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PubMed1990

[Difference of iron stores represented in bimodal distribution of erythrocyte values among a healthy male group].

The distributions of erythrocyte values (red cell count, hemoglobin concentration, etc.) of healthy male workers working in a certain factory were studied in order to evaluate the usefulness of the variables as health indicators. In addition to the estimation of erythrocyte values, anamneses concerning chronic bleeding, smoking habit and alcohol consumption were questioned and serum Fe, Cu, TIBC, ferritin, plasma erythropoietin, and serum enzymes (GOT, GPT, gamma-GTP) were measured. Two-dimensional frequency distributions with axes for the red cell count and hemoglobin concentration, and frequency distributions of the score calculated from principal component analysis, showed bimodal patterns. Using nonlinear curve fitting methods, the distributions of principal component scores were fitted to a mixture of two different Gaussian distributions. The workers were then divided into two groups corresponding to the Gaussian distribution he belongs. Then the frequencies of the items and mean values of the variates were compared between them. There were no differences in the incidences of diseases that caused iron deficiencies, but the mean serum ferritin level was significantly lower in one group than in the other, in other words the workers belonging to the group with low serum ferritin level had smaller iron stores than the other. The mean serum erythropoietin level and the mean serum copper level were higher in that group than in the other. As a result, this analysis gives a new evaluation of the health status of a man who belongs to the so-called healthy male group.

Adult↗
PubMed2001

Monochromatic aberrations of the human eye in a large population.

From both a fundamental and a clinical point of view, it is necessary to know the distribution of the eye's aberrations in the normal population and to be able to describe them as efficiently as possible. We used a modified Hartmann-Shack wave-front sensor to measure the monochromatic wave aberration of both eyes for 109 normal human subjects across a 5.7-mm pupil. We analyzed the distribution of the eye's aberrations in the population and found that most Zernike modes are relatively uncorrelated with each other across the population. A principal components analysis was applied to our wave-aberration measurements with the resulting principal components providing only a slightly more compact description of the population data than Zernike modes. This indicates that Zernike modes are efficient basis functions for describing the eye's wave aberration. Even though there appears to be a random variation in the eye's aberrations from subject to subject, many aberrations in the left eye were found to be significantly correlated with their counterparts in the right eye.

Adult↗
PubMed1993

Morphometric profiles of the mandible of SMXA recombinant inbred strains of mice and strain identification on the basis of mandible measurements.

SMXA recombinant inbred (RI) strains were derived from the F2 generation of a cross between two progenitor inbred strains, A/J and SM/J, which differ considerably with respect to many characters. In order to determine the morphometric profiles of the mandible of SMXA RI strains and to identify these strains, principal component and discriminant analyses (mandible analysis) were carried out using 11 mandible measurements. Principal component analysis revealed that the mandibles of each strain have their own characteristic size and shape. The RI strains, however, were roughly divided into 15 groups based on the morphometric profile of their mandibles. Discriminant analysis showed the probability of erroneous discrimination to be 6.49% (20/308) for the males and 8.06% (27/335) for the females. In both sexes, most of the erroneous discrimination (13 in the male, 17 in the female) occurred between RI strains having comparatively similar morphometric profiles. This suggested that all of the SMXA RI strains examined could be regarded as genetically homogeneous within each strain.

Animals↗
PubMed1998

Development of a scale to measure adherence to self-monitoring of blood glucose with latent variable measurement.

OBJECTIVE: Adherence to self-monitoring of blood glucose (SMBG) is problematic for many people with diabetes. Self-reports of adherence have been found to be unreliable, and existing paper-and-pencil measures have limitations. This study developed a brief measure of SMBG adherence with good psychometric properties and a useful factor structure that can be used in research and in practice. RESEARCH DESIGN AND METHODS: A total of 216 adults with diabetes responded to 30 items rated on a 9-point Likert scale that asked about blood monitoring habits. In part I of the study, items were evaluated and retained based on their psychometric properties. The sample was divided into exploratory and confirmatory halves. Using the exploratory half, items with acceptable psychometric properties were subjected to a principal components analysis. In part II of the study, structural equation modeling was used to confirm the component solution with the entire sample. Structural modeling was also used to test the relationship between these components. It was hypothesized that the scale would produce four correlated factors. RESULTS: Principal components analysis suggested a two-component solution, and confirmatory factor analysis confirmed this solution. The first factor measures the degree to which patients rely on others to help them test and thus was named "social influence." The second component measures the degree to which patients use physical symptoms of blood glucose levels to help them test and thus was named "physical influence." Results of the structural model show that the components are correlated and make up the higher-order latent variable adherence. CONCLUSIONS: The resulting 15-item scale provides a short, reliable way to assess patient adherence to SMBG. Despite the existence of several aspects of adherence, this study indicates that the construct consists of only two components. This scale is an improvement on previous measures of adherence because of its good psychometric properties, its interpretable factor structure, and its rigorous empirical development.

