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Origin of the electroretinogram in the intact macaque eye--I. Principal component analysis.

Responses to 400 msec flashes of white light were recorded at various depths in the retina of the intact macaque eye. A statistical technique, principal component analysis (PCA), was used to isolate independent components from the LERG, using the changing contribution of a cell's response to the LERG with retinal depth. Two approaches were evaluated: first, PCA was performed on the complete LERG waveforms and, second, on small sections of the LERG. The first method yielded a component strongly resembling the receptor response, apart from some inconsistencies in the proximal layers, and a component in which the b-wave was the most prominent feature, but which still contained some other responses. Hence, the components were not suitable to describe the LERG in terms of responses of specific cell types. The second method uses PCA to determine whether or not the potential change within a small time window is accounted for by a single component. This method, which essentially uses the knowledge that different cell types respond with different delay times, yielded three components. These were identified as the receptor component, the b-wave and the d.c.-component. The voltage-depth profiles of these components were assessed.

Animals↗

A principal component analysis of facial expressions.

Pictures of facial expressions from the Ekman and Friesen set (Ekman, P., Friesen, W. V., (1976). Pictures of facial affect. Palo Alto, California: Consulting Psychologists Press) were submitted to a principal component analysis (PCA) of their pixel intensities. The output of the PCA was submitted to a series of linear discriminant analyses which revealed three principal findings: (1) a PCA-based system can support facial expression recognition, (2) continuous two-dimensional models of emotion (e.g. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161-1178) are reflected in the statistical structure of the Ekman and Friesen facial expressions, and (3) components for coding facial expression information are largely different to components for facial identity information. The implications for models of face processing are discussed.

Adolescent↗

NMR spectral quantitation by principal component analysis. III. A generalized procedure for determination of lineshape variations.

We present a general procedure for automatic quantitation of a series of spectral peaks based on principal component analysis (PCA). PCA has been previously used for spectral quantitation of a single resonant peak of constant shape but variable amplitude. Here we extend this procedure to estimate all of the peak parameters: amplitude, position (frequency), phase and linewidth. The procedure consists of a series of iterative steps in which the estimates of position and phase from one stage of iteration are used to correct the spectra prior to the next stage. The process is convergent to a stable result, typically in less than 5 iterations. If desired, remaining linewidth variations can then be corrected. Correction of (typically) unwanted variations of these types is important not only for direct peak quantitation, but also as a preprocessing step for spectral data prior to application of pattern recognition/classification techniques. The procedure is demonstrated on simulated data and on a set of 992 (31)P NMR in vivo spectra taken from a kinetic study of rat muscle energetics. The proposed procedure is robust, makes very limited assumptions about the lineshape, and performs well with data of low signal-to-noise ratio.

Algorithms↗

Peak purity determination with principal component analysis of high-performance liquid chromatography-diode array detection data.

A method is proposed for the determination of chromatographic peak purity by means of principal component analysis (PCA) of high-performance liquid chromatography with diode array detection (HPLC-DAD) data. The method is exemplified with analysis of binary mixtures of lidocaine and prilocaine with different levels of separation. Lidocaine and prilocaine have very similar spectra and the chromatograms used had substantial peak overlap. The samples analysed contained a constant amount of lidocaine and a minor amount of prilocaine (0.02-2 conc.%) and hence the focus was on determining the purity of the lidocaine peak in the presence of much smaller levels of prilocaine. The peak purity determination was made by examination of relative observation residuals, scores and loadings from the PCA decomposition of DAD data over a chromatographic peak. As a reference method, the functions for peak purity analysis in the chromatographic data system used (Chromeleon) were applied. The PCA method showed good results at the same level as the detection limit of baseline-separated prilocaine, outperforming the methods in Chromeleon by a factor of ten. There is a discussion of the interpretation of the result, with some comparisons with evolving factor analysis (EFA). The main advantage of the PCA method for determination of peak purity over methods like EFA lies in its simplicity, short time of calculation and ease of use.

Chromatography, High Pressure Liquid↗

Forecasting peak daily ozone levels: part 2--A regression with time series errors model having a principal component trigger to forecast 1999 and 2002 ozone levels.

