A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data

Mass spectrometry from clinical specimens is used in order to identify biomarkers in a diagnosis. Thus, a reliable method for both feature selection and classification is required. A novel method is proposed to find biomarkers in SELDI-TOF in order to perform robust classification.The feature selection is based on a new genetic algorithm. Concerning the classification, a method which takes into account the great variability on intensity by using decision stumps has been developed. Moreover, as the samples are often small, it is more appropriate to use the decision stumps simultaneously than building a complete tree. The thresholds of the decision stumps are determined in the same genetic algorithm. Finally, the method was generalized to more than two groups based on pairwise coupling. The obtained algorithm was applied on two data sets: a publicly available one containing two groups allowing a comparison with other methods from the literature and a new one containing three groups.     

Photo-oxidation in an O2 atmosphere: A powerful tool to elucidate the mechanism of UV-visible light oxidation of polymers - Application to the photodegradation of MDMO-PPV

This article describes a new approach to improve the analysis of the chemical modifications that result from the degradation of polymers under UV-visible light exposure in the presence of oxygen. The tool which is used consists of an irradiation chamber whose atmosphere is composed of ¹⁸O₂. The ¹⁸O₂ pressure inside the chamber and the hygrometry can be adjusted. In this study, particular attention was paid to the photo-oxidation of poly[2-methoxy-5-(3′,7′-dimethyloctyloxy)-1,4-phenylenevinylene] (MDMO-PPV). Using infrared spectroscopy, irradiation under the oxygen 18 atmosphere allowed discrimination between compounds formed via fixation of oxygen from the atmosphere and those formed by reorganisation of the matrix. In addition, irradiation of MDMO-PPV in an ¹⁸O₂ wet atmosphere allowed for detection of the presence of phenyl formate and aromatic ketone functions. This permitted validating the mechanism of photo-oxidation previously proposed. Additionally, with regard to blends made with MDMO-PPV and methano-fullerene[6,6]-phenyl C₆₁-butyric acid methyl ester ([60] PCBM), it was demonstrated that this technique facilitates the identification of the photo-oxidation products formed.     

Antimicrobial effect of cranberry juice and extracts

The antimicrobial effect of cranberry juice and of three cranberry extracts (water-soluble (E1) and apolar phenolic compounds (E2), and anthocyanins (E3)) was investigated against seven bacterial strains (Enterococcus faecium resistant to vancomycin (ERV), Escherichia coli O157:H7 EDL 933, Escherichia coli ATCC 25922, Listeria monocytogenes HPB 2812, Pseudomonas aeruginosa ATCC 15442, Salmonella Typhimurium SL1344, and Staphylococcus aureus ATCC 29213). Each cranberry sample was analyzed to determine the minimum inhibitory concentration (MIC) and the maximal tolerated concentration (MTC) at neutral pH. The results, reported in μg phenol/mL, indicated that all the bacterial strains, both Gram-positive and Gram-negative, were selectively inhibited by the cranberry phenolic compounds. The extract rich in water-soluble phenolic compounds caused the most important growth inhibitions. The bacteria ERV, and to a lesser degree, P. aeruginosa, S. aureus and E. coli ATCC 25922, were the most sensitive to the antimicrobial activity of extract E1. The growth of P. aeruginosa and E. coli ATCC was also affected by the presence of the anthocyanin-rich cranberry extract E3, although the observed antibacterial effect was not as important as with extract E1. In general, L. monocytogenes, E. coli O157:H7 and S. Typhimurium were the most resistant to the antibacterial activity of the cranberry extracts. Within 30 min of exposure with pure neutralized cranberry juice, L. monocytogenes and ERV were completely inactivated.     

Dose calculations using artificial neural networks: A feasibility study for photon beams

