Improving peak shapes with counter gradients in two-dimensional high performance liquid chromatography

To achieve the greatest peak capacity in two-dimensional high performance liquid chromatography (2D-HPLC) a gradient should be operated in both separation dimensions. However, it is known that when an injection solvent that is stronger than the initial mobile phase composition is deleterious to peak performance, thus causing problems when cutting a portion from one gradient into another. This was overcome when coupling hydrophilic interaction with reversed phase chromatography by introducing a counter gradient that changed the solvent strength of the second dimension injection. It was found that an injection solvent composition of 20% acetonitrile in water gave acceptable results in one-dimensional simulations with an initial composition of 5% acetonitrile. When this was transferred to a 2D-HPLC separation of standards it was found that a marked improvement in peak shape was gained for the moderately retained analytes (phenol and dimethyl phthalate), some improvement for the weakly retained caffeine and very little change for the strongly retained n-propylbenzene and anthracene which already displayed good chromatographic profiles. This effect was transferred when applied to a 2D-HPLC separation of a coffee extract where the indecipherable retention profile was transformed to a successful application multidimensional chromatography with peaks occupying 71% of the separation space according to the geometric approach to factor analysis.

Determination of neurotransmitters and their metabolites using one- and two-dimensional liquid chromatography with acidic potassium permanganate chemiluminescence detection

High-performance liquid chromatography with chemiluminescence detection based on the reaction with acidic potassium permanganate and formaldehyde was explored for the determination of neurotransmitters and their metabolites. The neurotransmitters norepinephrine and dopamine were quantified in the left and right hemispheres of rat hippocampus, nucleus accumbens and prefrontal cortex, and the metabolites vanillylmandelic acid, 3,4-dihydrophenylacetic acid, 5-hydroxyindole-3-acetic acid and homovanillic acid were identified in human urine. Under optimised chemiluminescence reagent conditions, the limits of detection for these analytes ranged from 2.5 × 10−8 to 2.5 × 10−7 M. For the determination of neurotransmitter metabolites in urine, a two-dimensional high-performance liquid chromatography (2D-HPLC) separation operated in heart-cutting mode was developed to overcome the peak capacity limitations of the one-dimensional separation. This approach provided the greater separation power of 2D-HPLC with analysis times comparable to conventional one-dimensional separations.

Fracture modelling of DP780 sheets using a hybrid experimental-numerical method and two-dimensional digital image correlation

This paper is concerned with the construction of fracture envelopes of DP780 sheets using two methods: a hybrid experimental-numerical method; two-dimensional digital image correlation (2D-DIC). For the hybrid method, four types of ductile fracture tests were carried out covering a wide range of stress states on specimens: with a central hole; two symmetric circular notches; flat grooved; and diagonally double-notched. Based on the fracture strain and loading paths identified with finite element simulation, a fracture envelope was obtained by employing the three-parameter modified Mohr-Coulomb fracture model. In addition, the fracture surface strain was directly measured using 2D-DIC. Loading histories of each test were extracted from a surface element of a three dimensional finite element model. The comparison of fracture envelopes constructed by the two methods reveals that there is little difference. Thus, it can be concluded that 2D-DIC is applicable to fracture modelling of DP780 sheets despite the assumption of the plane stress condition even after necking

Off-line two-dimensional liquid chromatography for metabolomics: an example using Agaricus bisporus mushrooms exposed to UV irradiation

It has previously been shown that irradiation with UV light increases the vitamin D content of certain mushroom species, but the effect on other nutrients is unknown, and is difficult to assess due to the complexity of the sample matrix. Here, an offline reversed phase × reversed phase two-dimensional liquid chromatography methodology was developed and applied to Agaricus bisporus mushrooms in order to demonstrate the potential of the technique and assess the effect of UV irradiation on the mushroom’s metabolic profile. The method allowed the detection of 158 peaks in a single analytical run. A total of 51 compounds including sugars, amino acids, organic and fatty acids and phenolic compounds were identified using certified reference standards. After irradiation of the mushrooms with UV for 30 s the number of peaks detected decreased from 158 to 150; 47 compounds increased in concentration while 72 substances decreased. This is the first time that two-dimensional liquid chromatography has been carried out for the metabolomic analysis of mushrooms. The data provide an overview of the gain/loss of nutritional value of the mushrooms following UV irradiation and demonstrate that the increased peak capacity and separation space of two-dimensional liquid chromatography has great potential in metabolomics.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Statistical comparisons of non-deterministic IR systems using two dimensional variance

