Factor structure of a Cantonese-version Pittsburgh Sleep Quality Index

The objectives of this study were to translate, validate and test the factor structure of a Cantonese version of the Pittsburgh Sleep Quality Index (Cantonese-PSQI) in support of its use among Cantonese-speaking Chinese in Hong Kong, southern China and Chinese immigrant communities in the West. The study involved Cantonese-speaking Chinese aged 45 or over in Hong Kong. A cross-sectional survey of 97 participants was firstly conducted to validate the Cantonese-PSQI. A second survey of 794 participants identified the best-fitted factor structure of the scale. Confirmatory factor analysis then functioned to develop and test a single-factor model, a two-factor model, and a three-factor model. The one-factor model turned out to be unacceptable due to its poor fit (L² = 303.408, d.f. = 14, SRMR = 0.085; RMSEA = 0.161; CFI = 0.805). The three-factor model, despite its very good fit (L² = 36.450, d.f. = 11, SRMR = 0.022; RMSEA = 0.054; CFI = 0.983), was unacceptable because of the extraordinarily high correlation (r = 0.928) between two of its three factors, namely the sleep quality factor and the daily disturbances factor. The two-factor model showed a good fit with the data of all the participants (L² = 46.339, d.f. = 13, SRMR = 0.028; RMSEA = 0.057; CFI = 0.978), and with those of the middle-aged, the young-old and the old-old groups. The two-factor model of the Cantonese-PSQI characterized by sleep efficiency and sleep quality is therefore better than the original one-factor model and the three-factor model for measuring the sleep quality of middle-aged and older Cantonese-speaking Chinese.

     

Criterion validity of the Pittsburgh Sleep Quality Index: Investigation in a non-clinical sample

The objective of this study was to investigate the reliability and validity of the Pittsburgh Sleep Quality Index (PSQI) in a non-clinical sample consisting of younger and older adults. There has been little research validating the PSQI with respect to multinight recording as with actigraphy, and more validation is needed in samples not specifically selected for clinical disturbance. Also, the degree to which the PSQI scores may reflect depressive symptoms versus actual sleep disturbance remains unclear. One-hundred and twelve volunteers (53 younger and 59 older) were screened for their ability to perform treadmill exercises; inclusion was not based on sleep disturbance or depression. Internal homogeneity was evaluated by correlating PSQI component scores with the global score. Global and component scores were correlated with a sleep diary, actigraphy, and centers for epidemiological studies - depression scale scores to investigate criterion validity. Results showed high internal homogeneity. PSQI global score correlated appreciably with sleep diary variables and the depression scale, but not with any actigraphic sleep variables. These results suggest that the PSQI has good internal homogeneity, but may be less reflective of actual sleep parameters than a negative cognitive viewpoint or pessimistic thinking. The sleep complaints measured may often be more indicative of general dissatisfaction than of any specifically sleep-related disturbance.     

Heterogeneity of sleep quality based on the Pittsburgh Sleep Quality Index in a community sample: a latent class analysis

Sleep and Biological Rhythms - This study aims to assess the latent dimensional structure of sleep quality as measured by seven components of the Pittsburgh Sleep Quality Index (PSQI) and evaluate...     

Factor analysis models for structuring covariance matrices of additive genetic effects: a Bayesian implementation

Multivariate linear models are increasingly important in quantitative genetics. In high dimensional specifications, factor analysis (FA) may provide an avenue for structuring (co)variance matrices, thus reducing the number of parameters needed for describing (co)dispersion. We describe how FA can be used to model genetic effects in the context of a multivariate linear mixed model. An orthogonal common factor structure is used to model genetic effects under Gaussian assumption, so that the marginal likelihood is multivariate normal with a structured genetic (co)variance matrix. Under standard prior assumptions, all fully conditional distributions have closed form, and samples from the joint posterior distribution can be obtained via Gibbs sampling. The model and the algorithm developed for its Bayesian implementation were used to describe five repeated records of milk yield in dairy cattle, and a one common FA model was compared with a standard multiple trait model. The Bayesian Information Criterion favored the FA model.     

