Variant Of Principal Component Analysis Research Articles

Independent principal component analysis for simulation of soil water content and bulk density in a Canadian Watershed

Accurate characterization of soil properties such as soil water content (SWC) and bulk density (BD) is vital for hydrologic processes and thus, it is importance to estimate θ (water content) and ρ (soil bulk density) among other soil surface parameters involved in water retention and infiltration, runoff generation and water erosion, etc. The spatial estimation of these soil properties are important in guiding agricultural management decisions. These soil properties vary both in space and time and are correlated. Therefore, it is important to find an efficient and robust technique to simulate spatially correlated variables. Methods such as principal component analysis (PCA) and independent component analysis (ICA) can be used for the joint simulations of spatially correlated variables, but they are not without their flaws. This study applied a variant of PCA called independent principal component analysis (IPCA) that combines the strengths of both PCA and ICA for spatial simulation of SWC and BD using the soil data set from an 11km2 Castor watershed in southern Quebec, Canada. Diagnostic checks using the histograms and cumulative distribution function (cdf) both raw and back transformed simulations show good agreement. Therefore, the results from this study has potential in characterization of water content variability and bulk density variation for precision agriculture.

Joint sparse principal component analysis

Principal component analysis (PCA) is widely used in dimensionality reduction. A lot of variants of PCA have been proposed to improve the robustness of the algorithm. However, the existing methods either cannot select the useful features consistently or is still sensitive to outliers, which will depress their performance of classification accuracy. In this paper, a novel approach called joint sparse principal component analysis (JSPCA) is proposed to jointly select useful features and enhance robustness to outliers. In detail, JSPCA relaxes the orthogonal constraint of transformation matrix to make it have more freedom to jointly select useful features for low-dimensional representation. JSPCA imposes joint sparse constraints on its objective function, i.e., ℓ2,1-norm is imposed on both the loss term and the regularization term, to improve the algorithmic robustness. A simple yet effective optimization solution is presented and the theoretical analyses of JSPCA are provided. The experimental results on eight data sets demonstrate that the proposed approach is feasible and effective.

Deriving Shape-Based Features for C. elegans Locomotion Using Dimensionality Reduction Methods.

High-throughput analysis of animal behavior is increasingly common following the advances of recording technology, leading to large high-dimensional data sets. This dimensionality can sometimes be reduced while still retaining relevant information. In the case of the nematode worm Caenorhabditis elegans, more than 90% of the shape variance can be captured using just four principal components. However, it remains unclear if other methods can achieve a more compact representation or contribute further biological insight to worm locomotion. Here we take a data-driven approach to worm shape analysis using independent component analysis (ICA), non-negative matrix factorization (NMF), a cosine series, and jPCA (a dynamic variant of principal component analysis [PCA]) and confirm that the dimensionality of worm shape space is close to four. Projecting worm shapes onto the bases derived using each method gives interpretable features ranging from head movements to tail oscillation. We use these as a comparison method to find differences between the wild type N2 worms and various mutants. For example, we find that the neuropeptide mutant nlp-1(ok1469) has an exaggerated head movement suggesting a mode of action for the previously described increased turning rate. The different bases provide complementary views of worm behavior and we expect that closer examination of the time series of projected amplitudes will lead to new results in the future.

Applicability of Under Vacuum Fresh Tissue Sealing and Cooling to Omics Analysis of Tumor Tissues.

Biobanks of frozen human normal and malignant tissues represent a valuable source for "omics" analysis in translational cancer research and molecular pathology. However, the success of molecular and cellular analysis strongly relies on the collection, handling, storage procedures, and quality control of fresh human tissue samples. We tested whether under vacuum storage (UVS) effectively preserves tissues during the time between surgery and storage for "omics" analyses. Normal and matched tumor specimens, obtained from 16 breast, colon, or lung cancer patients and 5 independent mesenchymal tumors, were dissected within 20 minutes from surgical excision and divided in three to five aliquots; for each tissue sample, one aliquot was snap-frozen in liquid nitrogen (defined as baseline or T0 samples), and the other portions were sealed into plastic bags and kept at 4°C for 1, 24, 48, or 72 hours under vacuum and then frozen. The tissue and molecular preservation under vacuum was evaluated over time in terms of histomorphology, transcription (Illumina microarrays), protein (surface-enhanced laser desorption/ionization-time of flight/mass spectrometry and Western blot), and metabolic profile (nuclear magnetic resonance spectroscopy). Tissue morphology, Mib-1, and vimentin immunostaining were preserved over time without signs of tissue degradation. Principal variance component analysis showed that time of storage had a minimal effect on gene expression or the proteome, but affected the preservation of some metabolites to a greater extent. UVS did not impact the RNA and protein integrity or specific phosphorylation sites on mTOR and STAT3. Measurement of metabolites revealed pronounced changes after 1 hour of storage. Our results show that UVS can preserve tissue specimens for histological, transcriptomic, and proteomic examinations up to 48 hours and possibly longer, whereas it has limitations for metabolomic applications.

