Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

Biologically Inspired Features for Scene Classification in Video Surveillance

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.

Detection of neurotransmitters by a light scattering technique based on seed-mediated growth of gold nanoparticles

A simple light scattering detection method for neurotransmitters has been developed, based on the growth of gold nanoparticles. Neurotransmitters (dopamine, L-dopa, noradrenaline and adrenaline) can effectively function as active reducing agents for generating gold nanoparticles, which result in enhanced light scattering signals. The strong light scattering of gold nanoparticles then allows the quantitative detection of the neurotransmitters simply by using a common spectrofluorometer.

Seed-Mediated Growth of Nearly Monodisperse Palladium Nanocubes with Controllable Sizes

Nearly monodisperse Pd nanocubes with controllable sizes were synthesized through a seed-mediated growth approach. By using Pd nanocubes of 22 nm in size as seeds, the morphology of the as-grown nanostructures was fixed as single-crystalline, which enabled us to rationally tune the size of Pd nanocubes. The formation mechanism of initial 22 nm nanocubes was also discussed. The size-dependent surface plasmon resonance properties of the as-synthesized Pd nanocubes were investigated. Compared with previous methods, the yield, monodispersity, perfection of the shape formation, and the range of size control of these nanocubes are all improved.

Seed-mediated synthesis of branched gold nanoparticles with the assistance of citrate and their surface-enhanced Raman scattering properties

We report an easy synthesis of highly branched gold particles through a seed-mediated growth approach in the presence of citrate. The addition of citrate in the growth solution is found to be crucial for the formation of these branched gold particles. Their size can be varied from 47 to 185 nm. The length of the thumb-like branch is estimated to be between about 5 and 20 nm, and changes slightly as the particle size increases. Owing to these obtuse and short branches, their surface plasmon resonance displays a marked red-shift with respect to the normal spherical particles. These branched gold particles exhibit stronger SERS activity than the non-branched ones, which is most likely related to these unique branching features.

Simple and convenient preparation of Au-Pt core-shell nanoparticles on surface via a seed growth method

Au-Pt core-shell nanoparticles were prepared on glass surface by a seed growth method. Gold nanoparticles were used as seeds and ascorbic acid-H2PtCL6 solutions as growth solutions to deposit Pt shell on the surface of gold nanoparticles. These core-shell nanoparticles and their growth process were examined by UV-Vis spectroscopy, X-ray photoelectron spectroscopy, atomic force microscopy and field-emission environmental scanning electron microscopy and the results indicated that the deposition speed was fast and nanoparticles with obvious core-shell structure could be obtained after 2 min. Moreover, this seed growth method for preparation of the core-shell nanoparticles is simple and convenient compared with other seed growth methods with NH4OH as a mild reductant. In addition, electrochemical experiments indicated that these Au-Pt core-shell nanoparticles had similar electrochemical properties to those of the bulk Pt electrode.

Seed-mediated growth of large, monodisperse core-shell gold-silver nanoparticles with Ag-like optical properties

Large, monodisperse core-shell Au-Ag nanoparticles with Ag-like optical properties have been prepared by the seeding growth method in micellar media.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.

PenBase, the shrimp antimicrobial peptide penaeidin database: Sequence-based classification and recommended nomenclature

Antimicrobial peptides play a major role in innate immunity. The penaeidins, initially characterized from the shrimp Litopenaeus vannamei, are a family of antimicrobial peptides that appear to be expressed in all penaeid shrimps. As of recent, a large number of penaeid nucleotide sequences have been identified from a variety of penaeid shrimp species and these sequences currently reside in several databases under unique identifiers with no nomenclatural continuity. To facilitate research in this field and avoid potential confusion due to a diverse number of nomenclatural designations, we have made a systematic effort to collect, analyse, and classify all the penaeidin sequences available in every database. We have identified a common penaeidin signature and subsequently established a classification based on amino acid sequences. In order to clarify the naming process, we have introduced a 'penaeidin nomenclature' that can be applied to all extant and future penaeidins. A specialized database, PenBase, which is freely available at http://www.penbase.immunaqua.com, has been developed for the penaeidin family of antimicrobial peptides, to provide comprehensive information about their properties, diversity and nomenclature. (c) 2005 Elsevier Ltd. All rights reserved.