Strategies to Optimize Biosensors Based on Impedance Spectroscopy to Detect Phytic Acid Using Layer-by-Layer Films

Impedance spectroscopy has been proven a powerful tool for reaching high sensitivity in sensor arrays made with nanostructured films in the so-called electronic tongue systems, whose distinguishing ability may be enhanced with sensing units capable of molecular recognition. In this study we show that for optimized sensors and bio-sensors the dielectric relaxation processes involved in impedance measurements should also be considered, in addition to an adequate choice of sensing materials. We used sensing units made from layer-by-layer (LbL) films with alternating layers of the polyeletrolytes, poly(allylamine) hydrochloride (PAH) and poly(vinyl sulfonate) (PVS), or LbL films of PAH alternated with layers of the enzyme phytase, all adsorbed on gold interdigitate electrodes. Surprisingly, the detection of phytic acid was as effective in the PVS/PAH sensing system as with the PAH/phytase system, in spite of the specific interactions of the latter. This was attributed to the dependence of the relaxation processes on nonspecific interactions such as electrostatic cross-linking and possibly on the distinct film architecture as the phytase layers were found to grow as columns on the LbL film, in contrast to the molecularly thin PAH/PVS films. Using projection techniques, we were able to detect phytic acid at the micromolar level with either of the sensing units in a data analysis procedure that allows for further optimization.

Alignment-based extraction of multiword expressions

Due to idiosyncrasies in their syntax, semantics or frequency, Multiword Expressions (MWEs) have received special attention from the NLP community, as the methods and techniques developed for the treatment of simplex words are not necessarily suitable for them. This is certainly the case for the automatic acquisition of MWEs from corpora. A lot of effort has been directed to the task of automatically identifying them, with considerable success. In this paper, we propose an approach for the identification of MWEs in a multilingual context, as a by-product of a word alignment process, that not only deals with the identification of possible MWE candidates, but also associates some multiword expressions with semantics. The results obtained indicate the feasibility and low costs in terms of tools and resources demanded by this approach, which could, for example, facilitate and speed up lexicographic work.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Template-based quadrilateral meshing

Generating quadrilateral meshes is a highly non-trivial task, as design decisions are frequently driven by specific application demands. Automatic techniques can optimize objective quality metrics, such as mesh regularity, orthogonality, alignment and adaptivity; however, they cannot make subjective design decisions. There are a few quad meshing approaches that offer some mechanisms to include the user in the mesh generation process; however, these techniques either require a large amount of user interaction or do not provide necessary or easy to use inputs. Here, we propose a template-based approach for generating quad-only meshes from triangle surfaces. Our approach offers a flexible mechanism to allow external input, through the definition of alignment features that are respected during the mesh generation process. While allowing user inputs to support subjective design decisions, our approach also takes into account objective quality metrics to produce semi-regular, quad-only meshes that align well to desired surface features. Published by Elsevier Ltd.

All-optical switching device for infrared based on PbTe quantum dots

Multilayers of PbTe quantum dots embedded in SiO2 were fabricated by alternate use of Pulsed Laser Deposition (PLD) and Plasma Enhanced Chemical Vapor Deposition (PECVD) techniques. The morphological properties of the nanostructured material were studied by means of High Resolution Transmission Electron Microscopy (HRTEM), Grazing-Incidence Small-Angle X-ray scattering (GISAXS) and X-ray Reflectometry (XRR) techniques. A preliminary analysis of the GISAXS spectra provided information about the multilayer periodicity and its relationship to the size of the deposited PbTe nanoparticles. Finally multilayers were fabricated inside a Fabry-Perot cavity. The device was characterized by means of Scanning Electron Microscopy (SEM). Transmittance measurements show the device functionality in the infrared region. (C) 2007 Elsevier Ltd. All rights reserved.

