Use Of Principal Component Analysis Research Articles

Parameters based on X-ray images to assess the physical and physiological quality of Leucaena leucocephala seeds

ABSTRACT Non-destructive and high performance analyses are highly desirable and important for assessing the quality of forest seeds. The aim of this study was to relate parameters obtained from semi-automated analysis of radiographs of Leucaena leucocephala seeds to their physiological potential by means of multivariate analysis. To do so, seeds from five lots collected from parent trees from the region of Viçosa, MG, Brazil, were used. The study was carried out through analysis of radiographic images of seeds, from which the percentage of damaged seeds (predation and fungi), and measurements of area, perimeter, circularity, relative density, and integrated density of the seeds were obtained. After the X-ray test, the seeds were tested for germination in order to assess variables related to seed physiological quality. Multivariate statistics were applied to the data generated, with use of principal component analysis (PCA). X-ray testing allowed visualization of details of the internal structure of seeds and differences regarding density of seed tissues. Semi-automated analysis of radiographic images of Leucaena leucocephala seeds provides information on seed physical characteristics and generates parameters related to seed physiological quality in a simple, fast, and inexpensive manner.

Conceptualisation and Measurement of Women's Empowerment Revisited

This paper advances a new approach to conceptualise, define and systematically measure women's empowerment. A review of literature identified that empowerment is multidimensional, and that women empowered in one dimension are not necessarily empowered in the other. It was also established that women need both resources and a sense of agency to independently achieve their livelihood outcomes. Therefore, it was concluded that both agency and resources are the best indicators of women's level of empowerment. In view of the multidimensional nature of women's empowerment, this study proposed the use of principal component analysis (PCA) on women's level of agency and resources to generate factor scores (i.e., indicators of each woman's level of empowerment) at each dimension of empowerment (i.e., indicated by each PC) as the better approach to quantitatively measure women's empowerment. Data to test this approach was taken from rural women in Msinga Local Municipality of South Africa. Application of PCA, showed the dominant dimensions in which women in the study area were empowered while the PC factor loadings quantitatively measured the level of empowerment along each dimension. It was concluded that women's empowerment is best conceptualised as increases in women's capabilities and hence, application of PCA to the indicators of women's indicators of resources and agency is the most suitable approach to capture their empowerment levels across different dimensions.

Water pollution indicators and chemometric expertise for the assessment of the impact of municipal solid waste landfills on groundwater located in their area

The main aim of the paper was to assess the groundwater quality near the Municipal Solid Waste Landfill (MSW) landfill sites using landfill water pollution index (LWPI), Nemerow index (PI) and chemometric expertise. For this purpose, an analysis of eleven physicochemical parameters was performed including: pH, electrolytic conductivity (EC), total suspension (TS), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total organic carbon (TOC), dissolved organic carbon (DOC), total nitrogen (TN), total dissolved nitrogen (DN), total phosphorus (TP) and orthophosphates (OP) in landfill leachates and groundwater samples from two different MSW landfill sites in NE Poland. Research materials were collected in the period from January 2016 to May 2017. More than 80% of the data variance was explained by two factors identified by the use of principal component analysis: 44.8% was explained by the factor contributed by carbon and nitrogen-related parameters, while 35.7% by the factor contributed by phosphorus-related parameters and both, chemical and biochemical oxygen demands. The obtained results indicated negative impact of MSW landfill sites on groundwater quality which was clearly observed for samples from P1 and P4 in landfill H and for samples from P3 in landfill U.

