A comparison of five lipid extraction solvent systems for lipidomic studies of human LDL

Lipidome profile of fluids and tissues is a growing field as the role of lipids as signaling molecules is increasingly understood, relying on an effective and representative extraction of the lipids present. A number of solvent systems suitable for lipid extraction are commonly in use, though no comprehensive investigation of their effectiveness across multiple lipid classes has been carried out. To address this, human LDL from normolipidemic volunteers was used to evaluate five different solvent extraction protocols [Folch, Bligh and Dyer, acidified Bligh and Dyer, methanol (MeOH)-tert-butyl methyl ether (TBME), and hexane-isopropanol] and the extracted lipids were analyzed by LC-MS in a high-resolution instrument equipped with polarity switching. Overall, more than 350 different lipid species from 19 lipid subclasses were identified. Solvent composition had a small effect on the extraction of predominant lipid classes (triacylglycerides, cholesterol esters, and phosphatidylcholines). In contrast, extraction of less abundant lipids (phosphatidylinositols, lyso-lipids, ceramides, and cholesterol sulfates) was greatly influenced by the solvent system used. Overall, the Folch method was most effective for the extraction of a broad range of lipid classes in LDL, although the hexane-isopropanol method was best for apolar lipids and the MeOH-TBME method was suitable for lactosyl ceramides. Copyright © 2013 by the American Society for Biochemistry and Molecular Biology, Inc.

Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.

Physical dynamic simulation of shipboard power system components in a distributed computational environment

Shipboard power systems have different characteristics than the utility power systems. In the Shipboard power system it is crucial that the systems and equipment work at their peak performance levels. One of the most demanding aspects for simulations of the Shipboard Power Systems is to connect the device under test to a real-time simulated dynamic equivalent and in an environment with actual hardware in the Loop (HIL). The real time simulations can be achieved by using multi-distributed modeling concept, in which the global system model is distributed over several processors through a communication link. The advantage of this approach is that it permits the gradual change from pure simulation to actual application. In order to perform system studies in such an environment physical phase variable models of different components of the shipboard power system were developed using operational parameters obtained from finite element (FE) analysis. These models were developed for two types of studies low and high frequency studies. Low frequency studies are used to examine the shipboard power systems behavior under load switching, and faults. High-frequency studies were used to predict abnormal conditions due to overvoltage, and components harmonic behavior. Different experiments were conducted to validate the developed models. The Simulation and experiment results show excellent agreement. The shipboard power systems components behavior under internal faults was investigated using FE analysis. This developed technique is very curial in the Shipboard power systems faults detection due to the lack of comprehensive fault test databases. A wavelet based methodology for feature extraction of the shipboard power systems current signals was developed for harmonic and fault diagnosis studies. This modeling methodology can be utilized to evaluate and predicate the NPS components future behavior in the design stage which will reduce the development cycles, cut overall cost, prevent failures, and test each subsystem exhaustively before integrating it into the system.

Evaluation of the Scent Collection System for Its Effectiveness in Volatile Organic Compound Collection and Use in Canine Training

As a result of increased terrorist activity around the world, the development of a canine training aid suitable for daily military operations is necessary to provide effective canine explosive detection. Since the use of sniffer dogs has proven to be a reliable resource for the rapid detection of explosive volatiles organic compounds, the present study evaluated the ability of the Human Scent Collection System (HSCS) device for the creation of training aids for plasticized / tagged explosives, nitroglycerin and TNT containing explosives, and smokeless powders for canine training purposes. Through canine field testing, it was demonstrated that volatiles dynamically collected from real explosive material provided a positive canine response showing the effectiveness of the HSCS in creating canine training aids that can be used immediately or up to several weeks (3) after collection under proper storage conditions. These reliable non-hazardous training aids allow its use in areas where real explosive material aids are not practical and/or available.