Adult↗
PubMed1992

Correlated response in growth and body measurements accompanying selection for milk yield in Jerseys.

Growth and body measurements from a long-term selection project were analyzed to determine correlated responses to single-trait selection for milk yield. Data were from 1056 daughters (765 selection, 291 control) of 37 bulls (17 selection, 20 control) of 37 bulls (17 selection, 20 control) and included BW and measures of heart girth, chest depth, wither height, and length from withers to pins and from withers to hooks taken at 6 mo, 15 mo, first calving, end of first lactation, and maturity. Other data were birth weight, change in measurements and weights from first calving to end of first lactation, monthly rate of gain from 1 to 13 mo of age, and age reaching breeding weight (250 kg). Principal component scores were calculated from standardized measurements at each age. The first three principal components has meaning (size, length vs. girth, and height vs. girth). All analyses used linear mixed models with fixed effects of genetic group, generation within group, year-season of birth or calving, parity of dam, and birth status (multiple or single birth). Sires were assumed to be random and nested within genetic group. Mean squares for sires was used to test for group differences. Generation did not differ in any analysis and was removed from all models. Selection cows were heavier, larger in some measurements, and had greater overall size at 6 mo of age. Selection cows had greater monthly rate of gain and attained breeding weight at an earlier age. Genetic groups did not differ for any other measurement or weight. Control cows gained more weight and increased more in some measurements between first calving and end of first lactation. Selection for milk yield did not result in an undesirable correlated response in an growth or body measurement.

Animals↗
PubMed2005

[Spatial variation of microbial properties in a creosote-contaminated soil].

By the methods of geostatistics, this paper studied the spatial variation of microbial biomass, microbial community structure and microbial activity in a creosote-contaminated soil. The microbial biomass was indicated by the total amount of 26 examined phospholipid fatty acids (totPLFA), the microbial community structure was characterized by the first two principal components (PC1 and PC2) of the PLFA patterns through subjecting the PLFAs to principal component analysis, and the total amount of CO2-C respired (C(re)) during incubation was used to describe the soil microbial activity. The results showed that most of the variables exhibited various degrees of spatial autocorrelation, and spherical models with nugget could fit their semivariograms. The estimated spatial dependence distances of the variables varied from 2.50 to 16.60 m. The PLFA PC1, totPLFA and C(re) displayed relatively high spatial dependences, their relative structural variance, i.e., the proportion of total variance that can be attributed to the spatial autocorrelation, being 82.3%, 79.6% and 64.7%, respectively, while PLFA PC2 was not spatially autocorrelated. The kriged maps showed that there were several hot spots with relatively high microbial biomass and high microbial activity located in the study site. Gram- negative bacteria characterized by PLFAs 16:1omega7t, cyl7:0, 18:1omega7 and cyl9:0 were the dominant community in these hot spots. The concentration and spatial distribution of polycyclic aromatic hydrocarbons as the main contaminants in the soil could be one of the important factors affecting the spatial variation of soil microbial properties.

Biodegradation, Environmental↗
PubMed1985

Third trochanter incidence and metric trait covariation in the human femur.

The relationship between third trochanter incidence and femoral metric trait covariation has been investigated in a group of 60 left human femora. The experimental sample was constructed so that one group consisted of 15 male and 15 female femora which displayed a third trochanter and a second group consisted of an equal number of sexed long bones which lacked this trait. A battery of ten femoral measurements was sorted according to main effects and interactions and respective covariance matrices were tested for equivalence. Covariance matrices of sorted variables determined not to be significantly different were initially subjected to both ANOVA and MANOVA and subsequently to a principal component analysis. Covariance matrices determined to be significantly different were subjected to a principal component analysis separately. Results of this study indicate that third trochanter incidence is associated with short femora displaying robust proximal diaphyses. The gluteus maximus muscle may act as a primary factor governing third trochanter expression. Further, this infracranial discrete trait appears well suited for human taxonomy studies.

Analysis of Variance↗
PubMed2006

Analysis of human serum by liquid chromatography-mass spectrometry: improved sample preparation and data analysis.