Retrieval systems with non-deterministic output are widely used in information retrieval. Common examples include sampling, approximation algorithms, or interactive user input. The effectiveness of such systems differs not just for different topics, but also for different instances of the system. The inherent variance presents a dilemma - What is the best way to measure the effectiveness of a non-deterministic IR system? Existing approaches to IR evaluation do not consider this problem, or the potential impact on statistical significance. In this paper, we explore how such variance can affect system comparisons, and propose an evaluation framework and methodologies capable of doing this comparison. Using the context of distributed information retrieval as a case study for our investigation, we show that the approaches provide a consistent and reliable methodology to compare the effectiveness of a non-deterministic system with a deterministic or another non-deterministic system. In addition, we present a statistical best-practice that can be used to safely show how a non-deterministic IR system has equivalent effectiveness to another IR system, and how to avoid the common pitfall of misusing a lack of significance as a proof that two systems have equivalent effectiveness.

Screening of cannabinoids in industrial-grade hemp using two-dimensional liquid chromatography coupled with acidic potassium permanganate chemiluminescence detection

Widely known for its recreational use, the cannabis plant also has the potential to act as an antibacterial agent in the medicinal field. The analysis of cannabis plants/products in both pharmacological and forensic studies often requires the separation of compounds of interest and/or accurate identification of the whole cannabinoid profile. In order to provide a complete separation and detection of cannabinoids, a new two-dimensional liquid chromatography method has been developed using acidic potassium permanganate chemiluminescence detection, which has been shown to be selective for cannabinoids. This was carried out using a Luna 100 Å CN column and a Poroshell 120 EC-C18 column in the first and second dimensions, respectively. The method has utilized a large amount of the available separation space with a spreading angle of 48.4° and a correlation of 0.66 allowing the determination of more than 120 constituents and mass spectral identification of ten cannabinoids in a single analytical run. The method has the potential to improve research involved in the characterization of sensitive, complex matrices.

Quantitative chemical-structure evaluation using atom probe tomography: Short-range order analysis of Fe-Al.

Short-range-order (SRO) has been quantitatively evaluated in an Fe-18Al (at%) alloy using atom probe tomography (APT) data and by calculation of the generalised multicomponent short-range order (GM-SRO) parameters, which have been determined by shell-based analysis of the three-dimensional atomic positions. The accuracy of this method with respect to limited detector efficiency and spatial resolution is tested against simulated D03 ordered data. Whilst there is minimal adverse effect from limited atom probe instrument detector efficiency, the combination of this with imperfect spatial resolution has the effect of making the data appear more randomised. The value of lattice rectification of the experimental APT data prior to GM-SRO analysis is demonstrated through improved information sensitivity.

Multi-dimensional liquid chromatography and metabolomics, are two dimensions better than one?

 While data processing methods in metabolomic studies often work with 'n' number of dimensions, analytical techniques, with the notable exception of NMR, have mostly stuck only to one. Peak overlap continues to be a problem and there is an ever-present demand to maximize the number of metabolites that can be separated and identified in a single run. One method that might help to overcome these issues is multidimensional liquid chromatography, which uses two columns of different phases. A sequential collection of aliquots is made from the first column and reinjected onto a second, and the resulting data are then plotted in 2D or 3D space. The total peak capacity of such a system is the combined peak capacities of each column. The 'offline' version of this technique, using a fraction collector, was introduced over 30 years ago but with recent advances in instrumentation and software, particularly the 'online' approach using automated switching valves, has led to increasing interest in the technique. Both offline and online methods can be carried out as a comprehensive procedure, or via 'heart-cutting', in which only specific peaks are analysed in the second dimension. Past applications include proteomics, natural product chemistry, forensic science and pharmaceutical analysis. These successes are likely to be built on in the future as new column chemistries and bio-informatic approaches are developed. In this review an overview of the theory of twodimensional liquid chromatography is presented, its potential in the field of metabolomics is assessed and predictions for future research directions are made.

