition.

Viewpoint-Specific Representations in Three-Dimensional Object Recognition

We report a series of psychophysical experiments that explore different aspects of the problem of object representation and recognition in human vision. Contrary to the paradigmatic view which holds that the representations are three-dimensional and object-centered, the results consistently support the notion of view-specific representations that include at most partial depth information. In simulated experiments that involved the same stimuli shown to the human subjects, computational models built around two-dimensional multiple-view representations replicated our main psychophysical results, including patterns of generalization errors and the time course of perceptual learning.

An Efficient Correspondence Based Algorithm for 2D and 3D Model Based Recognition

A polynomial time algorithm (pruned correspondence search, PCS) with good average case performance for solving a wide class of geometric maximal matching problems, including the problem of recognizing 3D objects from a single 2D image, is presented. Efficient verification algorithms, based on a linear representation of location constraints, are given for the case of affine transformations among vector spaces and for the case of rigid 2D and 3D transformations with scale. Some preliminary experiments suggest that PCS is a practical algorithm. Its similarity to existing correspondence based algorithms means that a number of existing techniques for speedup can be incorporated into PCS to improve its performance.

Limitations of Non Model-Based Recognition Schemes

Different approaches to visual object recognition can be divided into two general classes: model-based vs. non model-based schemes. In this paper we establish some limitation on the class of non model-based recognition schemes. We show that every function that is invariant to viewing position of all objects is the trivial (constant) function. It follows that every consistent recognition scheme for recognizing all 3-D objects must in general be model based. The result is extended to recognition schemes that are imperfect (allowed to make mistakes) or restricted to certain classes of objects.

Distance Metric Between 3D Models and 3D Images for Recognition and Classification

Similarity measurements between 3D objects and 2D images are useful for the tasks of object recognition and classification. We distinguish between two types of similarity metrics: metrics computed in image-space (image metrics) and metrics computed in transformation-space (transformation metrics). Existing methods typically use image and the nearest view of the object. Example for such a measure is the Euclidean distance between feature points in the image and corresponding points in the nearest view. (Computing this measure is equivalent to solving the exterior orientation calibration problem.) In this paper we introduce a different type of metrics: transformation metrics. These metrics penalize for the deformatoins applied to the object to produce the observed image. We present a transformation metric that optimally penalizes for "affine deformations" under weak-perspective. A closed-form solution, together with the nearest view according to this metric, are derived. The metric is shown to be equivalent to the Euclidean image metric, in the sense that they bound each other from both above and below. For Euclidean image metric we offier a sub-optimal closed-form solution and an iterative scheme to compute the exact solution.

Recognition by Prototypes

A scheme for recognizing 3D objects from single 2D images is introduced. The scheme proceeds in two stages. In the first stage, the categorization stage, the image is compared to prototype objects. For each prototype, the view that most resembles the image is recovered, and, if the view is found to be similar to the image, the class identity of the object is determined. In the second stage, the identification stage, the observed object is compared to the individual models of its class, where classes are expected to contain objects with relatively similar shapes. For each model, a view that matches the image is sought. If such a view is found, the object's specific identity is determined. The advantage of categorizing the object before it is identified is twofold. First, the image is compared to a smaller number of models, since only models that belong to the object's class need to be considered. Second, the cost of comparing the image to each model in a classis very low, because correspondence is computed once for the whoel class. More specifically, the correspondence and object pose computed in the categorization stage to align the prototype with the image are reused in the identification stage to align the individual models with the image. As a result, identification is reduced to a series fo simple template comparisons. The paper concludes with an algorithm for constructing optimal prototypes for classes of objects.