Construct Validity and Factor Structure of the Pittsburgh Sleep Quality Index and Epworth Sleepiness Scale in a Multi-National Study of African,South East Asian and South American College Students

Background

The Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) are questionnaires used to assess sleep quality and excessive daytime sleepiness in clinical and population-based studies. The present study aimed to evaluate the construct validity and factor structure of the PSQI and ESS questionnaires among young adults in four countries (Chile, Ethiopia, Peru and Thailand).

Methods

A cross-sectional study was conducted among 8,481 undergraduate students. Students were invited to complete a self-administered questionnaire that collected information about lifestyle, demographic, and sleep characteristics. In each country, the construct validity and factorial structures of PSQI and ESS questionnaires were tested through exploratory and confirmatory factor analyses (EFA and CFA).

Results

The largest component-total correlation coefficient for sleep quality as assessed using PSQI was noted in Chile (r = 0.71) while the smallest component-total correlation coefficient was noted for sleep medication use in Peru (r = 0.28). The largest component-total correlation coefficient for excessive daytime sleepiness as assessed using ESS was found for item 1 (sitting/reading) in Chile (r = 0.65) while the lowest item-total correlation was observed for item 6 (sitting and talking to someone) in Thailand (r = 0.35). Using both EFA and CFA a two-factor model was found for PSQI questionnaire in Chile, Ethiopia and Thailand while a three-factor model was found for Peru. For the ESS questionnaire, we noted two factors for all four countries

Conclusion

Overall, we documented cross-cultural comparability of sleep quality and excessive daytime sleepiness measures using the PSQI and ESS questionnaires among Asian, South American and African young adults. Although both the PSQI and ESS were originally developed as single-factor questionnaires, the results of our EFA and CFA revealed the multi- dimensionality of the scales suggesting limited usefulness of the global PSQI and ESS scores to assess sleep quality and excessive daytime sleepiness.     

Graphical exploration of gene expression data: a comparative study of three multivariate methods

This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.     

Estimating the Weighting Components of a Health Quality Index

This paper introduces a maximum likelihood estimation model that uses patients' pretherapy characteristics to determine the weighting components of multipe outcomes of quality health. The resulting health quality index' can then be included in cost-effectiveness analyses to compare alternative treatment plans. A Monte Carlo simulation is performed to test the model's robustness and power.     

Causes of species diversity differences: a comparative analysis of Markov models

While many dynamic processes have been proposed to produce diversity differences among communities, most empirical investigations focus on static system attributes. An ideal analysis would consider multiple dynamic processes and their impact on many community members, but such analyses can be logistically daunting. I compared Markov chain models of ecological communities to explore general processes leading to diversity differences of sessile species between coral reefs and rocky intertidal mussel beds. As predicted by diversity theory, high diversity coral reefs had lower species replacement probabilities and higher disturbance rates than did lower diversity mussel beds. Intransitivities in species replacements, recruitment limitation and responses to perturbing species from equilibrium (Jacobian elements) did not differ significantly between systems. Tradeoffs between the displacement risk or displacement ability of a species and either disturbance risk or colonising ability were not apparent. Manipulating the coral reef model to eliminate disturbance or intransitivities caused declines in species diversity, whereas removing recruitment limitation or increasing the probability of interspecific replacement did not. Higher overall disturbance levels can explain part of the diversity difference between systems, but much remained unexplained, indicating that details of the pattern and strengths of species interactions were probably extremely important.     

Quantification of the whole-body burden of radiographic osteoarthritis using factor analysis

Introduction  

Although osteoarthritis (OA) commonly involves multiple joints, no widely accepted method for quantifying whole-body OA burden exists. Therefore, our aim was to apply factor analytic methods to radiographic OA (rOA) grades across multiple joint sites, representing both presence and severity, to quantify the burden of rOA.     