Discovery of biomarkers is a fast developing field in proteomics research. Liquid chromatography coupled on line to mass spectrometry (LC-MS) has become a powerful method for the sensitive detection, quantification and identification of proteins and peptides in biological fluids like serum. However, the presence of highly abundant proteins often masks those of lower abundance and thus generally prevents their detection and identification in proteomics studies. To perform future comparative analyses of samples from a serum bank of cervical cancer patients in a longitudinal and cross-sectional manner, methodology based on the depletion of high-abundance proteins followed by tryptic digestion and LC-MS has been developed. Two sample preparation methods were tested in terms of their efficiency to deplete high-abundance serum proteins and how they affect the repeatability of the LC-MS data sets. The first method comprised depletion of human serum albumin (HSA) on a dye ligand chromatographic and immunoglobulin G (IgG) on an immobilized Protein A support followed by tryptic digestion, fractionation by cation-exchange chromatography, trapping on a C18 column and reversed-phase LC-MS. The second method included depletion of the six most abundant serum proteins based on multiple immunoaffinity chromatography followed by tryptic digestion, trapping on a C18 column and reversed-phase LC-MS. Repeatability of the overall procedures was evaluated in terms of retention time and peak area for a selected number of endogenous peptides showing that the second method, besides being less time consuming, gave more repeatable results (retention time: <0.1% RSD; peak area: <30% RSD). Application of an LC-MS component detection algorithm followed by principal component analysis (PCA) enabled discrimination of serum samples that were spiked with horse heart cytochrome C from non-spiked serum and the detection of a concentration trend, which correlated to the amount of spiked horse heart cytochrome C to a level of 5 pmol cytochrome C in 2 microl original serum.

Animals↗
PubMed2005

Quantitative measures of aggressive periodontitis show substantial heritability and consistency with traditional diagnoses.

BACKGROUND: Aggressive periodontitis (AgP) research nearly always classifies subjects into traditional discrete categories of localized or generalized, based upon degree of attachment loss (AL) and types of affected teeth. Since AL is continuous and quantitative, however, useful information is lost. We developed quantitative measures of AgP, compared these to traditional methods, and estimated heritabilities in families. METHODS: We examined 237 healthy, 169 localized AgP, and 204 generalized AgP subjects. We used the site of maximum AL of each tooth to calculate means for each subject for different groups of teeth. We also applied principal components analysis (PCA) to condense variation among 28 teeth into three orthogonal (uncorrelated) variables. We used discriminant function analysis (DFA) to evaluate how well the quantitative measures match with traditional classifications. Quantitative trait heritabilities were estimated by variance components. RESULTS: PCA clustered first molars, incisors, and the other teeth into three groups. DFA showed that quantitative measures classified subjects consistent with traditional methods (87% to 94% agreement). Heritabilities ranged from 13.7% (P = 0.10) to 30.0% (P = 0.008) for quantitative measures, with highest values obtained for first molars. A combination of the principal component variables most heavily weighted on first molars and incisors gave the best model of disease susceptibility, with good separation of healthy versus diseased subjects, independent of disease extent or severity. CONCLUSIONS: Quantitative measures may provide improved precision and power for many kinds of periodontal research. Our finding of significant heritability supports their use in gene mapping studies of AgP susceptibility.

Acute Disease↗
PubMed2006

Long-term heart rate variability as a predictor of patient age.

Patients age has been estimated in healthy population by means of the heart rate variability (HRV) parameters to assess the potentiality of HRV indexes as a biomarker of age. A long-term analysis of HRV has been performed, computing linear time and frequency domain parameters as well as non-linear metrics, in a dataset of 113 healthy subjects (age range 20-85 years old). The principal component analysis has been used to capture age-related influence on HRV and then three different models have been applied to predict subjects age: a robust linear regressor (RLR), a feedforward neural network (FFNN) and a radial basis function neural network (RBFNN). A good prediction of patient age has been obtained (using all principal components, the Pearson correlation coefficient between predicted and real age: RLR=0.793; FFNN=0.872; RBFNN=0.829), even if an overestimation in younger subjects and an underestimation in older ones may be observed. The important and complementary contribution of non-linear indexes to aging related HRV modifications has also been underlined.

Adult↗
PubMed2026

Multi-level aggregation analysis of microbiome composition and host gene expression reveals associations with systemic and local immunity.