Calculation of the Total Sulfur Content in Crude Oils by Positive-Ion Atmospheric Pressure Photoionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Herein, a method to calculate the total sulfur concentration in petroleum samples from the chemometric modeling of data obtained by positive-ion atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry [(+) APPI FT-ICR MS] is described. Analysis by FT-ICR MS provides both a measurement of the total sulfur concentration and detailed molecular-level speciation of sulfur-containing compounds. A total of 30 crude oil samples ranging from 0.2 to 4.6 wt % sulfur were employed to train the sulfur prediction model. The ratios of the percent relative abundance (%RA) between the sulfur classes (Sx) and the hydrocarbon (HC) class were employed as variables for principal component analysis (PCA). The PCA results reveal a highly linear trend along the principal component with the highest explained variance (PC1). Analysis of the loadings plot reveals that the S1/HC ratio governs the trend in PC1. Values for PC2 are governed by S1/HC, S2/HC, and S3/HC ratios and provide the abili...

Principal component analysis: a review and recent developments.

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.

Network Intrusion Detection System Using PCA by Lp-Norm Maximization Based on Conjugate Gradient

Due to the fast growing of computer networks the potential for attacking those networks also became important. Therefore, all enterprises should implement various systems that supervise their network infrastructure security. To detect any eventual attacks, many Intrusion Detection Systems (IDSs) have been used in recent years. However, the most of them operate more often on enormous network traffic data with multiple redundant features. As a result, the IDS generates a high false alarms rate, which makes the intrusion detection inefficient and imprecise. To overcome that, several techniques for data dimensionality reduction have been proposed, such as Principal Component Analysis (PCA). Nonetheless, the classical PCA approach that is based on the L2-norm maximization is very sensitive to outliers. As a solution to this weakness, we propose to introduce a new variant of PCA called PCA Lp-norm using conjugate gradient algorithm to solve the Lp-norm optimization problem. The main idea behind this new method relies on the Lp-norm, which is more robust to the presence of outliers in data. Extensive experiments on two well-known datasets namely KDDcup99 and NSL-KDD prove the effectiveness of the proposed approach in terms of network attacks detection, false alarms reduction and CPU time minimization.

Application of 1H NMR Profiling To Assess Seed Metabolomic Diversity. A Case Study on a Soybean Era Population

(1)H NMR spectroscopy offers advantages in metabolite quantitation and platform robustness when applied in food metabolomics studies. This paper provides a (1)H NMR-based assessment of seed metabolomic diversity in conventional and glyphosate-resistant genetically modified (GM) soybean from a genetic lineage representing ∼35 years of breeding and differing yield potential. (1)H NMR profiling of harvested seed allowed quantitation of 27 metabolites, including free amino acids, sugars, and organic acids, as well as choline, O-acetylcholine, dimethylamine, trigonelline, and p-cresol. Data were analyzed by canonical discriminant analysis (CDA) and principal variance component analysis (PVCA). Results demonstrated that (1)H NMR spectroscopy was effective in highlighting variation in metabolite levels in the genetically diverse sample set presented. The results also confirmed that metabolite variability is influenced by selective breeding and environment, but not genetic modification. Therefore, metabolite variability is an integral part of crop improvement that has occurred for decades and is associated with a history of safe use.

Hierarchical sampling for metastable conformers determines biomolecular recognition: the case of malectin and diglucosylated N-glycan interactions

Structural information over the entire course of binding interactions based on the analyses of energy landscapes is described, which provides a framework to understand the events involved during biomolecular recognition. Conformational dynamics of malectin’s exquisite selectivity for diglucosylated N-glycan (Dig-N-glycan), a highly flexible oligosaccharide comprising of numerous dihedral torsion angles, are described as an example. For this purpose, a novel approach based on hierarchical sampling for acquiring metastable molecular conformations constituting low-energy minima for understanding the structural features involved in a biologic recognition is proposed. For this purpose, four variants of principal component analysis were employed recursively in both Cartesian space and dihedral angles space that are characterized by free energy landscapes to select the most stable conformational substates. Subsequently, k-means clustering algorithm was implemented for geometric separation of the major native state to acquire a final ensemble of metastable conformers. A comparison of malectin complexes was then performed to characterize their conformational properties. Analyses of stereochemical metrics and other concerted binding events revealed surface complementarity, cooperative and bidentate hydrogen bonds, water-mediated hydrogen bonds, carbohydrate–aromatic interactions including CH–π and stacking interactions involved in this recognition. Additionally, a striking structural transition from loop to β-strands in malectin CRD upon specific binding to Dig-N-glycan is observed. The interplay of the above-mentioned binding events in malectin and Dig-N-glycan supports an extended conformational selection model as the underlying binding mechanism.

