Synthesis and structure-activity study of quaternary ammonium functionalized beta-cyclodextrin-carboxymethylcellulose polymers

Carboxymethylcellulose (CMC) and beta-cyclodextrin (beta-CD)-based polymers functionalized with two types of quaternary ammonium compounds (QACs), the alkaquat DMB-451 (N-alkyl (50% C14, 40% C12, 10% C10) dimethylbenzylammonium chloride) (DMD-451) named polymer DMB-451, and FMB 1210-8 (a blend of 32 w% N-alkyl (50% C14, 40% C12, 10% C10) dimethylbenzylammonium chloride and 48 w% of didecyldimethylammonium chloride) named polymer FMB 1210-8, were synthethized and characterized by Fourier transform infrared spectroscopy. The antimicrobial activities of these polymers against Eschericia coli were also evaluated at 25 degrees C in wastewater. The results have indicated that the polymer FMB 1210-8 possesses a high-affinity binding with bacterial cells that induces a rapid disinfection process. Moreover, in the same experimental conditions of disinfection (mixture of 1.0 g of polymer and 100 mL of wastewater), the polymer FMB 1210-8 has a higher antimicrobial efficiency (99.90%) than polymer DMB-451 (92.8%). This phenomenon might be associated to a stronger interaction with bacterial cells due to stronger binding affinity for E. coli cells and greater killing efficiency of the C10 alkyl chains QAC of polymer FMB 1210-8 to disrupt the bacterial cell membrane as compared to N-alkyl (50% C14, 40% C12, 10% C10) dimethylbenzylammonium chloride. Together, these results suggest that the polymer FMB 1210-8 could constitute a good disinfectant against Escherichia coli, which could be advantageously used in wastewater treatments due to the low toxicity of beta-CD and CMC, and moderated toxicity of FMB 1210-8 to human and environment.     

Trade‐Offs in Seed Dispersal Strategies Across Riparian Trees: The How Matters as Much as the When

Riparian Salicaceae are prolific producers of short‐lived seeds that require very restrictive hydro‐geomorphic conditions for establishment. It is generally assumed that if floods are able to create nursery sites timed with seed dispersal, recruitment will occur. Other spatial and temporal seed dispersal patterns besides the dispersal period have historically received little attention. However, seed dispersal patterns can be highly variable between regions, species and over the years. In this paper, we report the seed dispersal patterns of three dominant riparian Salicaceae trees in Europe: Populus alba, P. nigra and Salix alba to suggest possible trade‐offs between seed dispersal patterns, germinability, longevity and establishment. Seed rain of the three species was monitored in 33 glue‐coated traps for three months yearly from 2006 to 2008 in an 8‐km stretch of the Middle Ebro River (N Spain), which has a pluvio‐nival regime. P. alba dispersed seeds earlier during a shorter time period and with a fewer number of seed release pulses compared with P. nigra, and especially with S. alba. With overlapping seed dispersal periods, the two latter species occupy similar landform units but rarely compete with P. alba, usually at higher elevations, as shown in a previous study in the same study area. The three species had very high germinability immediately after release (>90%), but longevity in S. alba was eight times shorter than that of its two Populus counterparts. We suggest that S. alba has compensated its lower seed quality with a more spaced seed release in several pulses of similar magnitude. With similar seed dispersal patterns and germinability but a higher longevity, P. nigra had a much higher density of individuals than S. alba in the recruitment zones of the study area. We hope that our results may inform river managers about how to optimize river flows to promote sexual regeneration of these species. Copyright © 2015 John Wiley & Sons, Ltd.     

Immune response to baculovirus expressed protein fragment amino acids 190-289 of respiratory syncytial virus (RSV) fusion protein

At least two neutralizing epitopes have been identified in the amino acid (aa) sequence 190-289 of the RSV fusion protein. The authors expressed this region in insect cells (bF190-289) and compared the immune response to bF190-289 with that induced by baculovirus expressed full-length fusion protein (bF). As with bF, mice primed with bF190-289 produced exclusively antibodies of IgG1 isotype, generated neutralizing antibodies, reduced significantly the virus titer (about a half log10 reduction) after RSV challenge and induced a Helper T (Th) 2 cell response in mediastinal lymph node cells (MLNC) restimulated in vitro. Thus, the aa sequence 190-289 represents a major immunogenic region of the RSV fusion protein.     