Parasites and stable isotopes: a comparative analysis of isotopic discrimination in parasitic trophic interactions

Stable isotopes are widely used to identify trophic interactions and to determine trophic positions of organisms in food webs. Comparative studies have provided general insights into the variation in isotopic composition between consumers and their diet (discrimination factors) in predator–prey and herbivore–plant relationships while other major components of food webs such as host–parasite interactions have been largely overlooked. In this study, we conducted a literature‐based comparative analysis using phylogenetically‐controlled mixed effects models, accounting for both parasite and host phylogenies, to investigate patterns and potential drivers in Δ¹³C and Δ¹⁵N discrimination factors in metazoan parasitic trophic interactions. Our analysis of 101 parasite–host pairs revealed a large range in Δ¹³C (–8.2 to 6.5) and Δ¹⁵N (–6.7 to 9.0) among parasite species, with no significant overall depletion or enrichment of ¹³C and ¹⁵N in parasites. As previously found in other trophic interactions, we identified a scaling relationship between the host isotopic value and both discrimination factors with Δ¹³C and Δ¹⁵N decreasing with increasing host δ¹³C and δ¹⁵N, respectively. Furthermore, parasite phylogenetic history explained a large fraction (>60%) of the observed variation in the Δ¹⁵N discrimination factor. Our findings suggest that the traditional isotope ecology framework (using an average Δ¹⁵N of 3.4‰) applies poorly to parasitic trophic interactions. They further indicate the need for a scaled rather than a fixed trophic discrimination factor framework along gradients of host δ¹⁵N. We also identified several conceptual and methodological issues which should to be considered in future research to help integrate parasitic interactions into a holistic isotope ecology framework across diverse trophic interactions.     

Psychometric properties of the Epworth Sleepiness Scale: A factor analysis and item-response theory approach

The purpose of this study is to examine the psychometric properties of the Epworth Sleepiness Scale (ESS) in two languages, German and English. Students from a university in Austria (N = 292; 55 males; mean age = 18.71 ± 1.71 years; 237 females; mean age = 18.24 ± 0.88 years) and a university in the US (N = 329; 128 males; mean age = 18.71 ± 0.88 years; 201 females; mean age = 21.59 ± 2.27 years) completed the ESS. An exploratory-factor analysis was completed to examine dimensionality of the ESS. Item response theory (IRT) analyses were used to provide information about the response rates on the items on the ESS and provide differential item functioning (DIF) analyses to examine whether the items were interpreted differently between the two languages. The factor analyses suggest that the ESS measures two distinct sleepiness constructs. These constructs indicate that the ESS is probing sleepiness in settings requiring active versus passive responding. The IRT analyses found that overall, the items on the ESS perform well as a measure of sleepiness. However, Item 8 and to a lesser extent Item 6 were being interpreted differently by respondents in comparison to the other items. In addition, the DIF analyses showed that the responses between German and English were very similar indicating that there are only minor measurement differences between the two language versions of the ESS. These findings suggest that the ESS provides a reliable measure of propensity to sleepiness; however, it does convey a two-factor approach to sleepiness. Researchers and clinicians can use the German and English versions of the ESS but may wish to exclude Item 8 when calculating a total sleepiness score.     

Characterization of a comparative model of the extracellular domain of the epidermal growth factor receptor

The Epidermal Growth Factor (EGF) receptor is a tyrosine kinase that mediates the biological effects of ligands such as EGF and transforming growth factor alpha. An understanding of the molecular basis of its action has been hindered by a lack of structural and mutational data on the receptor. We have constructed comparative models of the four extracellular domains of the EGF receptor that are based on the structure of the first three domains of the insulin-like growth factor-1 (IGF-1) receptor. The first and third domains of the EGF receptor, L1 and L2, are right-handed beta helices. The second and fourth domains of the EGF receptor, S1 and S2, consist of the modules held together by disulfide bonds, which, except for the first module of the S1 domain, form rod-like structures. The arrangement of the L1 and S1 domains of the model are similar to that of the first two domains of the IGF-1 receptor, whereas that of the L2 and S2 domains appear to be significantly different. Using the EGF receptor model and limited information from the literature, we have proposed a number of regions that may be involved in the functioning of the receptor. In particular, the faces containing the large beta sheets in the L1 and L2 domains have been suggested to be involved with ligand binding of EGF to its receptor.     