Dietary Acculturation among Puerto Rican Adults Varies by Acculturation Construct and Dietary Measure

BackgroundThe role of acculturation in dietary behaviors among Hispanics/Latinos in the United States remains unclear. Discrepancies may be explained by variations in acculturation constructs or ethnicity-specific dynamics. ObjectiveWe aimed to compare relations between 3 different acculturation constructs with dietary quality and patterns among Puerto Ricans in the mainland United States. MethodsWe analyzed cross-sectional data with 1194–1380 Puerto Ricans, aged 45–75 y. Acculturation was measured with the use of a language-based scale (0–100; higher score denotes more English use), a psychological-based scale (0–50; higher score denotes stronger US orientation), and years living in the mainland United States. Diet quality scores (higher scores denote healthier diet) were defined with the use of the Alternate Healthy Eating Index-2010 (AHEI) and the Mediterranean Diet Score (MeDS). Three dietary patterns were previously derived with the use of principal components analysis. Adjusted multivariable regression models tested the association of each acculturation construct with diet quality score or pattern. Interaction terms were included for income or education status. ResultsPsychological-based acculturation, but not the other constructs, was positively associated with AHEI (β ± SE: 0.013 ± 0.004; P = 0.002) and MeDS (0.009 ± 0.005; P = 0.041). Income, but not education, moderated this association (P = 0.03), with higher diet quality observed with higher income (>$25,000) and stronger US orientation. All constructs were inversely associated with a traditional dietary pattern, with the language-based scale being stronger (z score β ± SE: −0.160 ± 0.032; P < 0.0001) than the psychological-based scale (−0.097 ± 0.028; P = 0.001) or years living in the mainland United States (−0.058 ± 0.028; P = 0.041). No associations were observed for the Western or sweets/desserts patterns. ConclusionsIn Puerto Rican adults, stronger psychological US orientation was associated with higher diet quality, particularly with higher income. More Spanish use, stronger psychological Puerto Rican orientation, and shorter length of mainland-US residency were associated with traditional dietary patterns. Appropriate diet-related acculturation constructs should be carefully considered among Hispanics/Latinos. This trial was registered at clinicaltrials.gov as NCT01231958.

Linking Geostatistical Methods: Co-Kriging – Principal Component Analysis (PCA); with Integrated Well Data and Seismic Cross Sections for Improved Hydrocarbon Prospecting (Case Study: Field X)

In this era of globalization, the demand for energy is rising in tandem with social and economic development throughout the world. Current hydrocarbon demand is much greater than domestic crude oil and natural gas production. In order to bridge the gap between energy supply and demand, it is imperative to accelerate exploration activities and develop new effective and efficient techniques for discovering hydrocarbons. Therefore, this study presents a new method for integrating seismic inversion data and well data using geostatistical principles that allow for the high level of processing and interpretation expected nowadays. The main part of this paper will concern the preparation and processing of the input data, with the aim of constructing a map of hydrocarbon-potency distribution in a certain horizon. It will make use of principal component analysis (PCA) and the co-kriging method. In the case study of Field X, we analyze a single new dataset by applying PCA to every existing well that contains multivariate rock-physics data. The interpretation that can be extracted from the output gives us information about the hydrocarbon presence in a particular depth range. We use that output as our primary dataset from which our research map is constructed by applying the co-kriging method. We also rely on an acoustic impedance dataset that is available for a certain horizon to fulfill the co-kriging interpolation requirement. All of the acoustic impedance data and output data that result from the application of PCA in a particular horizon give strong correlation factors. Our resulting final map is also validated with information from proven hydrocarbon discoveries. It is demonstrated that the map gives accurate information suggesting the location of hydrocarbon potency, which will need some detailed follow-up work to enhance the distribution probabilities. This method can be considered for hydrocarbon prediction in any area of sparse well control.