Population Proportion of Explained Variance in Principal Component Analysis: A Note on Its Evaluation Via a Large-Sample Approach

A large-sample procedure for examining explained population variance in principal component analysis is discussed. The method yields interval estimates for the proportions of variance accounted for by the considered for retention principal components. The obtained confidence intervals can be used to make decisions about the number of selected components, based on their performance in studied populations. The approach can also be employed for ascertaining group similarity or differences in percentage explained variance by the considered principal components. The procedure is illustrated with 2 examples.

Characterization of near-infrared spectral variance in the authentication of skim and nonfat dry milk powder collection using ANOVA-PCA, pooled-ANOVA, and partial least-squares regression.

Forty-one samples of skim milk powder (SMP) and nonfat dry milk (NFDM) from 8 suppliers, 13 production sites, and 3 processing temperatures were analyzed by NIR diffuse reflectance spectrometry over a period of 3 days. NIR reflectance spectra (1700–2500 nm) were converted to pseudoabsorbance and examined using (a) analysis of variance-principal component analysis (ANOVA-PCA), (b) pooled-ANOVA based on data submatrices, and (c) partial least-squares regression (PLSR) coupled with pooled-ANOVA. ANOVA-PCA score plots showed clear separation of the samples with respect to milk class (SMP or NFDM), day of analysis, production site, processing temperature, and individual samples. Pooled-ANOVA provided statistical levels of significance for the separation of the averages, some of which were many orders of magnitude below 10–3. PLSR showed that the correlation with Certificate of Analysis (COA) concentrations varied from a weak coefficient of determination (R2) of 0.32 for moisture to moderate R2 values of 0.61 for fat and 0.78 for protein for this multinational study. In this study, pooled-ANOVA was applied for the first time to PLS modeling and demonstrated that even though the calibration models may not be precise, the contribution of the protein peaks in the NIR spectra accounted for the largest proportion of the variation despite the inherent imprecision of the COA values.

Applying robust variant of Principal Component Analysis as a damage detector in the presence of outliers

Using Principal Component Analysis (PCA) for Structural Health Monitoring (SHM) has received considerable attention over the past few years. PCA has been used not only as a direct method to identify, classify and localize damages but also as a significant primary step for other methods. Despite several positive specifications that PCA conveys, it is very sensitive to outliers. Outliers are anomalous observations that can affect the variance and the covariance as vital parts of PCA method. Therefore, the results based on PCA in the presence of outliers are not fully satisfactory. As a main contribution, this work suggests the use of robust variant of PCA not sensitive to outliers, as an effective way to deal with this problem in SHM field. In addition, the robust PCA is compared with the classical PCA in the sense of detecting probable damages. The comparison between the results shows that robust PCA can distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structures. Moreover, different types of robust PCA are compared with each other as well as with classical counterpart in the term of damage detection. All the results are obtained through experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and adding simulated damages.

"His" and "her" marriage? The role of positive and negative marital characteristics in global marital satisfaction among older adults.

We explore gender differences in older adults' appraisals of positive and negative aspects of their marriages, examine how these appraisals relate to global marital satisfaction, and identify distinctive marital profiles associated with global satisfaction in men and women. Data are from the Changing Lives of Older Couples Study (n = 1,110). We used a variant of principal components analysis to generate marital quality profiles, based on one's endorsement of positive and negative marital characteristics. OLS regression was used to detect associations between marital profiles and global marital satisfaction. Men offered more positive marital assessments than women, particularly on items reflecting positive treatment by one's wife. Three marital quality profiles emerged: Positive, Positive-Negative, and Negative. Although marital satisfaction was best explained by positive appraisals in both genders, they were less important for men than for women. The negative profile showed a tendency for a stronger prediction in men. Prior studies show small differences in men's and women's global marital satisfaction. Our work provides evidence that the presence and magnitude of such gender differences may vary based on the specific marital component considered. We discuss ways that gender shapes marital interactions, expectations, and perceptions, and the implications of our results for the well-being of married older adults.