Direct dose calculations are a crucial requirement for Treatment Planning Systems. Some methods, such as Monte Carlo, explicitly model particle transport, others depend upon tabulated data or analytic formulae. However, their computation time is too lengthy for clinical use, or accuracy is insufficient, especially for recent techniques such as Intensity-Modulated Radiotherapy. Based on artificial neural networks (ANNs), a new solution is proposed and this work extends the properties of such an algorithm and is called NeuRad.Prior to any calculations, a first phase known as the learning process is necessary. Monte Carlo dose distributions in homogeneous media are used, and the ANN is then acquired. According to the training base, it can be used as a dose engine for either heterogeneous media or for an unknown material. In this report, two networks were created in order to compute dose distribution within a homogeneous phantom made of an unknown material and within an inhomogeneous phantom made of water and TA6V4 (titanium alloy corresponding to hip prosthesis).All NeuRad results were compared to Monte Carlo distributions. The latter required about 7 h on a dedicated cluster (10 nodes). NeuRad learning requires between 8 and 18 h (depending upon the size of the training base) on a single low-end computer. However, the results of dose computation with the ANN are available in less than 2 s, again using a low-end computer, for a 150×1×150 voxels phantom. In the case of homogeneous medium, the mean deviation in the high dose region was less than 1.7%. With a TA6V4 hip prosthesis bathed in water, the mean deviation in the high dose region was less than 4.1%.Further improvements in NeuRad will have to include full 3D calculations, inhomogeneity management and input definitions.     

Molecular Orientational Ordering in Solid C60

Single-crystal X-ray diffraction studies of the orientational ordering in solid C₆₀ are reviewed. The temperature dependence of selected Bragg reflections was carefully examined, allowing to obtain original information on the first-order orientational ordering transition at T_o = 259K, the unusual behavior of both fundamental and superstructure reflections below T_o and the freezing in of the C₆₀ reorientations at T_g ≈ 85K. The diffuse intensity due to orientational disorder of the C₆₀ molecules at room temperature is found to be strongly modulated, both radially and azimuthally. showing that the molecular orientations are indeed correlated. The corresponding intensity distribution has been calculated within a mean-field theory for different microscopic models of inter molecular interactions. It should allow a better understanding of these interactions.     

Infrared Image Processing to Guide the Identification of Damage and Dissipative Mechanisms in 3D Layer-to-Layer Woven Composites

Applied Composite Materials - This article discusses techniques that aim at facilitating the identification of dissipative mechanisms activated in woven composites under cyclic loadings. The focus...     

Canadian research activity in aquaculture: A bibliometric analysis

Analysis of the Canadian publications in the field of aquaculture reveals that Canada is one of the word's major contributors in this area. This confirms that Canada's expertise in science and technology often finds its stimulus in its resource-based industries. Several bibliometric indicators were used to enlighten the peculiar features of the Canadian research system. These include the channels of communication used by scientists, the authorship pattern, the level of collaboration, the identification of the institutions in which the research is performed and the uneven research effort distribution inside the country. The relevance of such quantitative measures for science policy-making is emphasized. The present study shows how bibliometric analysis, by describing the actual strengths and weaknesses of Canadian research and identifying the agents of this research activity, might foster a better understanding of the Canadian research enterprise as a whole.     

INTERACTIVE SEMANTIC ANALYSIS OF TECHNICAL TEXTS

Sentence syntax is the basis for organizing semantic relations in TANKA, a project that aims to acquire knowledge from technical text. Other hallmarks include an absence of precoded domain-specific knowledge; significant use of public-domain generic linguistic information sources; involvement of the user as a judge and source of expertise; and learning from the meaning representations produced during processing. These elements shape the realization of the TANKA project: implementing a trainable text processing system to propose correct semantic interpretations to the user. A three-level model of sentence semantics, including a comprehensive Case system, provides the framework for TANKA's representations. Text is first processed by the DIPETT parser, which can handle a wide variety of unedited sentences. The semantic analysis module HAIKU then semi-automatically extracts semantic patterns from the parse trees and composes them into domain knowledge representations. HAIKU's dictionaries and main algorithm are described with the aid of examples and traces of user interaction. Encouraging experimental results are described and evaluated.     

Texture Mapping Real‐World Objects with Hydrographics

In the digital world, assigning arbitrary colors to an object is a simple operation thanks to texture mapping. However, in the real world, the same basic function of applying colors onto an object is far from trivial. One can specify colors during the fabrication process using a color 3D printer, but this does not apply to already existing objects. Paint and decals can be used during post‐fabrication, but they are challenging to apply on complex shapes. In this paper, we develop a method to enable texture mapping of physical objects, that is, we allow one to map an arbitrary color image onto a three‐dimensional object. Our approach builds upon hydrographics, a technique to transfer pigments printed on a sheet of polymer onto curved surfaces. We first describe a setup that makes the traditional water transfer printing process more accurate and consistent across prints. We then simulate the transfer process using a specialized parameterization to estimate the mapping between the planar color map and the object surface. We demonstrate that our approach enables the application of detailed color maps onto complex shapes such as 3D models of faces and anatomical casts.