The use of principal component analysis for the assessment of the spatial variability of curative waters from the Busko-Zdrój and Solec-Zdrój region (Poland) – preliminary results

Abstract In the Busko-Zdrój and Solec-Zdrój region (Poland), curative waters with different concentrations of sulphur(II) compounds are extracted. In this paper, waters from 14 intakes were considered. The waters analysed are chloride-sodium, sulphide, iodide (Cl–Na, S, I) or chloride-sodium, sulphide (Cl–Na, S) and are especially associated with deep circulation systems (Jurassic limestones) or shallow circulation systems mostly connected with Cretaceous formations (Senonian marls and limestones and Cenomanian sands and sandstones). The aim of this research is to evaluate the similarities or differences between samples of curative water taken from different geological formations and locations. Principal component analysis was used to assess the similarities between samples of curative waters collected from the intakes being examined. Two principal components were extracted. The analysis indicated that there is a difference between the chemical composition of waters taken from different stratigraphies. Water samples from Cretaceous formations of Cenomanian and Senonian age are similar. There is a small difference observed for waters abstracted from the Neogene and Cretaceous. However, these differences mostly resulted from SO42− and Ca2+ concentrations.

Heavy metals risk assessment in water and bottom sediments of ICOLLs in Northern Poland

&lt;p&gt;Natural diversity of intermittently closed and open lakes and lagoons (ICOLLs) depends on mutual interactions of several factors: (i) an impact of sea water and land background; (ii) temporary meteorological situation; (iii) hydrological conditions; and (iv) the shape of lake basin. However, some regional, local or even sudden impacts including anthropogenic ones create their final ecological status. To identify heavy metals risk assessment in ICOLLs located in Polish coastline wide range of them were determined in water and bottom sediment samples collected in 10 water reservoirs. Multidimensional data set of 20 variables was explored by the use of chemometrics according to seasonality (Spring, Summer, Autumn), sample type (water, sediment) and level of isolation (fully isolated, partially and fully connected lakes). The results showed that 70.5% and 77% of the data variance can be explained by the use of principal component analysis for waters and sediments, respectively. Waters of fully isolated or partially connected lakes are more abundant with Ir, Nd and Sm, while less abundant with Pr and Sr. Bottom sediments taken from Jamno lake show significant contamination by heavy metals of the highest environmental concern (Al, Cr, Cu, Ni, Ti and Zn).&lt;/p&gt;&#x0D;

Use of Principal Component Analysis for parameter selection for development of a novel Water Quality Index: A case study of river Ganga India

Water Quality Index (WQI) is one of the most widely used concepts for representation of the quality of a water resource. This concept has wide acceptance among policy makers and other stakeholders as this gives a clear and comprehensive picture of the status of the pollution of a water body. The standard step of development of a WQI are – parameter selection, assignment of weights, development of sub-index functions and final aggregation of weighted sub- index values. Out of these, the current study focusses on the first step, i.e. parameter selection. The results of this study shall play a crucial role in the development of Ganga Water Quality Index in the future. For the current study, the initially available data has been subjected to Principal Component Analysis (PCA) and this led to reduction of number of parameters from 28 to 9. This has been done to make the process more feasible and economic as this would drastically reduce the time, effort and cost required to monitor samples for a large number of parameters. The finally shortlisted 9 parameters were- Dissolved Oxygen (DO), pH, Conductivity, Biological Oxygen Demand (BOD), Total Coliform (TC), Chlorides, Magnesium, Sulphate, Total Dissolved Solids (TDS). PCA utilizes the variance in the entire data set and projects it in new dimensions, thereby reducing the number of parameters but retaining maximum variance. The use of statistical techniques in WQI development makes it less biased and more objective in nature and forms the basis of development of a Ganga Water Quality Index (GWQI) in future.

Multivariate analysis techniques applied for the performance measurement of Federal University Hospitals of Brazil

Abstract The presence of teaching hospitals represents a strategic role in improving the quality of life of the Brazil Unified Health System patients, based on the qualified training of health, medical and complementary professionals, which means efficiency in hospital production. Therefore, the purpose of this paper was to apply the multivariate analysis techniques of principal component (PCA) and clusters analysis in the performance measurement of Federal University Hospitals, aiming to contribute for the improving of Brazilian health care services. The research is descriptive, with exploratory purpose, being organized according to three stages of development, that is, the use of principal component analysis; in the second stage, the selection of inputs and outputs for the management of hospital organizations and; in the third, the use of clusters analysis technique, for the inputs and outputs defined. Results show that the groups' formation represented divergences between both techniques applied. Thus, it was verified that two hospitals, of twenty, remained alone in both clusters and PCA, demonstrating that these organizations differentiate from the others, regarding the similarity of their characteristics described by the original variables used in the analysis.