The human gut microbiome plays a critical role in immune regulation, yet the molecular links between microbiome composition and host gene expression remain incompletely understood. We analyzed associations between host gene expression and microbiome composition in a cohort of 315 healthy individuals, integrating microarray-based gene expression data from three intestinal sites (ileum, transverse colon, and rectum) and six immune cell types with microbiome sequencing data. Using a hierarchical feature aggregation strategy combining principal component analysis, clustering, and covariate correction, we discovered significant associations primarily related to immunity. While microbial profiles were similar across the three intestinal sites, the transverse colon yielded the most "microbiome-host gene expression" associations. Among the immune cell types, CD8+ cells showed the highest number of associations. The first principal component of microbiome composition, reflecting a gradient from commensals (e.g., Ruminococcaceae and Christensenellaceae) to proinflammatory taxa ([Ruminococcus] gnavus and Lachnoclostridium), correlated with the expression of TNF-&#x3b1;-linked genes (HMOX1, CPI17, HSD3B2, and SLC5A1). Among individual genera, Catenibacterium abundance was associated with gene expression in both intestinal and immune cells, including negative associations with MRPS21 (related to mitochondrial function) in the transverse colon and with CD8+ gene programs related to T cell differentiation. These findings align with emerging evidence implicating mitochondrial dysfunction in intestinal inflammation. Our results identify multi-level associations between the gut microbiome and host gene expression, suggesting potential mechanisms by which microbiota shape local and systemic immunity and vice versa. The implicated genes and taxa represent candidates for experimental validation to improve understanding of host-microbiome homeostasis and its disruption in disease.IMPORTANCEThe gut microbiome and immune system are engaged in a complex interplay throughout human life. While most associative studies focus on case-control comparisons-typically examining patients with conditions such as inflammatory bowel disease or metabolic diseases-less is known about the molecular links between the microbiome and immune system in healthy individuals. In this study of a large cohort of healthy individuals, we addressed this gap by applying multiscale modeling to tackle the high dimensionality of host-microbiome data. We identified multi-level associations between microbiome composition and host gene expression in both intestinal tissues and immune cells. These findings offer a valuable reference for understanding baseline host-microbiome communication and highlight molecular candidates-such as TNF-&#x3b1;-related genes and mitochondrial pathways-for future experimental validation.

Humans↗
PubMed2005

Movement skill assessment in children with profound multiple disabilities: a psychometric analysis of the top down motor milestone test.

OBJECTIVE: To analyse the psychometric properties of the Top Down Motor Milestone Test (TDMMT), an internationally used instrument in the planning and evaluation of movement-oriented interventions. SETTING: Centres for special education in the Netherlands. SUBJECTS: Children with profound multiple disabilities. METHODS: Validity, reliability and utility of the TDMMT were evaluated. Validity was assessed by conducting a principal component analysis. Reliability was determined by evaluating the internal consistency, test-retest and inter-rater reliability. With regard to utility, the hierarchical item ordering was evaluated with a Mokken scale analysis. Inter-item and item-rest correlations were calculated to confirm the allocation of the items in the TDMMT. RESULTS: Principal component analysis did not support the presence of three underlying factors. The internal consistency was high and test-retest and inter-rater reliability varied from moderate to perfect. Scale analysis showed that the subscales of the TDMMT are strong one-dimensional scales with good internal consistency, however, the order of the items could only partly be confirmed as well as the allocation of the items into four levels of functioning. CONCLUSION: The reliability of the TDMMT is good, but validity is moderate. Results indicate that adaptations to the structure of the TDMMT should be made when used for children with profound multiple disabilities. Further psychometric evaluation of the TDMMT is necessary.

Adolescent↗
PubMed2004

Biologically valid linear factor models of gene expression.

MOTIVATION: The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal component analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduced-dimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool. RESULTS: We address this issue by employing the probabilistic interpretation of PCA and proposing alternative linear factor models which are based on refined biological assumptions. A practical study on two well-understood microarray datasets highlights the weakness of PCA and the greater biological interpretability of the linear models we have developed.

Algorithms↗
PubMed2005

A multivariate statistical approach to spatial representation of groundwater contamination using hydrochemistry and microbial community profiles.

Managers of landfill sites are faced with enormous challenges when attempting to detect and delineate leachate plumes with a limited number of monitoring wells, assess spatial and temporal trends for hundreds of contaminants, and design long-term monitoring (LTM) strategies. Subsurface microbial ecology is a unique source of data that has been historically underutilized in LTM groundwater designs. This paper provides a methodology for utilizing qualitative and quantitative information (specifically, multiple water quality measurements and genome-based data) from a landfill leachate contaminated aquifer in Banisveld, The Netherlands, to improve the estimation of parameters of concern. We used a principal component analysis (PCA) to reduce nonindependent hydrochemistry data, Bacteria and Archaea community profiles from 16S rDNA denaturing gradient gel electrophoresis (DGGE), into six statistically independent variables, representing the majority of the original dataset variances. The PCA scores grouped samples based on the degree or class of contamination and were similar over considerable horizontal distances. Incorporation of the principal component scores with traditional subsurface information using cokriging improved the understanding of the contaminated area by reducing error variances and increasing detection efficiency. Combining these multiple types of data (e.g., genome-based information, hydrochemistry, borings) may be extremely useful at landfill or other LTM sites for designing cost-effective strategies to detect and monitor contaminants.

