Advances in multivariate data analysis

Two-dimensional difference gel electrophoresis (DIGE) in combination with univariate (Student's t-test) and multivariate data analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study the anti-inflammatory effects of the beta(2)-adrenergic receptor (beta(2)-AR) agonist zilpaterol. U937 macrophages were exposed to the endotoxin lipopolysaccharide (LPS) to induce an inflammatory reaction, which was inhibited by the addition of zilpaterol (LZ). This inhibition was counteracted by addition of the beta(2)-AR antagonist propranolol (LZP). The extracellular proteome of the U937 cells induced by the three treatments were examined by DIGE. PCA was used as an explorative tool to investigate the clustering of the proteome dataset. Using this tool, the dataset obtained from cells treated with LPS and LZP were separated from those obtained from LZ treated cells. PLS-DA, a multivariate data analysis tool that also takes correlations between protein spots and class assignment into account, correctly classified the different extracellular proteomes and showed that many proteins were differentially expressed between the proteome of inflamed cells (LPS and LZP) and cells in which the inflammatory response was inhibited (LZ). The Student's t-test revealed 8 potential protein biomarkers, each of which was expressed at a similar level in the LPS and LZP treated cells, but differently expressed in the LZ treated cells. Two of the identified proteins, macrophage inflammatory protein-1beta (MIP-1beta) and macrophage inflammatory protein-1alpha (MIP-1alpha) are known secreted proteins. The inhibition of MIP-1beta by zilpaterol and the involvement of the beta(2)-AR and cAMP were confirmed using a specific immunoassay.

Application of direct analysis in real time-orbitrap mass spectrometry combined with multivariate data analysis for rapid quality assessment of Yuanhu Zhitong Tablet

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

A new approach to cortical perfusion imaging is demonstrated using high-sensitivity thermography in conjunction with multivariate statistical data analysis. Local temperature changes caused by a cold bolus are imaged and transferred to a false color image. A cold bolus of 10 ml saline at ice temperature is injected systemically via a central venous access. During the injection, a sequence of 735 thermographic images are recorded within 2 min. The recorded data cube is subjected to a principal component analysis (PCA) to select slight changes of the cortical temperature caused by the cold bolus. PCA reveals that 11 s after injection the temperature of blood vessels is shortly decreased followed by an increase to the temperature before the cold bolus is injected. We demonstrate the potential of intraoperative thermography in combination with multivariate data analysis to image cortical cerebral perfusion without any markers. We provide the first in vivo application of multivariate thermographic imaging.

Hydroelectric generation is comprised of complex systems specifically designed to meet the dynamic load. Forced outages and unscheduled maintenance activities severely limit the generation output and oftentimes create undesired environmental effects within the immediate Dam/Reservoir area as well as the downstream surroundings. The introduction of multivariate descriptive data analysis and multi-criteria decision making in the maintenance sphere of hydroelectric generation are designed to eliminate reactive preservation methodologies while economically dispatching the unit. Moreover, the implementation of a correlation matrix for powertrain and auxiliary electrical systems produce a general method to localize the outage cause to a component level. Statistical regression techniques were used to evaluate the differences of inconsistencies between maintenance practices and theoretical systematic preservation methods experienced in the hydroelectric generation industry. The regression forecasting model minimizes the risks typically encountered in systems maintenance and prioritizes capital-intense projects within the power production envelope. Additionally, the application will assist the decision-makers with systematic and orderly ranking of projects competing for scarce resources (labor, material, and funding) over a multi-year period in a constrained power production environment. The identification of inadequate performing assets and its cost effectiveness throughout the electrical footprint is an important tenet of the program.

High quality extra virgin olive oils represent an optimal source of nutraceuticals. The European Union (EU) is the world’s leading olive oil producer, with the Mediterranean region as the main contributor. This makes the EU the greatest exporter and consumer of olive oil in the world. However, small olive oil producers also contribute to olive oil production. Beneficial effects on human health of extra virgin olive oil are well known, and these can be correlated to the presence of vitamin E and phenols. Together with the origin of the olives, extraction technology can influence the chemical composition of extra virgin olive oil. The aim of this study was to investigate the concentration of potentially bioactive compounds in Italian extra virgin olive oils from various sources. For this purpose, vitamin E and phenolic fractions were characterized using high-performance liquid chromatography (HPLC) coupled with fluorescence, photodiode array and mass spectrometry detection in fifty samples of oil pressed at industrial plants and sixty-six samples of oil produced in low-scale mills. Multivariate statistical data analysis was used to determine the applicability of selected phenolic compounds as potential quality indicators of extra virgin olive oils.

Multivariate approaches in plant science

There exists a sequence of stimulating overviews about (multivariate) data analysis and spatial problems (cf. Berry, 1971; Besag, 1975; Cliff and Ord, 1975; Cliff et al., 1975; Clark et al., 1974; Granger, 1975; Mather and Openshaw, 1974; Unwin and Hepple, 1974). Since I do not feel qualified enough in spatial analysis to add another paper to this admirable list, I have attempted a rather more limited topic: multivariate data analysis in situations where the observations cannot be assumed to be independent.