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol.

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Principal Component Analysis as a Tool for Characterizing Black Hole Images and Variability

Abstract We explore the use of principal component analysis (PCA) to characterize high-fidelity simulations and interferometric observations of the millimeter emission that originates near the horizons of accreting black holes. We show mathematically that the Fourier transforms of eigenimages derived from PCA applied to an ensemble of images in the spatial domain are identical to the eigenvectors of PCA applied to the ensemble of the Fourier transforms of the images, which suggests that this approach may be applied to modeling the sparse interferometric Fourier-visibilities produced by an array such as the Event Horizon Telescope. We also show that the simulations in the spatial domain can themselves be compactly represented with a PCA-derived basis of eigenimages, which allows for detailed comparisons to be made between variable observations and time-dependent models, as well as for detection of outliers or rare events within a time series of images. Furthermore, we demonstrate that the spectrum of PCA eigenvalues is a diagnostic of the power spectrum of the structure and, hence, of the underlying physical processes in the simulated and observed images.

Structure-Aware Principal Component Analysis for Single-Cell RNA-seq Data.

With the emergence of droplet-based technologies, it has now become possible to profile transcriptomes of several thousands of cells in a day. Although such a large single-cell cohort may favor the discovery of cellular heterogeneity, it also brings new challenges in the prediction of minority cell types. Identification of any minority cell type holds a special significance in knowledge discovery. In the analysis of single-cell expression data, the use of principal component analysis (PCA) is surprisingly frequent for dimension reduction. The principal directions obtained from PCA are usually dominated by the major cell types in the concerned tissue. Thus, it is very likely that using a traditional PCA may endanger the discovery of minority populations. To this end, we propose locality-sensitive PCA (LSPCA), a scalable variant of PCA equipped with structure-aware data sampling at its core. Structure-aware sampling provides PCA with a neutral spread of the data, thereby reducing the bias in its principal directions arising from the redundant samples in a data set. We benchmarked the performance of the proposed method on ten publicly available single-cell expression data sets including one very large annotated data set. Results have been compared with traditional PCA and PCA with random sampling. Clustering results on the annotated data sets also show that LSPCA can detect the minority populations with a higher accuracy.

The use of principal component analysis for fast and efficient kinetic analysis of combined in situ X-ray diffraction and spectroscopic data

The use of principal component analysis for fast and efficient kinetic analysis of combined <i>in situ</i> X-ray diffraction and spectroscopic data

Systematics of the broad-nosed bats, Platyrrhinus umbratus (Lyon, 1902) and P. nigellus (Gardner and Carter, 1972) (Chiroptera: Phyllostomidae), based on genetic, morphometric, and ecological niche analyses

ABSTRACTPlatyrrhinus is a genus of leaf-nosed frugivorous bats that are endemic to the Neotropics. P. umbratus occurs in the Andean and costal mountain systems of Venezuela and Colombia. P. nigellus occurs along the Andes from Venezuela to Bolivia. Both species are medium-sized members of the genus possessing confusing taxonomic histories that have never intersected. Four of the 21 recognized species of Platyrrhinus, among them P. umbratus, do not have their taxonomic identification confirmed by molecular analyses. We provide the first genetic data (Cyt-b and ND2 sequences) for the species. Phylogenetic analyses including the new genetic data lead to the conclusion that P. umbratus and P. nigellus are conspecific. Through the use of Principal Components Analysis (PCA) and Ecological Niche Modeling (ENM), we confirm that P. umbratus and P. nigellus share high morphometric and environmental similarities. Based on such integrative approach, we regard P. nigellus as a junior synonym of P. umbratus. We provide an emended diagnosis of P. umbratus (subsuming P. nigellus) and draw morphological comparisons with other species of the genus with which it is sympatric. The conservation status of P. umbratus needs to be determined. The high rate of habitat destruction in the tropical Andes may soon cause P. umbratus to be reassigned to the Near-Threatened (NT) category of the International Union for Conservation of Nature (IUCN).

