Evaluation of development traits of beef buffalo in Brazil using multivariate analysis

The buffalo population in Brazil increased about 12.9% between 1998 and 2003, to 2.8 million head, evidencing the importance of this species for the country. The objective this work was evaluation of animal growth using multivariate analysis. The data were from 2,944 water buffalo from 10 herds raised in pasture conditions in Brazil. Principal components and genetic distances were estimated using proc PRINCOMP and proc CANDISC in SAS (SAS Inst. Inc. Cary, NC, USA). Variables analyzed were birth weight (BW), age at weaning (AW), weaning weight (WT), weight adjusted to 205 d (W205), total gain between BW and WT (TG), daily gain between BW and WT (DG), weight adjusted to 365 d (W365), total gain between WT and W365 (TG3), daily gain between WT and W365 (TGD3), weight adjusted to 550 d (W550) and weight adjusted to 730 d (W730). Means and standard deviations for each variable were 39.4 +/- 3.2 kg, 225.6 +/- 38.8 d, 209.4 +/- 39.4 kg, 195.4 +/- 30.2 kg, 157.4 +/- 32.0 kg, 0.77 +/- 0.16 kg/d, 282.0 +/- 43.5 kg, 73.9 +/- 33.9 kg, 0.53 +/- 0.21 kg/d, 406.8 +/- 67.9 kg, and 468.2 +/- 70.6 kg, respectively. The eigenvalues to four first principal components were 5.29, 2.54, 1.66, 1.01, and justify 48%, 23%, 15% and 9%, respectively, with a total cumulative 95%. We created an index using the first principal component which is Y. 0.0552 BW + 0.0438 AW + 0.3142 WT + 0.3549 W205 + 0.3426 TG + 0.3426 DG + 0.4070 W365- 0.1531 TG3 - 0.2059 TGD3 - 0.3833 W550 - 0.3966 W730. This index accounted for 48% the variation in the correlation matrix. This principal component emphasizes early growth of the animal. Estimates the pair-wise squared distances between herds, D2(i vertical bar j)= ((x) over bar (i)-(x) over bar (j))' cov(-1)((x) over bar (i)-(x) over bar (j)), using with basis the average of weight of animals, showed the largest distance between herds eight (Murrah: DF) and seven (Murrah: Amazon) and the closest distance between herds one (Mediterranean - RS) and five (Jafarabadi - SP).

Chemometric analysis of Hymenoptera toxins and defensins: A model for predicting the biological activity of novel peptides from venoms and hemolymph

When searching for prospective novel peptides, it is difficult to determine the biological activity of a peptide based only on its sequence. The trial and error approach is generally laborious, expensive and time consuming due to the large number of different experimental setups required to cover a reasonable number of biological assays. To simulate a virtual model for Hymenoptera insects, 166 peptides were selected from the venoms and hemolymphs of wasps, bees and ants and applied to a mathematical model of multivariate analysis, with nine different chemometric components: GRAVY, aliphaticity index, number of disulfide bonds, total residues, net charge, pI value, Boman index, percentage of alpha helix, and flexibility prediction. Principal component analysis (PCA) with non-linear iterative projections by alternating least-squares (NIPALS) algorithm was performed, without including any information about the biological activity of the peptides. This analysis permitted the grouping of peptides in a way that strongly correlated to the biological function of the peptides. Six different groupings were observed, which seemed to correspond to the following groups: chemotactic peptides, mastoparans, tachykinins, kinins, antibiotic peptides, and a group of long peptides with one or two disulfide bonds and with biological activities that are not yet clearly defined. The partial overlap between the mastoparans group and the chemotactic peptides, tachykinins, kinins and antibiotic peptides in the PCA score plot may be used to explain the frequent reports in the literature about the multifunctionality of some of these peptides. The mathematical model used in the present investigation can be used to predict the biological activities of novel peptides in this system, and it may also be easily applied to other biological systems. © 2011 Elsevier Inc.

