Multivariate statistical analysis of coastal sediment data

Erratum to “Multivariate statistical analysis of atom probe tomography data” [Ultramicroscopy 110 (2010) 1362

The 10 species of Galaxias in Tasmania, G. olidus from mainland Australia and the four species of Paragalaxias were studied using principal co‐ordinates analysis (PCOA) and cluster analysis of a standardized Euclidean distance matrix based upon variate means, and by canonical variate analysis (CVA) conducted as a stepwise multiple discriminant analysis. Thirty‐five variables comprising 30 morphometric and five meristic characters were analysed. The meristic characters were not included in the CVA. Excellent separation of the two genera was achieved in all analyses. The multivariate analyses were repeated on each genus separately to see if relationships suggested by the overall analysis remain stable. When the resultant groupings of species are compared for the different analyses, no consistent, distinct groupings of species within each genus are apparent. Despite the absence of distinct groupings, some trends in the affinities of some species are evident. In particular, species affinities as indicated by the CVA are more consistent with established opinions of species relationships. From the results of the study it is suggested that caution be exercised in the application of multivariate statistical analyses of morphological data to ichthyological systematics and phylogeny.

To investigate the gene regulation of tumor cells in the process of different organ metastasis on a xenograft mouse model and screen the genes involved in the organ-target metastasis of tumor cells. A multi-organ metastasis model was constructed with a human ovarian clear cell carcinoma cell line (ES-2) based on a severe immunodeficiency mouse strain (NCG). Differentially expressed tumor proteins among multi-organ metastases were successfully characterized by microliter liquid chromatography-high-resolution mass spectrometry, sequence-specific data analysis and multivariate statistical data analysis. Liver metastases were selected as typical for subsequent bioinformatic analysis. Selected liver metastasis-specific genes in ES-2 cells were validated by sequence-specific quantitation including high resolution-multiple reaction monitoring quantification at protein level and quantitative real-time polymerase chain reaction at mRNA level. From the mass spectrometry data, a total of 4503 human proteins were identified using the sequence-specific data analysis strategy. Of them, 158 proteins were selected as specifically regulated genes in liver metastases for subsequent bioinformatics studies. Based on Ingenuity Pathway Analysis (IPA) pathway analysis and sequence-specific quantitation, Ferritin light chain (FTL), lactate dehydrogenase A (LDHA) and long-chain-fatty-acid-CoA ligase 1 (ACSL1) were finally validated as specifically upregulated proteins in liver metastases. Our work provides a new approach to analyze gene regulation in tumor metastasis in xenograft mouse model. In presence of a large number of mouse protein interference, we validated the up-regulation of human ACSL1, FTL and LDHA in ES-2 liver metastases, which reflects the adaptive regulation of tumor cells to the liver microenvironment through metabolic reprogramming.

Fingerprint data from swine manure microbial community rRNAs and rRNA genes were treated by multivariate statistical and diversity analyses to differentiate swine manures. Microbial communities from finishing pig manure and from a mixture of manure slurries from maternity confinement and finishing pigs were characterized using a combination of amplicon length heterogeneity PCR (LH-PCR) and terminal restriction fragment length polymorphism (T-RFLP), using PCR primers targeting Bacteria and Archaea, respectively. Unweighted pair group method with arithmetic mean clustering, principal components analysis (PCA), indicator species analysis (ISA), and diversity analyses showed that rRNA-based fingerprinting methods [reverse transcription (RT)-LH-PCR and RT-T-RFLP] were more effective than rDNA-based fingerprinting methods for distinguishing the manure samples. Multiresponse permutation procedure from fingerprint data showed that all manure samples had distinct microbial communities. PCA and ISA showed that the major phylotypes differentiating the LH-PCR or the RT-LH-PCR profiles were distributed differently between manures, suggesting that the bacterial community structure was different from the metabolically active bacterial community. The detection of minor archaeal populations was greater using RT-T-RFLP instead of T-RFLP. The findings indicated that the analysis of microbial community rRNAs could differentiate each manure sample from the others and would be appropriate for the monitoring of metabolically active populations.