Federal Inland Revenue Service Tax Awareness Index: Development and Validation

Taxation is not just a means for generating revenue for the country, it is also a tool used to regulate the economy using fiscal policy, to control inflation, prices of goods and services and bridge the gap between the rich and the poor. However, the awareness for this all-important component of any goal-oriented government is poor. It is against this backdrop that this study aimed at developing a Tax Awareness Index for use in the Nigerian context. The study adopted descriptive survey approach with a cross-sectional design. The sampling approach to recruit participants for the study was the convenience sampling method. Data analysis involved the use of principal component analysis (PCA) with Varimax rotation. Results showed that the initially proposed 10-factor solution of 94 questionnaire items was not supported with the data gathered. However, a 5-factor solution emerged that made substantive sense for the purpose of developing a Tax. Awareness Index. A composite score on the 5-factor solution indicates the level of tax awareness for a respondent. It was concluded that this index would serve as a viable tool to measure how much the general populace is aware of tax and its components.

Use of principal component analysis to evaluate variability in CASA and flow cytometer sperm characteristics

An intact and functional sperm plasmalemma has a major role in sperm motility and fertilizing capacity. Several techniques have been developed to evaluate the integrity of the sperm plasma membrane, but there are still some inconsistencies concerning the methods that are more closely associated with sperm function. In this study, the aim was to: i) evaluate the integrity of the boar sperm plasmalemma during 72 h of semen storage at 17 °C using four techniques: eosin/nigrosin (E/N), propidium iodide/carboxyfluorescein diacetate (PI/CFDA), hypo-osmotic swelling test (HOST), and combined HOST with eosin staining (HOST/E), ii) assess the correlations and the limits of consistency among these techniques, iii) and estimate the relationships with the acrosomal status and sperm kinetics. Results indicate that the integrity of the sperm plasmalemma decreases during 72 h of storage, although significant differences were found only using the HOST and HOST/E techniques. Moreover, use of E/N and PI/CFDA results in greater values relative to the undamaged sperm membrane than use of HOST and HOST/E at any incubation time. Overall, results using all techniques were consistent and correlate except for findings with PI/CFDA and HOST, which was slightly below 95%. Moreover, values using the techniques for the evaluation of the integrity of the sperm head and tail membranes are positively associated with the acrosomal status and different kinetic variables with the tail integrity being related to rapid linear trajectories and the head integrity to rapid curvilinear trajectories. The results of this study provide new insights into the relevance of evaluating the boar sperm plasmalemma in the routine spermiogram.

Energy-Efficient Mapping of LTE-A PHY Signal Processing Tasks on Microservers

Centralized radio access network (C-RAN) is a network architecture that is emerging as a key technology enabler for 5G mobile networks as capacity demands for mobile traffic continue to proliferate. Essentially, C-RAN involves separating the remote radio heads from baseband units to be processed in the cloud. A systematic design of C-RAN involves mapping of individual baseband signal processing tasks to general purpose cloud infrastructures, such as the microserver to reduce the energy footprint. In this paper, we start with mapping the lowest protocol stack, i.e., the physical layer (PHY), which is characterized by strict latency and dynamic data rate. To achieve this, we explore the use of machine intelligence for energy-efficient mapping of PHY signal processing on microservers. Fundamental to this approach is: 1) the use of principal component analysis to represent workload from multi-dimensional hardware performance statistics, demonstrating 99.88% correlation with the critical PHY processing latency and 2) the use of deep learning to model latency and predict dynamic workload for on-demand resource allocation, resulting in up to 36% reduction in hardware usage. These principles are built into a cross-layer run-time framework, which adapts resource allocation in response to time-varying data rate, guaranteeing latency, and improving energy efficiency by up to 48% (average 28%).