Nowadays, the use of unmanned aerial vehicles (UAVs) is becoming greater. The growing demand is related to economic considerations, as well as the capability of the UAVs to perform high-risk tasks. Detection of emergencies caused by onboard system failures is one of the main tasks in creating unmanned vehicles. To date, this problem is mainly solved by multiple redundancy. Due to the development of information technologies, intelligent methods of data analysis, namely, artificial neural networks, are becoming increasingly common. Multivariate data analysis using neural networks will allow the level of redundancy to be reduced. It will also allow solving a wide range of tasks in real time. This paper proposes a new approach to developing a comprehensive system for monitoring UAV condition. The approach is based on multivariate data analysis using neural networks. This system is designed to solve a number of tasks such as onboard equipment failure detection based on comprehensive measurement analysis, restoration of readings of faulty sensors, estimation of the aircraft condition, prediction and prevention of hazardous incidents. This system is also capable of detecting control system failure and finish the maneuver by taking over the control. This paper models the solution of these problems using simulated and real flight data.Nowadays, the use of unmanned aerial vehicles (UAVs) is becoming greater. The growing demand is related to economic considerations, as well as the capability of the UAVs to perform high-risk tasks. Detection of emergencies caused by onboard system failures is one of the main tasks in creating unmanned vehicles. To date, this problem is mainly solved by multiple redundancy. Due to the development of information technologies, intelligent methods of data analysis, namely, artificial neural networks, are becoming increasingly common. Multivariate data analysis using neural networks will allow the level of redundancy to be reduced. It will also allow solving a wide range of tasks in real time. This paper proposes a new approach to developing a comprehensive system for monitoring UAV condition. The approach is based on multivariate data analysis using neural networks. This system is designed to solve a number of tasks such as onboard equipment failure detection based on comprehensive measurement analysis, resto...

- The objective of this work was to evaluate physiological characteristics of sugar cane genotypes, as well as characterize them in groups according to their similarity, checking the ability of ecological adaptability of these genotypes. The work was performed in field conditions, being assessed ten sugarcane genotypes (RB855113, RB835486, RB867515, SP80-1816, RB72454, RB925345, RB855156, RB937570, RB947520 and RB925211) in a randomized block design with three repetitions. It were evaluated the stomatal gas flow rate (U - μ mol s -1 ), the concentration of under-stomatal CO 2 (Ci - μmol mol -1 ), the photosynthetic rate (A - μmol m -2 s -1 ), the CO 2 consumed (Δ C - μmol mol -1 ), the stomatal conductance (Gs - mol m -1 s -1 ), the temperature gradient between leaf and air (Δ T), and the transpiration rate (E - mol H 2 O m -2 s -1 ), being also calculated the water use efficiency (WUE - mol CO 2 mol H 2 O -1 ) from the values of the amount of CO 2 fixed by photosynthesis and amount of water transpirated. Both univariate and multivariate data analysis were made. The genotype SP80-1816 showed better water use efficiency, combined with low stomatal conductance and transpiratory rate. The cultivar RB855113 stood out by having high photosynthetic rate, and high consumption of CO 2 . The cultivar RB867515, in addition to showing high water use efficiency, also showed high photosynthetic rate. With respect to the multivariate analysis, the biotypes RB925345, RB925211, RB855156 and RB855113 are situated in different groups when compared to the others as to the physiological characteristics with respect to other genotypes with isolation in separate groups.

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Hyperspectral imaging coupled with multivariate data analysis is a powerful new tool for understanding complex biological and biomedical samples. The advantages and drawbacks of adding a spectral dimension and multivariate data analysis to optical microscopy for biological interrogation will be demonstrated with three applications - DNA microarrays, live cell imaging, and in-situ hybridization labeled tissue. These data are selected to present the type of impact hyperspectral imaging can have in biomedical science. Images are acquired using our state-of-the-art hyperspectral imaging system and multivariate data analysis is used to extract pure component spectra and corresponding independent concentration maps of all fluorescent species. In most cases the data analysis algorithms are successful with little or no information given a priori and generate images that are free of the influences of spectral crosstalk, cellular autofluorescence, and other background emissions that often plague traditional fluorescence microscopy.

Data manipulation and maximum efficient extraction of useful information need a range of searching, modeling, mathematical, and statistical approaches. Hence, an adequate multivariate characterization is the first necessary step in investigation and the results are interpreted after multivariate analysis. Multivariate data analysis is capable of not only large dataset management but also interpret them surely and rapidly. Application of chemometrics and cheminformatics methods may be useful for design and discovery of new drug compounds. In this review, we present a variety of information sources on chemometrics, which we consider useful in different fields of drug design. This review describes exploratory analysis (PCA), classification and multivariate calibration (PCR, PLS) methods to data analysis. It summarizes the main facts of linear and nonlinear multivariate data analysis in drug discovery and provides an introduction to manipulation of data in this field. It handles the fundamental aspects of basic concepts of multivariate methods, principles of projections (PCA and PLS) and introduces the popular modeling and classification techniques. Enough theory behind these methods, more particularly concerning the chemometrics tools is included for those with little experience in multivariate data analysis techniques such as PCA, PLS, SIMCA, etc. We describe each method by avoiding unnecessary equations, and details of calculation algorithms. It provides a synopsis of the method followed by cases of applications in drug design (i.e., QSAR) and some of the features for each method.

Discrimination of Samar and Talh honey produced in the Gulf Cooperation Council (GCC) region using multivariate data analysis

Radical Nephrectomy for Renal Cell Carcinoma: Long-Term Results and Prognostic Factors on a Series of 328 Cases