Three-dimensional slope stability assessment of two-layered undrained clay

This paper uses the finite element upper and lower bound limit analysis methods to investigate the three-dimensional (3D) slope stability of two-layered undrained clay slopes. The solutions obtained from the slope stability analyses are bracketed to within ±10% or better. For comparison purposes, results from two-dimensional (2D) analyses based on the numerical limit analysis methods and the conventional limit equilibrium method (LEM) are also discussed. This study shows that 3D boundary of a slope can have significant effects on the slope stability. In addition, the results are presented in the form of stability charts which can be convenient tools for practicing engineers.

Qualitative assessment of bubble behaviour for central and asymmetric injection in 2D gas-solid fluidized bed using image analysis technique and CFD modelling

Bubble characteristics such as shape, size, and trajectory control the hydrodynamics and therefore heat transfer in fluidized bed reactors. Thus understanding these characteristics is very important for the design and scaleup of fluidized beds. An earlier developed Eulerian-Eulerian two-fluid model for simulating dense gas–solid two-phase flow has been used to compare the experimental data in a pseudo-two-dimensional (2-D) bed. Bubbles are injected asymmetrically by locating the nozzle at proximity to the wall, thus presenting the effect wall has on asymmetrical injection as compared to symmetrical injection. In this work, a digital image analysis technique was developed to study the bubble behaviour in a two-dimensional bubbling bed. The high-speed photography reveals an asymmetric wake formation during detachment indicating an early onset of mixing process. The wall forces acts tangentially on thebubble and has a significant impact on the bubble shape, neck formation during detachment and its trajectory through the bed. Larger bubbles drifting away from the centre with longer paths are observed. This qualitative behaviour is well predicted by CFD modelling. Asymmetric injection can significantly influence the heat and mass transfer characteristics.

The influence of the supply chain agents on the new product development's performance: an analysis based on the multi-group moderation

Este estudo buscou verificar a influencia dos agentes da cadeia de suprimentos no desempenho do desenvolvimento de novos produtos quando os agentes são analisados em conjunto. A motivação desta pesquisa veio de estudos que alertaram para a consideração da integração da cadeia de suprimentos como um constructo multidimensional, englobando o envolvimento da manufatura, fornecedores e clientes no desenvolvimento de novos produtos; e devido à falta de informação sobre as influencias individuais destes agentes no desenvolvimento de novos produtos. Sob essas considerações, buscou-se construir um modelo analítico baseado na Teoria do Capital Social e Capacidade Absortiva, construir hipóteses a partir da revisão da literatura e conectar constructos como cooperação, envolvimento do fornecedor no desenvolvimento de novos produtos (DNP), envolvimento do cliente no DNP, envolvimento da manufatura no DNP, antecipação de novas tecnologias, melhoria contínua, desempenho operacional do DNP, desempenho de mercado do NPD e desempenho de negócio do DNP. Para testar as hipóteses foram consideradas três variáveis moderadoras, tais como turbulência ambiental (baixa, média e alta), indústria (eletrônicos, maquinários e equipamentos de transporte) e localização (América, Europa e Ásia). Para testar o modelo foram usados dados do projeto High Performance Manufacturing que contém 339 empresas das indústrias de eletrônicos, maquinários e equipamentos de transporte, localizadas em onze países. As hipóteses foram testadas por meio da Análise Fatorial Confirmatória (AFC) incluindo a moderação muti-grupo para as três variáveis moderadoras mencionadas anteriormente. Os principais resultados apontaram que as hipóteses relacionadas com cooperação foram confirmadas em ambientes de média turbulência, enquanto as hipóteses relacionadas ao desempenho no DNP foram confirmadas em ambientes de baixa turbulência ambiental e em países asiáticos. Adicionalmente, sob as mesmas condições, fornecedores, clientes e manufatura influenciam diferentemente no desempenho de novos produtos. Assim, o envolvimento de fornecedores influencia diretamente no desempenho operacional e indiretamente no desempenho de mercado e de negócio em baixos níveis de turbulência ambiental, na indústria de equipamentos de transporte em países da Americanos e Europeus. De igual forma, o envolvimento do cliente influenciou diretamente no desempenho operacional e indiretamente no desempenho de mercado e do negócio em médio nível de turbulência ambiental, na indústria de maquinários e em países Asiáticos. Fornecedores e clientes não influenciam diretamente no desempenho de mercado e do negócio e não influenciam indiretamente no desempenho operacional. O envolvimento da manufatura não influenciou nenhum tipo de desempenho do desenvolvimento de novos produtos em todos os cenários testados.