Physical dynamic simulation of shipboard power system components in a distributed computational environment

Shipboard power systems have different characteristics than the utility power systems. In the Shipboard power system it is crucial that the systems and equipment work at their peak performance levels. One of the most demanding aspects for simulations of the Shipboard Power Systems is to connect the device under test to a real-time simulated dynamic equivalent and in an environment with actual hardware in the Loop (HIL). The real time simulations can be achieved by using multi-distributed modeling concept, in which the global system model is distributed over several processors through a communication link. The advantage of this approach is that it permits the gradual change from pure simulation to actual application. In order to perform system studies in such an environment physical phase variable models of different components of the shipboard power system were developed using operational parameters obtained from finite element (FE) analysis. These models were developed for two types of studies low and high frequency studies. Low frequency studies are used to examine the shipboard power systems behavior under load switching, and faults. High-frequency studies were used to predict abnormal conditions due to overvoltage, and components harmonic behavior. Different experiments were conducted to validate the developed models. The Simulation and experiment results show excellent agreement. The shipboard power systems components behavior under internal faults was investigated using FE analysis. This developed technique is very curial in the Shipboard power systems faults detection due to the lack of comprehensive fault test databases. A wavelet based methodology for feature extraction of the shipboard power systems current signals was developed for harmonic and fault diagnosis studies. This modeling methodology can be utilized to evaluate and predicate the NPS components future behavior in the design stage which will reduce the development cycles, cut overall cost, prevent failures, and test each subsystem exhaustively before integrating it into the system.

Domain-specific question answering system : an application to the construction sector

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Membrane protein extraction and purification using styrene-maleic acid (SMA) co-polymer:effect of variations in polymer structure

The use of styrene maleic acid (SMA) co-polymers to extract and purify transmembrane proteins, whilst retaining their native bilayer environment, overcomes many of the disadvantages associated with conventional detergent based procedures. This approach has huge potential for the future of membrane protein structural and functional studies. In this investigation we have systematically tested a range of commercially available SMA polymers, varying in both the ratio of styrene to maleic acid and in total size, for the ability to extract, purify and stabilise transmembrane proteins. Three different membrane proteins (BmrA, LeuT and ZipA) which vary in size and shape were used. Our results show that several polymers can be used to extract membrane proteins comparably to conventional detergents. A styrene:maleic acid ratio of either 2:1 or 3:1, combined with a relatively small average molecular weight (7.5-10 kDa) is optimal for membrane extraction, and this appears to be independent of the protein size, shape or expression system. A subset of polymers were taken forward for purification, functional and stability tests. Following a one-step affinity purification SMA 2000 was found to be the best choice for yield, purity and function. However the other polymers offer subtle differences in size and sensitivity to divalent cations that may be useful for a variety of downstream applications.

Domain-specific question answering system : an application to the construction sector

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

(Figure 1 and 2) Phosphorus pools in the investigated sediments quantified by SEDEX sequential extraction and distribution of recovered 33P spike between sedimentary P pools after incubation

Phosphorus is an essential nutrient for life. In the ocean, phosphorus burial regulates marine primary production**1, 2. Phosphorus is removed from the ocean by sedimentation of organic matter, and the subsequent conversion of organic phosphorus to phosphate minerals such as apatite, and ultimately phosphorite deposits**3, 4. Bacteria are thought to mediate these processes**5, but the mechanism of sequestration has remained unclear. Here, we present results from laboratory incubations in which we labelled organic-rich sediments from the Benguela upwelling system, Namibia, with a 33P-radiotracer, and tracked the fate of the phosphorus. We show that under both anoxic and oxic conditions, large sulphide-oxidizing bacteria accumulate 33P in their cells, and catalyse the nearly instantaneous conversion of phosphate to apatite. Apatite formation was greatest under anoxic conditions. Nutrient analyses of Namibian upwelling waters and sediments suggest that the rate of phosphate-to-apatite conversion beneath anoxic bottom waters exceeds the rate of phosphorus release during organic matter mineralization in the upper sediment layers. We suggest that bacterial apatite formation is a significant phosphorus sink under anoxic bottom-water conditions. Expanding oxygen minimum zones are projected in simulations of future climate change**6, potentially increasing sequestration of marine phosphate, and restricting marine productivity.