Stochastic matrix models for conservation and management: a comparative review of methods

Stochastic matrix models are frequently used by conservation biologists to measure the viability of species and to explore various management actions. Models are typically parameterized using two or more sets of estimated transition rates between age/size/stage classes. While standard methods exist for analyzing a single set of transition rates, a variety of methods have been employed to analyze multiple sets of transition rates. We review applications of stochastic matrix models to problems in conservation and use simulation studies to compare the performance of different analytic methods currently in use. We find that model conclusions are likely to be robust to the choice of parametric distribution used to model vital rate fluctuations over time. However, conclusions can be highly sensitive to the within-year correlation structure among vital rates, and therefore we suggest using analytical methods that provide a means of conducting a sensitivity analysis with respect to correlation parameters. Our simulation results also suggest that the precision of population viability estimates can be improved by using matrix models that incorporate environmental covariates in conjunction with experiments to estimate transition rates under a range of environmental conditions.     

Self‐Reported Body Mass Index and Health‐Related Quality of Life: Findings from the Behavioral Risk Factor Surveillance System

Objective: To examine the relationship between self‐reported body mass index (BMI) and health‐related quality of life in the general adult population in the United States. Research Methods and Procedures: Using data from 109,076 respondents in the 1996 Behavioral Risk Factor Surveillance System, we examined how self‐reported BMI is associated with five health‐related quality of life measures developed by the Centers for Disease Control and Prevention for population health surveillance. Results: After adjusting for age, gender, race or ethnicity, educational attainment, employment status, smoking status, and physical activity status, participants with a self‐reported BMI of <18.5 kg/m² and participants with a self‐reported BMI of ≥30 kg/m² reported impaired quality of life. Compared with persons with a self‐reported BMI of 18.5 to <25 kg/m², odds ratios (ORs) of poor or fair self‐rated health increased among persons with self‐reported BMIs of <18.5 (1.57, 95% confidence interval [CI]: 1.31 to 1.89), 25 to <30 kg/m² (1.12, 95% CI: 1.04 to 1.20), 30 to <35 kg/m² (1.65, 95% CI: 1.50 to 1.81), 35 to <40 kg/m² (2.58, 95% CI: 2.21 to 3.00), and ≥40 kg/m² (3.23, 95% CI: 2.63 to 3.95); ORs for reporting ≥14 days of poor physical health during the previous 30 days were 1.44 (95% CI: 1.21 to 1.72), 1.04 (95% CI: 0.96 to 1.14), 1.32 (95% CI: 1.19 to 1.47), 1.80 (95% CI: 1.52 to 2.13), and 2.37 (95% CI: 1.90 to 2.94), respectively; ORs for having ≥14 days of poor mental health during the previous 30 days were 1.18 (95% CI: 0.97 to 1.42), 1.02 (95% CI: 0.95 to 1.11), 1.22 (95% CI: 1.10 to 1.36), 1.68 (95% CI: 1.42 to 1.98), and 1.66 (95% CI: 1.32 to 2.09), respectively. Discussion: In the largest study to date, low and increased self‐reported BMI significantly impaired health‐related quality of life. Particularly, deviations from normal BMI affected physical functioning more strongly than mental functioning.     

Synergistic induction of endothelial tissue factor by tumor necrosis factor and vascular endothelial growth factor: functional analysis of the tumor necrosis factor receptors

Tissue factor expression on the surface of endothelial cells can be induced by tumor necrosis factor (TNF) and vascular endothelial growth factor (VEGF) in a synergistic manner. We have investigated the role of the two different TNF receptors for this synergy. Firstly, stimulation of the 60 kDa TNF receptor (TNFR60) by a mutant of TNF specific for TNFR60 induced responses comparable to wild-type TNF. In contrast, stimulation of TNFR80 by a TNFR80-specific TNF mutein did not result in enhancement of tissue factor expression even in the presence of a suboptimal TNFR60 triggering. Secondly, we tested neutralizing TNF receptor antibodies for inhibition of tissue factor synthesis induced by VEGF and TNF. A TNFR60-specific antibody inhibited tissue factor production over a broad range of TNF concentrations, indicating an essential role of TNFR60 in the TNF/VEGF synergy. In contrast, blocking of TNF binding to TNFR80 strongly inhibited TNF-induced tissue factor expression at low, but less pronounced at high, TNF concentrations. In conclusion, these data are in agreement with a model in which TNFR80 participates in the synergy between VEGF and low concentrations of soluble TNF by passing the ligand to the signalling TNFR60.     