A modified time series approach, a Box-Jenkins regression with time series errors (RTSE) model plus a principal component (PC) trigger, has been developed to forecast peak daily 1-hr ozone (O3) in real time at the University of Wisconsin-Milwaukee North (UWM-N) during 1999 and 2002. The PC trigger acts as a predictor variable in the RTSE model. It tries to answer the question: will the next day be a possible high O3 day? To answer this question, three PC trigger rules were developed: (1) Hi-Low Checklist, (2) Discriminant Function Approach I, and (3) Discriminant Function Approach II. Also, a pure RTSE model without including the PC trigger and a persistence approach were tested for comparison. The RTSE model with DFA I successfully forecast the only two 1-hr federal exceedances (124 ppb), one in 1999 and one in 2002. In terms of the O3 100-ppb exceedances, 60-80% of the incorrect forecasts occurred with incorrect PC decisions. A few others may have been caused by unexpected O3-weather relations. When the three approaches used UWM-N data to forecast a 100-ppb exceedance somewhere in the Milwaukee, WI, metropolitan area, their performance was dramatically improved: the false alarm rate was reduced from 0.89 to 0.44, and the probability of detection was increased from 0.71 to 0.88.

Forecasting↗

Beyond principal component analysis: canonical component analysis for data reduction in classification of EPs.

The authors tested a new procedure for the discrimination of EPs obtained in different stimulus situations. In contrast with principal component analysis (PCA) used so far for the purpose of data compression, the method referred to as canonical component analysis (CCA) is optimal for the purpose of discrimination. To illustrate this, the authors performed both PCA and CCA for the same material, then after carrying out discriminant analysis ( SWDA ) for the data transformed in this way, compared the performance of the two procedures in discrimination. In view of both the theoretical and practical considerations, the authors recommend that in the future researchers use CCA instead of PCA in EP studies for data reduction carried out for discrimination.

Computers↗

Automatic frequency alignment and quantitation of single resonances in multiple magnetic resonance spectra via complex principal component analysis.

Several algorithms for automatic frequency alignment and quantitation of single resonances in multiple magnetic resonance (MR) spectra are investigated. First, a careful comparison between the complex principal component analysis (PCA) and the Hankel total least squares-based methods for quantifying the resonances in the spectral sets of magnetic resonance spectroscopy imaging (MRSI) spectra is presented. Afterward, we discuss a method based on complex PCA plus linear regression and a method based on cross correlation of the magnitude spectra for correcting frequency shifts of resonances in sets of MR spectra. Their advantages and limitations are demonstrated on simulated MR data sets as well as on an in vivo MRSI data set of the human brain.

Brain↗

[Determination of three principal components in chuanjing tablets by using high performance liquid chromatography].

Uniform design method was employed to optimize the mobile phase of HPLC in order to determine simultaneously three principal components, theophylline, amobarbital and methylephedrine hydrochloride, in Chuanjing tablet, a compound preparation for asthma and cough. The stationary phase was ODS and the optimal mobile phase composition was V (0.015 mol/L phosphate buffer containing 0.3% triethylamine, pH 4.9): V (methanol) = 35:65. The detection was performed at 215 nm and the sensitivity was time programmed for simultaneous determination of minor and major components. Caffeine was selected as the internal standard. A baseline separation was achieved within 10 min. The linear ranges for theophylline, amobarbital and methylephedrine hydrochloride were 0.03 g/L-0.20 g/L, 7.5 mg/L-50.0 mg/L and 7.5 mg/L-50.0 mg/L, recoveries 99.7%-102.6%, 98.5%-100.2% and 98.0%-102.7%, inter-day RSDs 0.23%-1.2%, 0.35%-2.5%, 0.33%-1.6% respectively. This HPLC method is rapid and accurate, and suitable for the quality control of the preparation.

Amobarbital↗

Classification of environmental estrogens by physicochemical properties using principal component analysis and hierarchical cluster analysis.

A structurally diverse assortment of 60 environmental estrogens was divided into two main clusters ("A", "B") and a pair of subclusters ("C1", "C2") by applying principal component analysis to selected 1D and 2D molecular descriptors and subjecting the PCs to hierarchical cluster analysis. Although clustering was predicated solely on physicochemical properties, the dependence on particular physicochemical parameters of xenoestrogen binding affinities (pK(i)) to murine uterine cytosolic estrogen receptor (ER) proved greater for compounds within (sub)clusters than for compounds between (sub)clusters. Quantitative structure-binding affinity relationships derived using molecular descriptors and PCs suggested differences in the driving forces for xenoestrogen-ER binding for different (sub)clusters. The modeling power for xenoestrogen-ER binding affinities of a combination of TLSER and WHIM 3D indices was much greater than that of combinations of 1D and 2D molecular descriptors or the PCs derived therefrom. The clusterings obtained using PCs also proved applicable to the 3D-QSARs.