Aerosol inorganic composition at a tropical site: Discrepancies between filter-based sampling and a semi-continuous method

The concentrations of the water-soluble inorganic aerosol species, ammonium (NH4+), nitrate (NO3-), chloride (Cl-), and sulfate (SO42-), were measured from September to November 2002 at a pasture site in the Amazon Basin (Rondnia, Brazil) (LBA-SMOCC). Measurements were conducted using a semi-continuous technique (Wet-annular denuder/Steam-Jet Aerosol Collector: WAD/SJAC) and three integrating filter-based methods, namely (1) a denuder-filter pack (DFP: Teflon and impregnated Whatman filters), (2) a stacked-filter unit (SFU: polycarbonate filters), and (3) a High Volume dichotomous sampler (HiVol: quartz fiber filters). Measurements covered the late dry season (biomass burning), a transition period, and the onset of the wet season (clean conditions). Analyses of the particles collected on filters were performed using ion chromatography (IC) and Particle-Induced X-ray Emission spectrometry (PIXE). Season-dependent discrepancies were observed between the WAD/SJAC system and the filter-based samplers. During the dry season, when PM2.5 (D-p <= 2.5 mu m) concentrations were similar to 100 mu g m(-3), aerosol NH4+ and SO42- measured by the filter-based samplers were on average two times higher than those determined by the WAD/SJAC. Concentrations of aerosol NO3- and Cl- measured with the HiVol during daytime, and with the DFP during day- and nighttime also exceeded those of the WAD/SJAC by a factor of two. In contrast, aerosol NO3- and Cl- measured with the SFU during the dry season were nearly two times lower than those measured by the WAD/SJAC. These differences declined markedly during the transition period and towards the cleaner conditions during the onset of the wet season (PM2.5 similar to 5 mu g m(-3)); when filter-based samplers measured on average 40-90% less than the WAD/SJAC. The differences were not due to consistent systematic biases of the analytical techniques, but were apparently a result of prevailing environmental conditions and different sampling procedures. For the transition period and wet season, the significance of our results is reduced by a low number of data points. We argue that the observed differences are mainly attributable to (a) positive and negative filter sampling artifacts, (b) presence of organic compounds and organosulfates on filter substrates, and (c) a SJAC sampling efficiency of less than 100%.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

A combination of techniques to evaluate photodynamic efficiency of photosensitizers

Photodynamic therapy, used mainly for cancer treatment and microorganisms inaction, is based on production of reactive oxygen species by light irradiation of a sensitizer. Hematoporphyrin derivatives as Photofrin (R) (PF) Photogem (R) (PG) and Photosan (R) (PF), and chlorin-c6-derivatives as Photodithazine (R)(PZ), have suitable sensitizing properties. The present study provides a way to make a fast previous evaluation of photosensitizers efficacy by a combination of techniques: a) use of brovine serum albumin and uric acid as chemical dosimeters; b) photo-hemolysis of red blood cells used as a cell membrane interaction model, and c) octanol/phosphate buffer partition to assess the relative lipophilicity of the compounds. The results suggest the photodynamic efficient rankings PZ > PG >= PF > PS. These results agree with the cytotoxicity of the photosensitizers as well as to chromatographic separation of the HpDs, both performed in our group, showing that the more lipophilic is the dye, the more acute is the damage to the RBC membrane and the oxidation of indol, which is immersed in the hydrophobic region of albumin.

Improving data perturbation testing techniques for Web services

The widespread use of service-oriented architectures (SOAs) and Web services in commercial software requires the adoption of development techniques to ensure the quality of Web services. Testing techniques and tools concern quality and play a critical role in accomplishing quality of SOA based systems. Existing techniques and tools for traditional systems are not appropriate to these new systems, making the development of Web services testing techniques and tools required. This article presents new testing techniques to automatically generate a set of test cases and data for Web services. The techniques presented here explore data perturbation of Web services messages upon data types, integrity and consistency. To support these techniques, a tool (GenAutoWS) was developed and applied to real problems. (C) 2010 Elsevier Inc. All rights reserved.

Disposable Graphite Foil Based Electrodes and Their Application in Pharmaceutical Analysis

This paper explores a new source of graphite for working electrodes, which presents advantages such as low electrical resistance, good flexibility, favorable mechanical performance, versatility to design electrodes in almost any size and very low cost. The new electrodes were investigated in batch electrochemical cells as associated with flow injection analysis systems. Cyclic voltammetry, stripping voltammetry, and amperometry associated with flow injection analysis techniques were applied for the determination of ascorbic acid, zinc and paracetamol in pharmaceutical formulations, respectively. Well-established analytical methods were applied for comparison purposes. The results herein demonstrate the potential of graphite foils as working electrodes in different electroanalytical methods, offering the possibility of producing disposable sensors for routine applications.