Identification of microorganisms based on headspace analysis of volatile organic compounds by gas chromatography–mass spectrometry

The identification of specific volatile organic compounds (VOCs) produced by microorganisms may assist in developing a fast and accurate methodology for the determination of pulmonary bacterial infections in exhaled air. As a first step, pulmonary bacteria were cultured and their headspace analyzed for the total amount of excreted VOCs to select those compounds which are exclusively associated with specific microorganisms. Development of a rapid, noninvasive methodology for identification of bacterial species may improve diagnostics and antibiotic therapy, ultimately leading to controlling the antibiotic resistance problem. Two hundred bacterial headspace samples from four different microorganisms (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumoniae) were analyzed by gas chromatography–mass spectrometry to detect a wide array of VOCs. Statistical analysis of these volatiles enabled the characterization of specific VOC profiles indicative for each microorganism. Differences in VOC abundance between the bacterial types were determined using ANalysis of VAriance-principal component analysis (ANOVA-PCA). These differences were visualized with PCA. Cross validation was applied to validate the results. We identified a large number of different compounds in the various headspaces, thus demonstrating a highly significant difference in VOC occurrence of bacterial cultures compared to the medium and between the cultures themselves. Additionally, a separation between a methicillin-resistant and a methicillin-sensitive isolate of S. aureus could be made due to significant differences between compounds. ANOVA-PCA analysis showed that 25 VOCs were differently profiled across the various microorganisms, whereas a PCA score plot enabled the visualization of these clear differences between the bacterial types. We demonstrated that identification of the studied microorganisms, including an antibiotic susceptible and resistant S. aureus substrain, is possible based on a selected number of compounds measured in the headspace of these cultures. These in vitro results may translate into a breath analysis approach that has the potential to be used as a diagnostic tool in medical microbiology.

Advection modes by optimal mass transfer.

Classical model reduction techniques approximate the solution of a physical model by a limited number of global modes. These modes are usually determined by variants of principal component analysis. Global modes can lead to reduced models that perform well in terms of stability and accuracy. However, when the physics of the model is mainly characterized by advection, the nonlocal representation of the solution by global modes essentially reduces to a Fourier expansion. In this paper we describe a method to determine a low-order representation of advection. This method is based on the solution of Monge-Kantorovich mass transfer problems. Examples of application to point vortex scattering, Korteweg-de Vries equation, and hurricane Dean advection are discussed.

Ana bileşenler analizi ile panik bozukluğu alt tiplerinin değerlendirilmesi

Evaluating the subtypes of panic disorder by using principal component analysis Objective: Panic disorder is put forward to have a heterogeneous structure and multiple subtypes. Evaluating the different subtypes is important for diagnosis, treatment and prognosis. Studies about the quantitative psychopathology assessment are so rare. We aim to determine the subtypes with principal component analysis of panic attack symptoms. Method: 159 outpatients with panic disorder who had referred to the anxiety disorder outpatient unit in Bakirkoy Research and Training Hospital for Psychiatry, Neurology and Neurosurgery were included in the study. A sociodemographic questionnaire that was prepared by the investigators, The Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), Panic and Agoraphobia Scale (PAS), Hamilton Depression Rating Scale (HAM-D), Beck Anxiety Inventory (BAI) were used in the study. Variables with 15 items were created adding ‘Fear of stroke’ and ‘desire to escape, restlessness’. Principal Component Analysis was applied according to the panic symptoms that the patiens declared. Results: Six loading factors were obtained explaning 56% of total variance in Principal Component Analysis. The first set was named autonomic activation, the second was vestibular symptoms, the third was cardiovascular symptoms, the fourth was pseudoneurologic symptoms, the fifth was respiratory system symptoms and the sixth was fear of death. Conclusion: Symptom profiles in our study were compatible with DSM-IV-TR diagnostic criteria. Significant panic disorder subtypes were found in principal component analysis.