The Creativity Challenge Game: An educational intervention for creativity enhancement with the integration of Information and Communication Technologies (ICTs)

Creativity enhancement is an educational objective. The integration of Information and Communication Technologies (ICTs) into the curriculum is another goal of many educators. In this study a creativity enhancement intervention was conducted with participants students (N = 90, 46 male, 44 female) of mean age M = 18.38, in an information systems course. A quasi-experimental design was employed and the proposed method included the extensive use of Facebook, a collaborative team structure, a game-like competitive environment, questions generation and answering. Creativity was measured with several pre-post divergent thinking tests. Academic achievement was obtained through exam results. Additional data were collected with online questionnaires. Results show that the intervention was overall effective in stimulating creativity. There was a statistically significant increase in fluency, flexibility, elaboration and originality, as measured by divergent thinking tests. Total student creativity calculated with the use of principal component analysis showed a significant positive link to academic achievement and ICT knowledge. Students with almost zero Facebook usage exhibited the highest levels of creativity followed closely by their peers with the highest Facebook usage. Creativity enhancement was not related to Facebook usage or ICT knowledge. Results and implications are discussed, and propositions for future research are offered.

Structural characteristics of food protein-originating di- and tripeptides using principal component analysis

This paper presents the use of principal component analysis (PCA) to study the effect of specific physicochemical attributes on bitterness of di- and tripeptides originating from food proteins. Peptide sequences were derived from the BIOPEP-UWM database of sensory peptides and amino acids. Descriptors defining the physicochemical properties of amino acids forming the analyzed peptides were study variables. They were derived from ProtScale program and Biological Magnetic Resonance Data Bank. Finally, PCA was carried out for 51 dipeptides/12 variables, and 51 tripeptides/18 variables using STATISTICA®13.1 software. PCA allowed reducing the input datasets to 4 principal components (PCs) for dipeptides and to 5 PCs for tripeptides. The impact of the following properties on the bitterness of peptides was observed: relatively high molecular weight, bulkiness, increasing number of carbon and hydrogen atoms of amino acids forming the sequences. These properties characterized the N- (negative correlations) and C-terminal residue (positive correlations) of both di- and tripeptides. An additional property affecting peptide bitterness was amino acids’ hydrophobicity. Our results were consistent with scientific reports on structure–bitterness of peptides. Thus, we find PCA a chemometric approach helpful in broadening the knowledge about the function of peptides resulting from their chemical nature.

Solid-Contact Potentiometric Sensors and Multisensors Based on Polyaniline and Thiacalixarene Receptors for the Analysis of Some Beverages and Alcoholic Drinks.

Electronic tongue is a sensor array that aims to discriminate and analyze complex media like food and beverages on the base of chemometrics approaches for data mining and pattern recognition. In this review, the concept of electronic tongue comprising of solid-contact potentiometric sensors with polyaniline and thacalix[4]arene derivatives is described. The electrochemical reactions of polyaniline as a background of solid-contact sensors and the characteristics of thiacalixarenes and pillararenes as neutral ionophores are briefly considered. The electronic tongue systems described were successfully applied for assessment of fruit juices, green tea, beer, and alcoholic drinks They were classified in accordance with the origination, brands and styles. Variation of the sensor response resulted from the reactions between Fe(III) ions added and sample components, i.e., antioxidants and complexing agents. The use of principal component analysis and discriminant analysis is shown for multisensor signal treatment and visualization. The discrimination conditions can be optimized by variation of the ionophores, Fe(III) concentration, and sample dilution. The results obtained were compared with other electronic tongue systems reported for the same subjects.