An analysis of the Hygiea asteroid family orbital region

(10) Hygiea is the fourth largest asteroid of the main belt, by volume and mass, and it is the largest member of its family, that is made mostly by low-albedo, C-type asteroids, typical of the outer main belt. Like many other large families, it is associated with a 'halo' of objects, that extends far beyond the boundary of the core family, as detected by traditional hierarchical clustering methods (HCM) in proper element domains. Numerical simulations of the orbital evolution of family members may help in estimating the family and halo family age, and the original ejection velocity field. But, in order to minimize the errors associated with including too many interlopers, it is important to have good estimates of family membership that include available data on local asteroid taxonomy, geometrical albedo and local dynamics. For this purpose, we obtained synthetic proper elements and frequencies of asteroids in the Hygiea orbital region, with their errors. We revised the current knowledge on asteroid taxonomy, including Sloan Digital Sky Survey-Moving Object Catalog 4th release (SDSS-MOC 4) data, and geometric albedo data from Wide-field Infrared Survey Explorer (WISE) and Near-Earth Object WISE (NEOWISE). We identified asteroid family members using HCM in the domain of proper elements (a, e, sin (i)) and in the domains of proper frequencies most appropriate to study diffusion in the local web of secular resonances, and eliminated possible interlopers based on taxonomic and geometrical albedo considerations. To identify the family halo, we devised a new hierarchical clustering method in an extended domain that includes proper elements, principal components PC1, PC2 obtained based on SDSS photometric data and, for the first time, WISE and NEOWISE geometric albedo. Data on asteroid size distribution, light curves and rotations were also revised for the Hygiea family. The Hygiea family is the largest group in its region, with two smaller families in proper element domain and 18 families in various frequencies domains identified in this work for the first time. Frequency groups tend to extend vertically in the (a, sin (i)) plane and cross not only the Hygiea family but also the near C-type families of Themis and Veritas, causing a mixture of objects all of relatively low albedo in the Hygiea family area. A few high-albedo asteroids, most likely associated with the Eos family, are also present in the region. Finally, the new multidomains hierarchical clustering method allowed us to obtain a good and robust estimate of the membership of the Hygiea family halo, quite separated from other asteroids families halo in the region, and with a very limited (about 3 per cent) presence of likely interlopers. © 2013 The Author Published by Oxford University Press on behalf of the Royal Astronomical Society.

Multivariate curve resolution analysis applied to time-resolved synchrotron X-ray Absorption Spectroscopy monitoring of the activation of copper alumina catalyst

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multivariate analysis of the temporal variability of sugarcane ripening in south-eastern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures

Quality of fresh-cut carambola (Averrhoa carambola L) is related to many chemical and biochemical variables especially those involved with softening and browning, both influenced by storage temperature. To study these effects, a multivariate analysis was used to evaluate slices packaged in vacuum-sealed polyolefin bags, and stored at 2.5 degrees C, 5 degrees C and 10 degrees C, for up to 16 d. The quality of slices at each temperature was correlated with the duration of storage, O(2) and CO(2) concentration in the package, physical chemical constituents, and activity of enzymes involved in softening (PG) and browning (PPO) metabolism. Three quality groups were identified by hierarchical cluster analysis, and the classification of the components within each of these groups was obtained from a principal component analysis (PCA). The characterization of samples by PCA clearly distinguished acceptable and non-acceptable slices. According to PCA, acceptable slices presented higher ascorbic acid content, greater hue angles ((o)h) and final lightness (L-5) in the first principal component (PC1). On the other hand, non-acceptable slices presented higher total pectin content. PPO activity in the PC1. Non-acceptable slices also presented higher soluble pectin content, increased pectin solubilisation and higher CO(2) concentration in the second principal component (PC2) whereas acceptable slices showed lower total sugar content. The hierarchical cluster and PCA analyses were useful for discriminating the quality of slices stored at different temperatures. (C) 2011 Elsevier B.V. All rights reserved.

Determination of bioactive and potentially toxic compounds in food: raw material analysis and shelf life evaluation

"Bioactive compounds" are extranutritional constituents that typically occur in small quantities in food. They are being intensively studied to evaluate their effects on health. Bioactive compounds include both water soluble compounds, such as phenolics, and lipidic substances such as n-3 fatty acids, tocopherols and sterols. Phenolic compounds, tocopherols and sterols are present in all plants and have been studied extensively in cereals, nuts and oil. n-3 fatty acids are present in fish and all around the vegetable kingdom. The aim of the present work was the determination of bioactive and potentially toxic compounds in cereal based foods and nuts. The first section of this study was focused on the determination of bioactive compounds in cereals. Because of that the different forms of phytosterols were investigated in hexaploid and tetraploid wheats. Hexaploid cultivars were the best source of esterified sterols (40.7% and 37.3% of total sterols for Triticum aestivum and Triticum spelta, respectively). Significant amounts of free sterols (65.5% and 60.7% of total sterols for Triticum durum and Triticum dicoccon, respectively) were found in the tetraploid cultivars. Then, free and bound phenolic compounds were identified in barley flours. HPLCESI/ MSD analysis in negative and positive ion mode established that barley free flavan-3- ols and proanthocyanidins were four dimers and four trimers having (epi)catechin and/or (epi)gallocatechin (C and/or GC) subunits. Hydroxycinnamic acids and their derivatives were the main bound phenols in barley flours. The results obtained demonstrated that barley flours were rich in phenolic compounds that showed high antioxidant activity. The study also examined the relationships between phenolic compounds and lipid oxidation of bakery. To this purpose, the investigated barley flours were used in the bakery production. The formulated oven products presented an interesting content of phenolic compounds, but they were not able to contain the lipid oxidation. Furthermore, the influence of conventional packaging on lipid oxidation of pasta was evaluated in n-3 enriched spaghetti and egg spaghetti. The results proved that conventional packaging was not appropriated to preserve pasta from lipid oxidation; in fact, pasta that was exposed to light showed a high content of potentially toxic compounds derived from lipid oxidation (such as peroxide, oxidized fatty acids and COPs). In the second section, the content of sterols, phenolic compounds, n-3 fatty acids and tocopherols in walnuts were reported. Rapid analytical techniques were used to analyze the lipid fraction and to characterize phenolic compounds in walnuts. Total lipid chromatogram was used for the simultaneous determination of the profile of sterols and tocopherols. Linoleic and linolenic acids were the most representative n-6 and n-3 essential dietary fatty acids present in these nuts. Walnuts contained substantial amounts of γ- and δ-tocopherol, which explained their antioxidant properties. Sitosterol, Δ5-avenasterol and campesterol were the major free sterols found. Capillary electrophoresis coupled to DAD and microTOF was utilized to determine phenolic content of walnut. A new compound in walnut ((2E,4E)- 8-hydroxy-2,7-dimethyl-2,4-decadiene-1,10-dioic acid 6-O-β-D-glucopiranosyl ester, [M−H]− 403.161m/z) with a structure similar to glansreginins was also identified. Phenolic compounds corresponded to 14–28% of total polar compounds quantified. Aglycone and glycosylated ellagic acid represented the principal components and account for 64–75% of total phenols in walnuts. However, the sum of glansreginins A, B and ((2E,4E)-8-hydroxy- 2,7-dimethyl-2,4-decadiene-1,10-dioic acid 6-O-β-D-glucopiranosyl ester was in the range of 72–86% of total quantified compounds.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Quantitative analysis of imprint shape and its relation to mechanical properties measured by microindentation in bone