Studies in non-Gaussian analysis

This thesis presents general methods in non-Gaussian analysis in infinite dimensional spaces. As main applications we study Poisson and compound Poisson spaces. Given a probability measure μ on a co-nuclear space, we develop an abstract theory based on the generalized Appell systems which are bi-orthogonal. We study its properties as well as the generated Gelfand triples. As an example we consider the important case of Poisson measures. The product and Wick calculus are developed on this context. We provide formulas for the change of the generalized Appell system under a transformation of the measure. The L² structure for the Poisson measure, compound Poisson and Gamma measures are elaborated. We exhibit the chaos decomposition using the Fock isomorphism. We obtain the representation of the creation, annihilation operators. We construct two types of differential geometry on the configuration space over a differentiable manifold. These two geometries are related through the Dirichlet forms for Poisson measures as well as for its perturbations. Finally, we construct the internal geometry on the compound configurations space. In particular, the intrinsic gradient, the divergence and the Laplace-Beltrami operator. As a result, we may define the Dirichlet forms which are associated to a diffusion process. Consequently, we obtain the representation of the Lie algebra of vector fields with compact support. All these results extends directly for the marked Poisson spaces.

Dynamic headspace solid-phase microextraction combined with one-dimensional gas chromatography–mass spectrometry as a powerful tool to differentiate banana cultivars based on their volatile metabolite profile

In this study the effect of the cultivar on the volatile profile of five different banana varieties was evaluated and determined by dynamic headspace solid-phase microextraction (dHS-SPME) combined with one-dimensional gas chromatography–mass spectrometry (1D-GC–qMS). This approach allowed the definition of a volatile metabolite profile to each banana variety and can be used as pertinent criteria of differentiation. The investigated banana varieties (Dwarf Cavendish, Prata, Maçã, Ouro and Platano) have certified botanical origin and belong to the Musaceae family, the most common genomic group cultivated in Madeira Island (Portugal). The influence of dHS-SPME experimental factors, namely, fibre coating, extraction time and extraction temperature, on the equilibrium headspace analysis was investigated and optimised using univariate optimisation design. A total of 68 volatile organic metabolites (VOMs) were tentatively identified and used to profile the volatile composition in different banana cultivars, thus emphasising the sensitivity and applicability of SPME for establishment of the volatile metabolomic pattern of plant secondary metabolites. Ethyl esters were found to comprise the largest chemical class accounting 80.9%, 86.5%, 51.2%, 90.1% and 6.1% of total peak area for Dwarf Cavendish, Prata, Ouro, Maçã and Platano volatile fraction, respectively. Gas chromatographic peak areas were submitted to multivariate statistical analysis (principal component and stepwise linear discriminant analysis) in order to visualise clusters within samples and to detect the volatile metabolites able to differentiate banana cultivars. The application of the multivariate analysis on the VOMs data set resulted in predictive abilities of 90% as evaluated by the cross-validation procedure.

Allergic asthma exhaled breath metabolome: a challenge for comprehensive two-dimensional gas chromatography

Allergic asthma represents an important public health issue, most common in the paediatric population, characterized by airway inflammation that may lead to changes in volatiles secreted via the lungs. Thus, exhaled breath has potential to be a matrix with relevant metabolomic information to characterize this disease. Progress in biochemistry, health sciences and related areas depends on instrumental advances, and a high throughput and sensitive equipment such as comprehensive two-dimensional gas chromatography–time of flight mass spectrometry (GC × GC–ToFMS) was considered. GC × GC–ToFMS application in the analysis of the exhaled breath of 32 children with allergic asthma, from which 10 had also allergic rhinitis, and 27 control children allowed the identification of several hundreds of compounds belonging to different chemical families. Multivariate analysis, using Partial Least Squares-Discriminant Analysis in tandem with Monte Carlo Cross Validation was performed to assess the predictive power and to help the interpretation of recovered compounds possibly linked to oxidative stress, inflammation processes or other cellular processes that may characterize asthma. The results suggest that the model is robust, considering the high classification rate, sensitivity, and specificity. A pattern of six compounds belonging to the alkanes characterized the asthmatic population: nonane, 2,2,4,6,6-pentamethylheptane, decane, 3,6-dimethyldecane, dodecane, and tetradecane. To explore future clinical applications, and considering the future role of molecular-based methodologies, a compound set was established to rapid access of information from exhaled breath, reducing the time of data processing, and thus, becoming more expedite method for the clinical purposes.