Biolabeling with nanoparticles based on Y(2)O(3):Nd(3+) and luminescence detection in the near-infrared

Neodymium based fluorescence presents several advantages in comparison to conventional rare earth or enzyme-substrate based fluorescence emitting sources (e.g.Tb, HRP). Based on this fact we have herein explored a Nd-based fluoroimmunoassay. We efficiently detected the presence of an oxidized low-density lipoprotein (oxLDL) in human plasma a well-known marker for cardiovascular diseases, which causes around 30% of deaths worldwide. Conventional fluoroimmunoassay uses time-resolved luminescence techniques, with detection in the visible range, to eliminate the fluorescence background from the biological specimens. By using an immunoassay based on functionalized Y(2)O(3):Nd(3+) nanoparticles, where the excitation and emission processes in the Nd(3+) ion occur in the near-infrared (NIR) region, we have succeeded in eliminating the interferences from the biological fluorescence background, avoiding the use of time-resolved techniques. This yields higher emission intensity from the Nd(3+)-nanolabels and efficient detection of anti-oxidized low-density lipoproteins (anti-oxLDL) by Y(2)O(3):Nd(3+)-antibody-antigen conjugation, leading to a novel biolabeling method. (C) 2010 Elsevier B.V. All rights reserved.

SnO(2)-based materials for pesticide degradation

This study presents the results of the degradation of the pesticide atrazine using electrochemical and photo-assisted electrochemical degradation techniques using SnO(2)-containing electrode of nominal composition electrodes of composition Ti/Ru(x)Sni-(x)O(2) (where X = 0.10, 0.15, 0.20, 0.25 and 0.30). The materials were characterized ex situ and in situ in order to correlate the observed atrazine removal rates with electrode morphology/composition. The results obtained demonstrate the effectiveness of the photo-assisted electrochemical degradation. Using purely electrochemical methods the rate of atrazine removal is almost zero at all the electrodes studied. However, the application of photo-assisted degradation results in almost complete atrazine removal in 1 h of electrolysis. The efficiency of atrazine degradation does not seem to be greatly affected by the electrode material or by SnO(2) content, but the overall COD removal is dependent on the SnO(2) content. Overall, the SnO(2)-containing electrodes do not reach the level of COD removal (maximum similar to 21%) seen for the Ti/Ru(0.3)Ti(0.2)O(2) electrode. An interesting correlation between the morphology factor (phi) and chemical oxygen demand removal is observed. (C) 2010 Elsevier B.V. All rights reserved.

Gelatin-based protonic electrolyte for electrochromic windows

Proton-conducting gel polymer electrolytes based on gelatin plasticized with glycerol and containing acetic acid were investigated, characterized, and applied to electrochromic window. For glycerol contents varying from 7% to 48%, the conductivity of the uniform and predominantly amorphous gel electrolyte was found to follow a Vogel-Tamman-Fulcher behavior with the temperature. Typically, for the electrolyte chosen to make 7 x 2 cm(2) electrochromic smart window with the configuration: glass/fluor-doped tin oxide (FTO)/WO(3)/gelatin electrolyte/CeO(2)-TiO(2)/FTO/glass and containing 28% of glycerol, the conductivities were found to be of the order of 5 x 10(-5) S/cm at room temperature and 3.6 x 10(-4) S/cm at 80 A degrees C. The device was characterized by spectroelectrochemical techniques and was tested up to 10,000 cycles showing a fast coloring/bleaching behavior, where the coloring process was achieved in 10 s and the bleaching in 2 s. The transmission variation at the wavelength of 550 nm was about 15%. The cyclic voltammograms showed a very good reversibility of the cathodic/anodic processes, and the charge density was about 3.5 mC/cm(2). The memory tests showed that the transmittance in the colored state increased by 8% in 90 min after removing the potential.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.