Microindentation in bone is a micromechanical testing technique routinely used to extract material properties related to bone quality. As the analysis of microindentation data is based on assumptions about the contact between sample and surface, the aim of this study was to quantify the topological variability of indentations in bone and examine its relationship with mechanical properties. Indentations were performed in dry human and ovine bone in axial and transverse directions and their topology was measured by atomic force microscopy. Statistical shape modeling of the residual imprint allowed to define a mean shape and to describe the variability in terms of 21 principal components related to imprint depth, surface curvature and roughness. The indentation profile of bone was found to be highly consistent and free of any pile up while differing mostly by depth between species and direction. A few of the topological parameters, in particular depth, showed significant but rather weak and inconsistent correlations to variations in mechanical properties. The mechanical response of bone as well as the residual imprint shape was highly consistent within each category. We could thus verify that bone is rather homogeneous in its micromechanical properties and that indentation results are not strongly influenced by small deviations from an ideally flat surface.

Nonlinear dimension reduction in pathway-based genome-wide association analysis

Pathway based genome wide association study evolves from pathway analysis for microarray gene expression and is under rapid development as a complementary for single-SNP based genome wide association study. However, it faces new challenges, such as the summarization of SNP statistics to pathway statistics. The current study applies the ridge regularized Kernel Sliced Inverse Regression (KSIR) to achieve dimension reduction and compared this method to the other two widely used methods, the minimal-p-value (minP) approach of assigning the best test statistics of all SNPs in each pathway as the statistics of the pathway and the principal component analysis (PCA) method of utilizing PCA to calculate the principal components of each pathway. Comparison of the three methods using simulated datasets consisting of 500 cases, 500 controls and100 SNPs demonstrated that KSIR method outperformed the other two methods in terms of causal pathway ranking and the statistical power. PCA method showed similar performance as the minP method. KSIR method also showed a better performance over the other two methods in analyzing a real dataset, the WTCCC Ulcerative Colitis dataset consisting of 1762 cases, 3773 controls as the discovery cohort and 591 cases, 1639 controls as the replication cohort. Several immune and non-immune pathways relevant to ulcerative colitis were identified by these methods. Results from the current study provided a reference for further methodology development and identified novel pathways that may be of importance to the development of ulcerative colitis.^

Blind separation of auditory event-related brain responses into independent components

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.

Chemometric modelling to relate antioxidants, neutral lipid fatty acids and flavour components in chicken breast

Relationships among quality factors in retailed free-range, corn-fed, organic, and conventional chicken breasts (9) were modeled using chemometric approaches. Use of principal component analysis (PCA) to neutral lipid composition data explained the majority (93%) of variability (variance) in fatty acid contents in 2 significant multivariate factors. PCA explained 88 and 75% variance in 3 factors for, respectively, flame ionization detection (FID) and nitrogen phosphorus (NPD) components in chromatographic flavor data from cooked chicken after simultaneous distillation extraction. Relationships to tissue antioxidant contents were modeled. Partial least square regression (PLS2), interrelating total data matrices, provided no useful models. By using single antioxidants as Y variables in PLS (1), good models (r2 values > 0.9) were obtained for alpha-tocopherol, glutathione, catalase, glutathione peroxidase, and reductase and FID flavor components and among the variables total mono and polyunsaturated fatty acids and subsets of FID, and saturated fatty acid and NPD components. Alpha-tocopherol had a modest (r2 = 0.63) relationship with neutral lipid n-3 fatty acid content. Such factors thus relate to flavor development and quality in chicken breast meat.