Origin of the 1918 Spanish influenza virus: a comparative genomic analysis

To test the avian-origin hypothesis of the 1918 Spanish influenza virus we surveyed influenza sequences from a broad taxonomic distribution and collected 65 full-length genomes representing avian, human and "classic" swine H1N1 lineages in addition to numerous other swine (H1N2, H3N1, and H3N2), human (H2N2, H3N2, and H5N1), and avian (H1N1, H4N6, H5N1, H6N1, H6N6, H6N8, H7N3, H8N4, H9N2, and H13N2) subtypes. Amino acids from all eight segments were concatenated, aligned, and used for phylogenetic analyses. In addition, the genes of the polymerase complex (PB1, PB2, and PA) were analyzed individually. All of our results showed the Brevig-Mission/1918 strain in a position basal to the rest of the clade containing human H1N1s and were consistent with a reassortment hypothesis for the origin of the 1918 virus. Our genome phylogeny further indicates a sister relationship with the "classic" swine H1N1 lineage. The individual PB1, PB2, and PA phylogenies were consistent with reassortment/recombination hypotheses for these genes. These results demonstrate the importance of using a complete-genome approach for addressing the avian-origin hypothesis and predicting the emergence of new pandemic influenza strains.     

Initial Development and Evaluation of a Sediment Quality Index for the Great Lakes Region

A sediment quality index (SQI) based on the Canadian Water Quality Index was developed and applied to the assessment of sediment quality in two Great Lakes Areas Of Concern where metals are the primary contaminants of potential concern, Peninsula Harbour (Lake Superior) and Collingwood Harbour (Lake Huron). The SQI was calculated according to an equation incorporating two elements; scope—the number of variables that do not meet guideline objectives; and, amplitude—the magnitude by which variables exceed guideline objectives. Categorizations of sediment quality were developed based on SQI scores. The robustness of the SQI was evaluated through comparison of the relative rankings of sediment quality in the two test areas with results obtained from principle components analysis (PCA) incorporating reference sites, and calculations of hazard quotients (HQs). Trends and rankings in sediment quality determined by the SQI were similar to those calculated using PCA at both test areas. The HQs also appeared to be good indicators of sediment quality. Both the SQI and HQ methods are based on existing Sediment Quality Guidelines, but the SQI had the added benefit of allowing straightforward integration of multiple contaminants. The SQI and PCA analyses appeared complementary in that the SQI incorporated information on the number of variables exceeding guideline values and the degree to which these guidelines were exceeded. The PCA allowed a simple check of the SQI by relating test conditions to regional background. It is recommended that this analysis be performed concurrently with SQI to ensure that non-anthropogenic sources of contaminants (metals in this case) are not considered as representing an anthropogenic hazard.     

NMR restraint analysis of transforming growth factor alpha: a key component for NMR structure refinement.

Structures of the protein, transforming growth factor alpha (TGF-alpha), have been derived from NMR data using distance geometry and subsequent energy refinement. Analysis of the sequential NOE distance bounds using a template algorithm provides a check for consistency in the calculation of bounds, stereospecific assignment of prochiral centers, and secondary structure assignment. Application of the template algorithm to the long range NOEs found within the NMR data sets collected at pH 6.3 and pH 3.4 is used to assess the confidence levels for the accuracy of the structures obtained from modeling. The method also provides critical insight in differentiating regions of the structure that are well defined from those that are not. Use of the restraint analysis protocol is shown to be a powerful adjunct to currently used methods for the assignment of protein structures from NMR data.