Chemical Phenomena↗

Addressing misallocation of variance in principal components analysis of event-related potentials.

Interpretation of evoked response potentials is complicated by the extensive superposition of multiple electrical events. The most common approach to disentangling these features is principal components analysis (PCA). Critics have demonstrated a number of caveats that complicate interpretation, notably misallocation of variance and latency jitter. This paper describes some further caveats to PCA as well as using simulations to evaluate three potential methods for addressing them: parallel analysis, oblique rotations, and spatial PCA. An improved simulation model is introduced for examining these issues. It is concluded that PCA is an essential statistical tool for event-related potential analysis, but only if applied appropriately.

Analysis of Variance↗

A model of head-related transfer functions based on principal components analysis and minimum-phase reconstruction.

Free-field to eardrum transfer functions (HRTFs) were measured from both ears of 10 subjects with sound sources at 265 different positions. A principal components analysis of the resulting 5300 HRTF magnitude functions revealed that the HRTFs can be modeled as a linear combination of five basic spectral shapes (basis functions), and that this representation accounts for approximately 90% of the variance in the original HRTF magnitude functions. HRTF phase was modeled by assuming that HRTFs are minimum-phase functions and that interaural phase differences can be approximated by a simple time delay. Subjects' judgments of the apparent directions of headphone-presented sounds that had been synthesized from the modeled HRTFs were nearly identical to their judgments of sounds synthesized from measured HRTFs. With fewer than five basis functions used in the model, a less faithful reconstruction of the HRTF was produced, and the frequency of large localization errors increased dramatically.

Attention↗

Effects of orally administered capsaicin, the principal component of capsicum fruits, on the in vitro metabolism of the tobacco-specific nitrosamine NNK in hamster lung and liver microsomes.

Capsaicin (8-methyl-N-vanillyl-6-nonenamide) is the principal component in Capsicum fruits consumed worldwide as a food additive. Capsaicin is known for its hot, pungent qualities. The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is viewed as an important etiological factor in the causation of human lung cancer. In our study, a single oral dose of capsaicin administered by oral gavage at 2 mg/kg and 10 mg/kg body weight to male Syrian golden hamsters altered the in vitro metabolism of NNK by liver and lung microsomes. The most significant effect was on the inhibition of alpha-carbon hydroxylation. The orally administered capsaicin inhibited the formation of keto aldehyde (methylating pathway) and the formation of keto alcohol (pyridyloxobutylating pathway) in lung microsomes except for microsomes from animals receiving 10 mg/kg capsaicin 24 h post-treatment. In contrast, capsaicin inhibited only the methylating pathway in liver microsomal metabolism of NNK. This effect persisted at 24 h post-treatment. Since it is reported that the pyridyloxobutylating pathway enhances the effects of the more damaging methylating pathway in the metabolism of NNK (reference 25), our results suggest that any potentially chemopreventive action of orally administered capsaicin may be greater toward NNK-induced lung tumorigenesis than toward NNK-induced liver tumorigenesis.

Administration, Oral↗

Discrimination of intact and injured Listeria monocytogenes by Fourier transform infrared spectroscopy and principal component analysis.

Fourier transform infrared spectroscopy (FT-IR, 4000-600 cm(-)(1)) was used to discriminate between intact and sonication-injured Listeria monocytogenes ATCC 19114 and to distinguish this strain from other selected Listeria strains (L. innocua ATCC 51742, L. innocua ATCC 33090, and L. monocytogenes ATCC 7644). FT-IR vibrational overtone and combination bands from mid-IR active components of intact and injured bacterial cells produced distinctive "fingerprints" at wavenumbers between 1500 and 800 cm(-)(1). Spectral data were analyzed by principal component analysis. Clear segregations of different intact and injured strains of Listeria were observed, suggesting that FT-IR can detect biochemical differences between intact and injured bacterial cells. This technique may provide a tool for the rapid assessment of cell viability and thereby the control of foodborne pathogens.