Classification of torbanite and cannel coal: II. Insights from pyrolysis-GC/MS and multivariate statistical analysis

Spatial variability of electronic transport in BiFeO3-CoFe2O4 (BFO-CFO) self-assembled heterostructures is explored using spatially resolved first-order reversal curve (FORC) current voltage (IV) mapping. Multivariate statistical analysis of FORC-IV data classifies statistically significant behaviors and maps characteristic responses spatially. In particular, regions of grain, matrix, and grain boundary responses are clearly identified. k-Means and Bayesian demixing analysis suggest the characteristic response be separated into four components, with hysteretic-type behavior localized at the BFO-CFO tubular interfaces. The conditions under which Bayesian components allow direct physical interpretation are explored, and transport mechanisms at the grain boundaries and individual phases are analyzed. This approach conjoins multivariate statistical analysis with physics-based interpretation, actualizing a robust, universal, data-driven approach to problem solving, which can be applied to exploration of local transport and other functional phenomena in other spatially inhomogeneous systems.

Extraction methods are critical in the production and success of an extra virgin olive oil ready for consumption. The first step of olive oil extraction procedure produces an olive paste, which is subsequently processed by separation of the liquid phases (oil and vegetable water) from the pomace. Profiles of Apulian extra virgin olive oils and the deuterated chloroform extracts of its paste and pomace were studied by 1D and 2D NMR (NMR) spectroscopy without separation and pre‐treatment of samples. In order to study the metabolic profiles variation in the paste and pomace extracts with respect to the corresponding EVOOs, multivariate statistical analyses (PCA and OPLS DA) of the1H NMR data were performed. As expected, a different minor components profile, in particular for the presence of higher content of secoiridoids, lignans and phenolic compounds in olive paste and pomace extracts with respect to the EVOOs, was observed.Practical applications:Amount of extractable oil from olive paste and pomace after the production processes of EVOO was measured by1H NMR spectroscopy. Identification of metabolites in olive paste and pomace was performed by 1D and 2D NMR spectroscopy without separation and pre‐treatment of samples. The chemical composition of major and minor components (in particular secoiridoids, lignans, and phenolic compounds) present in olive paste and pomace chloroform extracts could be used for extraction process trimming purposes as well as in fingerprint analyses for cultivar and/or geographical origin certification of olive oil production. Moreover, determination of the entire metabolic content of different materials of the olive oil production process could be exploited in other applications such as recycling on agricultural land, studies of the health benefits on human nutrition and alternative dietary lipid source in animals. In fact, it is worth noting that the olive paste and pomace extracts could be even healthier than other vegetable oils.The extraction processes are critical in the extra virgin olive oil production. The chemical composition of EVOOs and its paste and pomace extracts have been investigated by Nuclear Magnetic Resonance Spectroscopy (NMR).

Three-dimensional geological modelling and multivariate statistical analysis of water chemistry data to analyse and visualise aquifer structure and groundwater composition in the Wairau Plain, Marlborough District, New Zealand