Work Extraction and Energy Storage in the Dicke Model

We study work extraction from the Dicke model achieved using simple unitary cyclic transformations keeping into account both a non optimal unitary protocol, and the energetic cost of creating the initial state. By analyzing the role of entanglement, we find that highly entangled states can be inefficient for energy storage when considering the energetic cost of creating the state. Such surprising result holds notwithstanding the fact that the criticality of the model at hand can sensibly improve the extraction of work. While showing the advantages of using a many-body system for work extraction, our results demonstrate that entanglement is not necessarily advantageous for energy storage purposes, when non optimal processes are considered. Our work shows the importance of better understanding the complex interconnections between non-equilibrium thermodynamics of quantum systems and correlations among their subparts.

Artificial Neural Network and Neutron Aplication in a Volume Fraction Calculation in Annular and Stratified Multiphase System

Multiphase flows, type oil–water-gas are very common among different industrial activities, such as chemical industries and petroleum extraction, and its measurements show some difficulties to be taken. Precisely determining the volume fraction of each one of the elements that composes a multiphase flow is very important in chemical plants and petroleum industries. This work presents a methodology able to determine volume fraction on Annular and Stratified multiphase flow system with the use of neutrons and artificial intelligence, using the principles of transmission/scattering of fast neutrons from a 241Am-Be source and measurements of point flow that are influenced by variations of volume fractions. The proposed geometries used on the mathematical model was used to obtain a data set where the thicknesses referred of each material had been changed in order to obtain volume fraction of each phase providing 119 compositions that were used in the simulation with MCNP-X –computer code based on Monte Carlo Method that simulates the radiation transport. An artificial neural network (ANN) was trained with data obtained using the MCNP-X, and used to correlate such measurements with the respective real fractions. The ANN was able to correlate the data obtained on the simulation with MCNP-X with the volume fractions of the multiphase flows (oil-water-gas), both in the pattern of annular flow as stratified, resulting in a average relative error (%) for each production set of: annular (air= 3.85; water = 4.31; oil=1.08); stratified (air=3.10, water 2.01, oil = 1.45). The method demonstrated good efficiency in the determination of each material that composes the phases, thus demonstrating the feasibility of the technique.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Development of a simple and fast “DNA extraction kit” for sea food identification and marine species

