Chicken acidic leucine-rich EGF-like domain containing brain protein (CALEB), a neural member of the EGF family of differentiation factors, is implicated in neurite formation

Chicken acidic leucine-rich EGF-like domain containing brain protein (CALEB) was identified by combining binding assays with immunological screens in the chicken nervous system as a novel member of the EGF family of differentiation factors. cDNA cloning indicates that CALEB is a multidomain protein that consists of an NH2-terminal glycosylation region, a leucine-proline-rich segment, an acidic box, a single EGF-like domain, a transmembrane, and a short cytoplasmic stretch. In the developing nervous system, CALEB is associated with glial and neuronal surfaces. CALEB is composed of a 140/130-kD doublet, an 80-kD band, and a chondroitinsulfate-containing 200-kD component. The latter two components are expressed in the embryonic nervous system and are downregulated in the adult nervous system. CALEB binds to the extracellular matrix glycoproteins tenascin-C and -R. In vitro antibody perturbation experiments reveal a participation of CALEB in neurite formation in a permissive environment.     

A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex

The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a pulsed neural network. In presenting such a model, a formal connection of the fields of Time Frequency Analysis and Pulsed Neural Networks is established. The resulting neural time-frequency signal representation is shown to be representable as a signal-dependent overcomplete dictionary. It is derived from neural population coding. Signal decomposition and filtering effects are presented, indicating obvious technical applications of the proposed model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Smart polyoxometalate-based nitrogen monoxide sensors

An electrochemical sensor design for selective NO detection is presented based on a polyoxometalate (POM) cluster immobilized on an electrode through a polyelectrolyte matrix. It is suggested that the POM can electrocatalyze the reduction of NO. The reduction current is proportional to the NO concentration in the investigated concentration window ranging from 1 nM to 10 microM. The sensitivity of the device can be adjusted by the number of immobilized layers. The response to possible interfering reagents such as nitrate and nitrite can be controlled through the multilayer design. By a predominant negatively charged outer surface, the response to these ions is markedly reduced.     

Modeling Human Judgment of Digital Imagery for Multimedia Retrieval

The application of machine learning techniques to image and video search has been shown to boost the performance of multimedia retrieval systems, and promises to lead to more generalized semantic search approaches. In particular, the availability of large training collections allows model-driven search using a substantial number of semantic concepts. The training collections are obtained in a manual annotation process where human raters review images and assign predefined semantic concept labels. Besides being prone to human error, manual image annotation is biased by the view of the individual annotator because visual information almost always leaves room for ambiguity. Ideally, several independent judgments are obtained per image, and the inter-rater agreement is assessed. While disagreement between ratings bears valuable information on the annotation quality, it complicates the task of clearly classifying rated images based on multiple judgments. In the absence of a gold standard, evaluating multiple judgments and resolving disagreement between raters is not trivial. In this paper, we present an approach using latent structure analysis to solve this problem. We apply latent class modeling to the annotation data collected during the TRECVID 2005 Annotation Forum, and demonstrate how to use this statistic to clearly classify each image on the basis of varying numbers of ratings. We use latent class modeling to quantify the annotation quality and discuss the results in comparison with the well-known Kappa inter-rater agreement measure.     

3D characterization of microstructured poly(methacrylic acid) thin films via Mach-Zehnder interference microscopy

We demonstrate the adaption of a further developed Mach-Zehnder interference (MZI) microscope for the rapid 3D characterization of transparent microstructured polymer thin films. In order to quantify the accuracy of the Mach-Zehnder interferometer, comparative film thickness measurements of photolithographically patterned poly(methacrylic acid) polymer brushes are performed employing two alternative techniques: white light profilometry (WIM) and atomic force microscopy (AFM). When the refractive index of the polymer brushes is calculated from MZI data, we obtain a good agreement with results received from an independent method (ellipsometry).In contrast to surface probing techniques such as AFM or WIM, Mach-Zehnder interferometry is a transmitted light method that measures both surface height profiles and refractive index distributions. MZI thus enables the quantification of film homogeneity with respect to height and density variations at the lateral resolution of a refraction limited microscope. We conclude that MZI is an adequate tool for the rapid and non-destructive characterization of structured polymer thin films. This method should be particularly useful for production quality control of microstructured polymer thin films which possess great potential in electronic device fabrication and biotechnology.     

Evaluating The Extent of Bio-Polyester Polymerization in Solid Wood by Thermogravimetric Analysis

In this study, the thermogravimetric analysis (TGA) technique has been applied to determine the extent of in situ polymerization achievable in solid wood on treating with bio-polyester oligomers. Low-molecular-weight oligomers of poly(lactic acid) (PLA), poly(glycolic acid) (PGA), poly(butylene succinate) (PBS), and poly(butylene adipate) (PBA) were impregnated and then thermally polymerized within solid wood to enhance the physical properties of wood. TGA revealed a similar degree of oligomer polymerization was achieved either with pure oligomers or within the wood structure. The influence of relatively acidic treatments, such as low-molecular-weight PGA oligomers, was observed to lead to degradation of the hemicellulose wood component. Polymerization of PLA and PGA oligomer treatments which penetrate the wood cell wall gave relatively lower wood thermal stability. Treatment and polymerization of lumen filling PBS and PBA oligomers contributed to increased wood thermal stability.     

Effect of size and shape of metal particles to improve hardness and electrical properties of carbon nanotube reinforced copper and copper alloy composites

Utilizing the extra-ordinary properties of carbon nanotube (CNT) in metal matrix composite (MMC) for macroscopic applications is still a big challenge for science and technology. Very few successful attempts have been made for commercial applications due to the difficulties incorporating CNTs in metals with up-scalable processes. CNT reinforced copper and copper alloy (bronze) composites have been fabricated by well-established hot-press sintering method of powder metallurgy. The parameters of CNT–metal powder mixing and hot-press sintering have been optimized and the matrix materials of the mixed powders and composites have been evaluated. However, the effect of shape and size of metal particles as well as selection of carbon nanotubes has significant influence on the mechanical and electrical properties of the composites. The hardness of copper matrix composite has improved up to 47% compared to that of pure copper, while the electrical conductivity of bronze composite has improved up to 20% compared to that of the pure alloy. Thus carbon nanotube can improve the mechanical properties of highly-conductive low-strength copper metals, whereas in low-conductivity high-strength copper alloys the electrical conductivity can be improved.