A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods

Ulcerative colitis and irritable bowel patients exhibit distinct abnormalities of the gut microbiota

<p>Abstract</p> <p>Background</p> <p>Previous studies suggest a link between gut microbiota and the development of ulcerative colitis (UC) and irritable bowel syndrome (IBS). Our aim was to investigate any quantitative differences in faecal bacterial compositions in UC and IBS patients compared to healthy controls, and to identify individual bacterial species that contribute to these differences.</p> <p>Methods</p> <p>Faecal microbiota of 13 UC patients, 11 IBS patients and 22 healthy volunteers were analysed by PCR-Denaturing Gradient Gel Electrophoresis (DGGE) using universal and Bacteroides specific primers. The data obtained were normalized using in-house developed statistical method and interrogated by multivariate approaches. The differentiated bands were excised and identified by sequencing the V3 region of the 16S rRNA genes.</p> <p>Results</p> <p>Band profiles revealed that number of predominant faecal bacteria were significantly different between UC, IBS and control group (p < 10^-4). By assessing the mean band numbers in UC (37 ± 5) and IBS (39 ± 6), compared to the controls (45 ± 3), a significant decrease in bacterial species is suggested (p = 0.01). There were no significant differences between IBS and UC. Biodiversity of the bacterial species was significantly lower in UC (μ = 2.94, σ = 0.29) and IBS patients (μ = 2.90, σ = 0.38) than controls (μ = 3.25, σ = 0.16; p = 0.01). Moreover, similarity indices revealed greater biological variability of predominant bacteria in UC and IBS compared to the controls (median Dice coefficients 76.1% (IQR 70.9 - 83.1), 73.8% (IQR 67.0 - 77.5) and 82.9% (IQR 79.1 - 86.7) respectively). DNA sequencing of discriminating bands suggest that the presence of <it>Bacteroides vulgatus, B. ovatus, B. uniformis</it>, and <it>Parabacteroides sp</it>. in healthy volunteers distinguishes them from IBS and UC patients. DGGE profiles of Bacteroides species revealed a decrease of Bacteroides community in UC relative to IBS and controls.</p> <p>Conclusion</p> <p>Molecular profiling of faecal bacteria revealed abnormalities of intestinal microbiota in UC and IBS patients, while different patterns of Bacteroides species loss in particular, were associated with UC and IBS.</p

Chemical, functional, and structural properties of spent coffee grounds and coffee silverskin

Spent coffee grounds (SCG) and coffee silverskin (CS) represent a great pollution hazard if discharged into the environment. Taking this fact into account, the purpose of this study was to evaluate the chemical composition, functional properties, and structural characteristics of these agro-industrial residues in order to identify the characteristics that allow their reutilization in industrial processes. According to the results, SCG and CS are both of lignocellulosic nature. Sugars polymerized to their cellulose and hemicellulose fractions correspond to 51.5 and 40.45 % w/w, respectively; however, the hemicellulose sugars and their composition significantly differ from one residue to another. SCG and CS particles differ in terms of morphology and crystallinity, but both materials have very low porosity and similar melting point. In terms of functional properties, SCG and CS present good water and oil holding capacities, emulsion activity and stability, and antioxidant potential, being therefore great candidates for use on food and pharmaceutical fields.The authors acknowledge the financial support of the Science and Technology Foundation of Portugal (FCT) through the grant SFRH/BD/80948/2011 and the Strategic Project PEst-OE/EQB/LA0023/2013. The authors also thank the Project "BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes", REF. NORTE-07-0124-FEDER-000028 co-funded by the Programa Operacional Regional do Norte (ON.2-O Novo Norte), QREN, FEDER. Thanks are also given to Prof. Jose J.M. Orfao, from the Department of Chemical Engineering, Universidade do Porto (Portugal), for his assistance with the porosity analyses

1H-NMR-Based Metabolic Profiling of Maternal and Umbilical Cord Blood Indicates Altered Materno-Foetal Nutrient Exchange in Preterm Infants

Background: Adequate foetal growth is primarily determined by nutrient availability, which is dependent on placental nutrient transport and foetal metabolism. We have used 1H nuclear magnetic resonance (NMR) spectroscopy to probe the metabolic adaptations associated with premature birth. Methodology: The metabolic profile in 1H NMR spectra of plasma taken immediately after birth from umbilical vein, umbilical artery and maternal blood were recorded for mothers delivering very-low-birth-weight (VLBW) or normo-ponderal full-term (FT) neonates. Principal Findings: Clear distinctions between maternal and cord plasma of all samples were observed by principal component analysis (PCA). Levels of amino acids, glucose, and albumin-lysyl in cord plasma exceeded those in maternal plasma, whereas lipoproteins (notably low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) and lipid levels were lower in cord plasma from both VLBW and FT neonates. The metabolic signature of mothers delivering VLBW infants included decreased levels of acetate and increased levels of lipids, pyruvate, glutamine, valine and threonine. Decreased levels of lipoproteins glucose, pyruvate and albumin-lysyl and increased levels of glutamine were characteristic of cord blood (both arterial and venous) from VLBW infants, along with a decrease in levels of several amino acids in arterial cord blood. Conclusion: These results show that, because of its characteristics and simple non-invasive mode of collection, cord plasma is particularly suited for metabolomic analysis even in VLBW infants and provides new insights into the materno-foetal nutrient exchange in preterm infants

Comparison of geometric morphometric outline methods in the discrimination of age-related differences in feather shape

BACKGROUND: Geometric morphometric methods of capturing information about curves or outlines of organismal structures may be used in conjunction with canonical variates analysis (CVA) to assign specimens to groups or populations based on their shapes. This methodological paper examines approaches to optimizing the classification of specimens based on their outlines. This study examines the performance of four approaches to the mathematical representation of outlines and two different approaches to curve measurement as applied to a collection of feather outlines. A new approach to the dimension reduction necessary to carry out a CVA on this type of outline data with modest sample sizes is also presented, and its performance is compared to two other approaches to dimension reduction. RESULTS: Two semi-landmark-based methods, bending energy alignment and perpendicular projection, are shown to produce roughly equal rates of classification, as do elliptical Fourier methods and the extended eigenshape method of outline measurement. Rates of classification were not highly dependent on the number of points used to represent a curve or the manner in which those points were acquired. The new approach to dimensionality reduction, which utilizes a variable number of principal component (PC) axes, produced higher cross-validation assignment rates than either the standard approach of using a fixed number of PC axes or a partial least squares method. CONCLUSION: Classification of specimens based on feather shape was not highly dependent of the details of the method used to capture shape information. The choice of dimensionality reduction approach was more of a factor, and the cross validation rate of assignment may be optimized using the variable number of PC axes method presented herein

Discriminant analysis of high-dimensional data:A comparison of principal components analysis and partial least squares data reduction methods

Partial least squares (PLS) methods are presented as valuable alternatives to principal components analysis (PCA) for compressing high-dimensional data before performing linear discriminant analysis (LDA). It is shown that using PLS, considerable improvement in class separation and thus discriminant ability can be obtained. In general, fewer of the compressed dimensions are required to give the same level of prediction successes, and for some data sets, PLS methods yield higher prediction success rates than those obtainable using PCA scores. Results are presented for two experimental data sets, comprising mid-infrared spectra of edible oils and plant seeds. The potential dangers of PLS methods are also demonstrated, in particular its ability to introduce apparent groupings into data where there is no inherent class structure