Seafood products fraud, the misrepresentation of them, have been discovered all around the world in different forms as false labeling, species substitution, short-weighting or over glazing in order to hide the correct identity, origin or weight of the seafood products. Due to the value of seafood products such as canned tuna, swordfish or grouper, these species are the subject of the commercial fraud is mainly there placement of valuable species with other little or no value species. A similar situation occurs with the shelled shrimp or shellfish that are reduced into pieces for the commercialization. Food fraud by species substitution is an emerging risk given the increasingly global food supply chain and the potential food safety issues. Economic food fraud is committed when food is deliberately placed on the market, for financial gain deceiving consumers (Woolfe, M. & Primrose, S. 2004). As a result of the increased demand and the globalization of the seafood supply, more fish species are encountered in the market. In this scenary, it becomes essential to unequivocally identify the species. The traditional taxonomy, based primarily on identification keys of species, has shown a number of limitations in the use of the distinctive features in many animal taxa, amplified when fish, crustacean or shellfish are commercially transformed. Many fish species show a similar texture, thus the certification of fish products is particularly important when fishes have undergone procedures which affect the overall anatomical structure, such as heading, slicing or filleting (Marko et al., 2004). The absence of morphological traits, a main characteristic usually used to identify animal species, represents a challenge and molecular identification methods are required. Among them, DNA-based methods are more frequently employed for food authentication (Lockley & Bardsley, 2000). In addition to food authentication and traceability, studies of taxonomy, population and conservation genetics as well as analysis of dietary habits and prey selection, also rely on genetic analyses including the DNA barcoding technology (Arroyave & Stiassny, 2014; Galimberti et al., 2013; Mafra, Ferreira, & Oliveira, 2008; Nicolé et al., 2012; Rasmussen & Morrissey, 2008), consisting in PCR amplification and sequencing of a COI mitochondrial gene specific region. The system proposed by P. Hebert et al. (2003) locates inside the mitochondrial COI gene (cytochrome oxidase subunit I) the bioidentification system useful in taxonomic identification of species (Lo Brutto et al., 2007). The COI region, used for genetic identification - DNA barcode - is short enough to allow, with the current technology, to decode sequence (the pairs of nucleotide bases) in a single step. Despite, this region only represents a tiny fraction of the mitochondrial DNA content in each cell, the COI region has sufficient variability to distinguish the majority of species among them (Biondo et al. 2016). This technique has been already employed to address the demand of assessing the actual identity and/or provenance of marketed products, as well as to unmask mislabelling and fraudulent substitutions, difficult to detect especially in manufactured seafood (Barbuto et al., 2010; Galimberti et al., 2013; Filonzi, Chiesa, Vaghi, & Nonnis Marzano, 2010). Nowadays,the research concerns the use of genetic markers to identify not only the species and/or varieties of fish, but also to identify molecular characters able to trace the origin and to provide an effective control tool forproducers and consumers as a supply chain in agreementwith local regulations.

Extraction and purification of immunoglobulin G with aqueous biphasic systems

The immune system is able to produce antibodies, which have the capacity to recognize and to bind to foreign molecules or pathogenic organisms. Currently, there are a diversity of diseases that can be treated with antibodies, like immunoglobulins G (IgG). Thereby, the development of cost-efficient processes for their extraction and purification is an area of main interest in biotechnology. Aqueous biphasic systems (ABS) have been investigated for this purpose, once they allow the reduction of costs and the number of steps involved in the process, when compared with conventional methods. Nevertheless, typical ABS have not showed to be selective, resulting in low purification factors and yields. In this context, the addition of ionic liquids (ILs) as adjuvants can be a viable and potential alternative to tailor the selectivity of these systems. In this work, ABS composed of polyethylene glycol (PEG) of different molecular weight, and a biodegradable salt (potassium citrate) using ILs as adjuvants (5 wt%), were studied for the extraction and purification of IgG from a rabbit source. Initially, it was tested the extraction time, the effect on the molecular weight of PEG in a buffer solution of K3C6H5O7/C6H8O7 at pH≈7, and the effect of pH (59) on the yield (YIgG) and extraction efficiency (EEIgG%) of IgG. The best results regarding EEIgG% were achieved with a centrifugation step at 1000 rpm, during 10 min, in order to promote the separation of phases followed by 120 min of equilibrium. This procedure was then applied to the remaining experiments. The results obtained in the study of PEGs with different molecular weights, revealed a high affinity of IgG for the PEG-rich phase, and particularly for PEGs of lower molecular weight (EEIgG% of 96 % with PEG 400). On the other hand, the variation of pH in the buffer solution did not show a significant effect on the EEIgG%. Finally, it was evaluated the influence of the addition of different ILs (5% wt) on the IgG extraction in ABS composed of PEG 400 at pH≈7. In these studies, it was possible to obtain EEIgG% of 100% with the ILs composed of the anions [TOS]-, [CH3CO2]-and Cl-, although the obtained YIgG% were lower than 40%. On the other hand, the ILs composed of the anions Br-, as well as of the cation [C10mim]+, although not leading to EEIgG% of 100%, provide an increase in the YIgG%. ABS composed of PEG, a biodegradable organic salt and ILs as adjuvants, revealed to be an alternative and promising method to purify IgG. However, additional studies are still required in order to reduce the loss of IgG.