Characterization of carbon-carbon composites—II: Oxidation behaviour

Carbon-carbon composites made from ex-acrylic fibres and pyrocarbon matrices have been gasified in air in order to investigate their resistance to oxidation. The carbon fibres and the surrounding pyrocarbon layers are preferentially oxidized due to a catalytic process. The importance of the catalytic phenomenon depends primarly on the heat treatment of the composites. Lowest oxidation rates are obtained after heat treatment at 2900 K. Oxidation tests allow a simple evaluation of the carbon-carbon composites performance at the laboratory scale.     

Angle resolved X-ray photoemission spectroscopy double layer model for in situ characterization of metal organic chemical vapour deposition nanometric films

In situ Angle Resolved X-ray Photoemission Spectroscopy (ARXPS) characterizations of TiO₂ thin films grown on silicon by Metal Organic Chemical Vapour Deposition were performed in order to get information on interfacial reactions at the first stages of the growth, one of the aims being to understand the influence of deposition conditions. Thickness measurements were also carried out from ARXPS analyses. As the real structure of the films was shown to be a double layer system such as TiO₂/SiO₂/Si, an ARXPS model of thickness and surface coverage determination was applied to each layer independently. However, the application of this model to very thin films underestimates the surface coverage of the interfacial layer. A “Double Layer” model taking into account the attenuation of the silicon oxide and substrate signals by the external layer was also developed.     

Using Boolean factors for the construction of an artificial neural networks

ABSTRACT

We propose a novel approach to define Artificial Neural Network(ANN) architecture from Boolean factors. ANNs are a subfield of machine learning applicable to several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. Since it is difficult to look into the network and figure out exactly what it has learnt, the complexity of such a technique makes its interpretation more tedious. We propose in this paper to build feedforward ANNs using the optimal factors obtained from the Boolean context representing a data. Since optimal factors completely cover the data and therefore give an explanation to these data, We could give an interpretation to the neurons activation and justify the presence of a neuron in our proposed neural network. We show through experiments and comparisons on the use data sets that this approach provides relatively better results for some key performance measures.     

Juror competence in civil trials: Effects of preinstruction and evidence technicality.

48 jury-eligible adults heard 1 of 4 versions of a tort trial. The design combined high and moderate levels of evidence technicality and the placement of substantive judicial instructions either before or after evidence presentation. Jurors given instructions before hearing the evidence for liability and before the evidence for compensation made clear distinctions among 4 differentially worthy plaintiffs, whereas jurors instructed after evidence presentation were not able to distinguish among the plaintiffs. Preinstructions enabled jurors to devise a causal model, as measured by both verbal representation of the evidence and recognition tests, that contained more probative evidence and less nonprobative and evaluative information than the models constructed by jurors who were postinstructed. Preinstructed jurors were better able than postinstructed jurors to correctly reject recognition items not part of the trial text and to correctly identify items from the trial. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method

The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a ? function, involving two hyperparameters. Our goal is to estimate the optimal parameters in order to automatically reconstruct images. We propose to use the maximum-likelihood estimator (MLE), applied to the observed image. We need sampling from prior and posterior distributions. Since the convolution prevents use of standard samplers, we have developed a modified Geman-Yang algorithm, using an auxiliary variable and a cosine transform. We present a Markov chain Monte Carlo maximum-likelihood (MCMCML) technique which is able to simultaneously achieve the estimation and the reconstruction.     

Data quality awareness: a case study for cost optimal association rule mining

The quality of discovered association rules is commonly evaluated by interestingness measures (commonly support and confidence) with the purpose of supplying indicators to the user in the understanding and use of the new discovered knowledge. Low-quality datasets have a very bad impact over the quality of the discovered association rules, and one might legitimately wonder if a so-called “interesting” rule noted LHS→ RHS is meaningful when 30% of the LHS data are not up-to-date anymore, 20% of the RHS data are not accurate, and 15% of the LHS data come from a data source that is well-known for its bad credibility. This paper presents an overview of data quality characterization and management techniques that can be advantageously employed for improving the quality awareness of the knowledge discovery and data mining processes. We propose to integrate data quality indicators for quality aware association rule mining. We propose a cost-based probabilistic model for selecting legitimately interesting rules. Experiments on the challenging KDD-Cup-98 datasets show that variations on data quality have a great impact on the cost and quality of discovered association rules and confirm our approach for the integrated management of data quality indicators into the KDD process that ensure the quality of data mining results.