Investigation of Biochemical Diversity in a Soybean Lineage Representing 35 Years of Breeding

This paper reports an assessment of seed biochemical and metabolite variability and diversity in a series of nine soybean varieties; all lines share the same genetic lineage but represent ∼35 years of breeding (launch years 1972-2008) and differing yield potentials. These varieties, including six conventional and three glyphosate-tolerant lines, were grown concurrently at two replicated field sites in the United States during the 2011 growing season, and seeds were harvested at maturity. A compositional assessment included measurement of proximates, amino acids, fatty acids, tocopherols, isoflavones, saccharides, organic acids, and selected phytohormones. Statistical analysis included application of principal variance component analysis (PVCA) to investigate the interrelationships among compositional components from these soybean varieties and the impacts of location (environment) and pedigree on variability of these components. Results demonstrated that (i) some biochemical analytes showed trends (either increased or decreased) with launch year and/or yield, (ii) some analytes varied according to variety but showed no trend with launch year and/or yield, and (iii) almost all analytes showed extensive variation within and across sites. In summary, varietal development of high-yielding soybean, as represented in this study, has been accompanied by compositional changes but these are typically modest relative to environmental factors.

A Generalized Approach to the Modeling and Analysis of 3D Surface Morphology in Organisms

The surface geometry of an organism represents the boundary of its three-dimensional (3D) form and can be used as a proxy for the phenotype. A mathematical approach is presented that describes surface morphology using parametric 3D equations with variables expressed as x, y, z in terms of parameters u, v. Partial differentiation of variables with respect to parameters yields elements of the Jacobian representing tangent lines and planes of every point on the surface. Jacobian elements provide a compact size-free summary of the entire surface, and can be used as variables in principal components analysis to produce a morphospace. Mollusk and echinoid models are generated to demonstrate that whole organisms can be represented in a common morphospace, regardless of differences in size, geometry, and taxonomic affinity. Models can be used to simulate theoretical forms, novel morphologies, and patterns of phenotypic variation, and can also be empirically-based by designing them with reference to actual forms using reverse engineering principles. Although this study uses the Jacobian to summarize models, they can also be analyzed with 3D methods such as eigensurface, spherical harmonics, wavelet analysis, and geometric morphometrics. This general approach should prove useful for exploring broad questions regarding morphological evolution and variation.

Principal Variance Component Analysis of Crop Composition Data: A Case Study on Herbicide-Tolerant Cotton

Compositional studies on genetically modified (GM) and non-GM crops have consistently demonstrated that their respective levels of key nutrients and antinutrients are remarkably similar and that other factors such as germplasm and environment contribute more to compositional variability than transgenic breeding. We propose that graphical and statistical approaches that can provide meaningful evaluations of the relative impact of different factors to compositional variability may offer advantages over traditional frequentist testing. A case study on the novel application of principal variance component analysis (PVCA) in a compositional assessment of herbicide-tolerant GM cotton is presented. Results of the traditional analysis of variance approach confirmed the compositional equivalence of the GM and non-GM cotton. The multivariate approach of PVCA provided further information on the impact of location and germplasm on compositional variability relative to GM.

Abstract 237: MicroRNA patterns in Abdominal Aortic Aneurysm Appear to Reflect Adventitial Inflammation

MicroRNAs (miRNA) have been shown to play an important role in vascular biology. In this study we examined the miRNA profile within human abdominal aortic tissue and plasma to identify differential expression patterns between aneurysm cases and controls. Whole miRNome profiles were assessed using array-based assays (Affymetrix miRNA 2.0) in 27 AAA and 14 control aortic biopsies. Results were validated in 89 AAA and 21 controls using real-time PCR (Taqman-based) assays. A subset of case and control aortic samples were also microdissected. Separate intima/medial and adventitial miRNome profiles were compared with matching total wall. In addition, whole miRNome profiles of EDTA plasma from 22 AAA and 20 age and gender matched controls were examined and validated by RT-PCR in 62 AAA and 62 control plasmas. In aortic tissue, 18 miRNA were highly significantly (p<1x10-4, q<1x10-3) differentially expressed in AAA versus controls. Variable principal component analysis indicated that these miRNA were expressed in three distinct clusters. MiRNA within each cluster were subsequently validated by RT-PCR. The strongest validated associations were miR-146a (p<0.0004), mir-150 (p<0.001), miR-223 (p<0.0001), and mir-140-3p (p=0.006). Aortic wall sub-layer analysis indicated that total wall miRNA profiles reflected adventitial related miRNA in AAA and intima/medial related miRNA in controls. In plasma, 12 miRNA were initially identified (including three which were also observed in tissue) however only miR-151-5p (p=0.005) was successfully validated by RT-PCR. The general pattern of AAA associated miRNA expression indicated regulation of genes known to be involved with B-cell and T-cell function. This was also consistent with the concentration of these miRNA within the AAA adventitia, as these cells are key regulators of the vascular (adventitial) inflammation associated with AAA. In conclusion, microRNAs appear to have significant potential as AAA biomarkers. However, further work is required to determine the specificity of such associations to account for potential confounders, including as concurrent arterial disease.