Food Contamination↗

Diagnostic subgrouping of depressed patients by principal component analysis and visualized pattern recognition.

A data-analytical method is described for identifying behavioral and biological variables in psychiatric patients with predictive value in defining clinical subgroups. The procedure, based on principal component analysis (PCA) and graphical analysis, was applied in a group of 28 depressed patients. The 28 depressed patients of unipolar type were observed for up to 15 years for re-evaluation of the diagnoses at the start of the study. Platelet monoamine oxidase activity, post-dexamethasone serum cortisol and serum melatonin predicted two main clinical subgroups as well as a smaller subgroup of bipolar patients. The selection procedure revealed which of several variables were predictive of subgroups that were not possible to identify by univariate methods. The three biological variables may thus be useful in further assessment of clinical subgroups of unipolar depressed patients studied by other research groups.

Adult↗

Commentary and opinion: I. Principal component analysis, variance partitioning, and "functional connectivity".

We briefly review the need for careful study of "variance partitioning" and "optimal model selection" in functional positron emission tomography (PET) data analysis, emphasizing the use of principal component analysis (PCA) and the importance of data analytic techniques that allow for heterogeneous spatial covariance structures. Using an [15O]water dataset, we demonstrate that--even after data processing--the intrasubject signal component of primary interest in baseline activation studies constitutes a very small fraction of the intersubject variance. This small intrasubject variance component is subtly but significantly changed by using analysis of covariance instead of scaled subprofile model processing before applying PCA. Finally, we argue that the concept of "functional connectivity" should be interpreted very generally until the relative roles of inter- and intrasubject variability in both disease and normal PET datasets are better understood.

Analysis of Variance↗

[Principal component analysis for microalbuminuria in patients with noninsulin-dependent, maturity-onset diabetes mellitus].

To determine causal mechanism(s) of microalbuminuria seen in patients with noninsulin-dependent diabetes mellitus (NIDDM), multivariate analysis (principal component analysis) was applied, using patient's age, disease length, fasting blood sugar level (FBS), hemoglobin A1c (HbA1c %), and presence of hypertension as variables. Albumin concentration in the first morning urine was determined by the Latex Photometric Immunoassay (LPIA), and was expressed as albumin index (AI, albumin excretion per gram creatinine). Sixty five cases who had been continuously negative or equivocal (+/-) for urinary protein by an usual paper test method were analysed. The result indicated these patients could be separated into following three groups. Group A (12 cases) showed the highest AI value, was characterized by longer disease length (greater than 10 yrs), and was thought to be in transitional phase into clinical proteinuric stage. Group B (7 cases) was characterized by poor diabetic control and normalization of the microalbuminuria might be possible by strict control measures. In Group C (14 cases), patients were in relatively early stage of the disease, and were under good diabetic control, but presence of hypertension was thought to be a provocative factor.

Adult↗

Principal component analysis of event-related potentials: misallocation of variance revisited.

Misallocating variance, in event-related potential analysis, refers to attributing an experimental effect to components not actually affected. A vector interpretation of the relationship between mathematically derived and true underlying components shows that misallocation depends exclusively on incorrect identification of the affected component. Simulations, using seven imperfect rotations, confirmed all predictions from the vector interpretation concerning the presence, direction, and size of misallocated variance. Contrary to principal component analysis (PCA). Möcks's topographic component model (TCM) is not subject to rotation problems. These two methods were compared over 100 simulations in which the components had constant waveforms and topographics across participants. The group effect was always detected, but only PCA and not TCM showed significance on other components, except when their random weights happened to differ between groups.

Analysis of Variance↗

Principal component analysis of the features of concentric needle EMG motor unit action potentials.

Motor unit action potentials (MUAPs) were recorded from the biceps muscle of normal subjects and of patients with nerve or muscle diseases. Principal component analysis of the MUAP amplitude, area, area/amplitude ratio, duration, and the number of turns and phases produced three components that among them contained 90% of the variance of the data set. Thus the dimensionality of data was reduced from six to three. The first component reflected changes in the size of the MU, whereas the second reflected variations in the arrival time at the recording electrode of the action potentials of muscle fibers in the motor unit. The third factor reflected local loss of muscle fibers within the MU territory. Patterns of variations in the three components were different in patients with neuropathy and myopathy.

Action Potentials↗