Archaea↗
PubMed2005

Research note: A socioeconomic index for the INCAP longitudinal study 1969-77.

In this note, we calculate and describe proxy measures that account for variation in standard of living across subjects in the Institute of Nutrition of Central America and Panama (INCAP) Longitudinal Study (1969-77), at the time of the original intervention. Using principal components analysis, we construct two linear indices from an array of household consumer durable goods and housing characteristics, measured at the nuclear family level in the 1975 cross-sectional census. The two indices perform well on three dimensions. First, they are internally coherent in that average ownership and quality of housing characteristics increase with the principal component score. Second, they are robust in that the different approaches yield similar results, for example, in classifying nuclear families into tertiles. And third, they are consistent in that they yield results similar to scores constructed by previous researchers. The indices can be used as background controls in analyses of the INCAP Longitudinal Study (1969-77) data and subsequent follow-up studies, including the Human Capital Study 2002-04. Several articles presented in this supplement to the Food and Nutrition Bulletin used the 1975 index.

Adult↗
PubMed2006

[Study on the identification of tea using near infrared reflectance spectroscopy].

A rapid tea identification method by near infrared spectroscopy coupled with pattern recognition based on principal components analysis and Mahalanobis' distance technique was proposed. Four famous brand teas in China were studied, including Longjing tea, Biluochun tea, Maofeng tea and Tieguanyin tea in the experiment. In the spectral region between 6 500 and 5 300 cm(-1), through preprocessing method of MSC (multiplicative scatter comection), the prediction model was built. The result showed that the model was the best with 8 principal component factors. The rates of identification in calibration set samples and prediction set samples were 98.75% and 95%, respectively. A new idea about quick and precise identification of tea was offered.

Algorithms↗
PubMed2005

An approach to develop two-dimensional fingerprint for the quality control of Qingkailing injection by high-performance liquid chromatography with diode array detection.

An approach was proposed to develop two-dimensional fingerprint (2D fingerprint) by means of principal component analysis (PCA) of high-performance liquid chromatography with diode array detection (HPLC/DAD) data. The approach was applied to establish 2D fingerprints of various Qingkailing injections which were produced by different manufacturers and procedures. In comparison with common one-dimensional fingerprint (ID fingerprint) at fixed wavelength, 2D fingerprint compiled additional spectral data and was hence more informative. Principal component analysis of the 2D fingerprint data was performed in this study, and it led to an accurate classification of various samples on their manufacturers and procedures. The quality of Qingkailing samples was further evaluated by similarity measures and the same results were achieved. For comparison, four conventional ID fingerprints were also applied to the quality assessment for the same samples. Finally, we demonstrated that 2D fingerprint was a more powerful tool to characterize the quality of samples, and could be used to comprehensively conduct the quality control of traditional Chinese medicines.

Chromatography, High Pressure Liquid↗
PubMed2006

A three-step statistical procedure to identify sperm kinematic subpopulations in canine ejaculates: changes after cryopreservation.

Strong evidences suggest that many different sperm subpopulations co-exist within the mammalian ejaculate. These subpopulations have been identified in a number of species; however, to the best of our knowledge, no data exist regarding the existence of sperm subpopulations within the canine ejaculate. Ejaculates were obtained by masturbation from four mongrels and processed using a standard freezing protocol. Motility data were analysed before and after cryopreservation using a computer assisted sperm analysis (CASA) system ISAS. On raw data, a principal component analysis (PCA) was performed to reduce the number of motility descriptors to a few informative variables, and then a K-means cluster procedure was performed and then a regression analysis to validate the clusters obtained in the second analysis. ANOVAs and chi-squared analyses were used to compare clusters and males. PCA revealed that two principal components represented more that the 88% of the variance with eigenvalues of 3.25 and 3.02, respectively. The clustering and discriminant analysis using curvilinear velocity and linear velocity as variables revealed the existence of 11 sperm subpopulations--four of them characterized by high velocities, two by medium values and five by low velocities. After freezing-thawing, nine subpopulations were found--four of high velocities, two of medium and three of low velocities. It is concluded that freezing-thawing not only impairs sperm motility but also produces changes in the sperm subpopulation structure in the canine ejaculate, that the evaluation of the sperm structure subpopulations is a better indicator of semen quality and freezeability than the use of mean values, and that two sperm motility quality indexes can be used to resume of the variables obtained from the CASA analysis.

Analysis of Variance↗