Water containing high concentrations of nitrate is unfit for human consumption and, if discharging to freshwater or marine habitats, can contribute to algal blooms and eutrophication. The level of nitrate contamination in groundwater of two densely populated, agro-industrial areas of the Cameroon Volcanic Line (CVL) (Banana Plain and Mount Cameroon area) was evaluated. A total of 100 samples from boreholes, open wells and springs (67 from the Banana Plain; 33 from springs only, in the Mount Cameroon area) were collected in April 2009 and January 2010 and analyzed for chemical constituents, including nitrates. The average groundwater nitrate concentrations for the studied areas are: 17.28 mg/l for the Banana Plain and 2.90 mg/l for the Mount Cameroon area. Overall, groundwaters are relatively free from excessive nitrate contamination, with nitrate concentrations in only 6 % of groundwater resources in the Banana Plain exceeding the maximum admissible concentration for drinking water (50 mg/l). Sources of NO3− in groundwater of this region may be mainly anthropogenic (N-fertilizers, sewerage, animal waste, organic manure, pit latrines, etc.). Multivariate statistical analyses of the hydrochemical data revealed that three factors were responsible for the groundwater chemistry (especially, degree of nitrate contamination): (1) a geogenic factor; (2) nitrate contamination factor; (3) ionic enrichment factor. The impact of anthropogenic activities, especially groundwater nitrate contamination, is more accentuated in the Banana Plain than in the Mount Cameroon area. This study also demonstrates the usefulness of multivariate statistical analysis in groundwater study as a supplementary tool for interpretation of complex hydrochemical data sets.

Batch process data can be arranged in a three-way matrix (batch × variable × time). This paper provides a critical discussion of various aspects of the treatment of these multiway data. First, several methods that have been proposed for decomposing three-way data matrices are discussed in the context of batch process data analysis and monitoring. These methods are multiway principal component analysis (MPCA)—also called Tucker1—parallel factor analysis (PARAFAC) and Tucker3. Secondly, different ways of unfolding, mean centering and scaling the three-way matrix are compared and discussed with respect to their effects on the analysis of batch data. Finally, the role of the time variable in batch process data is considered and methods suggested to predict the per cent completion of batch runs with unequal duration are discussed. Copyright © 1999 John Wiley & Sons, Ltd.

Exploratory multivariate statistical analysis of geochemical data from the Cenomanian–Turonian transition of the Yezo Supergroup, Hokkaido, Japan

The knowledge of the isotopic composition of virgin olive oil (VOO) allows the evaluation of authenticity and geographical origin, being an important tool against fraud. This study aimed to assess if VOOs produced in three Mediterranean regions could be discriminated on the basis of multivariate statistical analysis of geoclimatic and isotopic data. A total of 138 geo-referenced VOO samples from Portugal, France and Turkey from two different cultivation years were collected. The isotopic composition (δ13C, δ2H and δ18O) of VOOs was obtained using an elemental analyzer coupled to an isotope ratio mass spectrometer (EA-IRMS). One-way analysis of variance for δ13C, δ2H and δ18O showed some significant differences either between crop years or geoclimatic conditions. Based on multiple regression analyses using meteorological and geographical parameters, a meteoric water line for olive oil from Portugal, France and Turkey, in two harvest years, were created to assess the impact of climate change on their δ2H and δ18O values. Principal component analysis and Linear Discriminant Analysis, used to sort samples according to geoclimatic origin, performed best for French and Portuguese olive oils. In light of the results, multivariate isotopic analysis of VOO samples may discriminate not only between geoclimatic regions but also among cultivation years.

Multivariate analysis of particulate sulfate and other air quality variables by principal components-Part I: Annual data from Los Angeles and New York

Metabolic composition of healthy and infected Grüner Veltliner grapes and wine assessed by high resolution mass spectrometry

Societal and political changes in European countries have led to a renewed scholarly interest in the relationship between individuals’ religious identification and attitudes toward immigrants and immigration. However, previous research on this topic did not reach a consistent conclusion. It remains unclear whether greater religious identification acts as ‘bridge’ and results in positive attitudes or as a ‘barrier’ which results in negative attitudes toward immigrants and immigration. The present cumulative dissertation dedicates itself to addressing this ambiguity. Multivariate statistical analyses of survey data have come to at least three major conclusions for individuals’ attitudes: First, the attitudes depends on the values religious individuals support and how threatened they generally feel by immigrants, not only on how strongly they identify with their religious group. Second, the attitudes differ in the context of external shocks that highlight the salience of religion and immigration. Third, religious identification does not only shape the attitudes of majority group members within the host population but also of minority group members, especially Muslim immigrants.