Statistical theory indicates that hierarchical clustering by interviewers or raters needs to be considered to avoid incorrect inferences when performing any analyses including regression, factor analysis (FA) or item response theory (IRT) modelling of binary or ordinal data. We use simulated Positive and Negative Syndrome Scale (PANSS) data to show the consequences (in terms of bias, variance and mean square error) of using an analysis ignoring clustering on confirmatory factor analysis (CFA) estimates. Our investigation includes the performance of different estimators, such as maximum likelihood, weighted least squares and Markov Chain Monte Carlo (MCMC). Our simulation results suggest that ignoring clustering may lead to serious bias of the estimated factor loadings, item thresholds, and corresponding standard errors in CFAs for ordinal item response data typical of that commonly encountered in psychiatric research. In addition, fit indices tend to show a poor fit for the hypothesized structural model. MCMC estimation may be more robust against clustering than maximum likelihood and weighted least squares approaches but further investigation of these issues is warranted in future simulation studies of other datasets. Copyright © 2015 John Wiley & Sons, Ltd.
The aim of this study is to analyze social and individual determinants of health behaviour. The following factors are evaluated: nutrition, physical activity (PA) and smoking behaviour. The examined determinants of health behavior include: health-specific self-efficacy and health locus of control. Material and Methods: The survey was carried out among 298 students at state and private universities in Poznań and 342 teachers in primary and secondary schools in the Wielkopolska Province in Poland. The author’s questionnaire was used to assess nutrition and smoking status and the International Physical Activity Questionnaire (IPAQ) served to evaluate PA. The health related self-efficacy questionnaires and Multidimensional Health Locus of Control Scale (MHCL) were employed. Results and Conclusion: Health oriented physical education studies favour a more healthy lifestyle both during the studies and employment. The worst health behaviour patterns have been found among pedagogy students. The issue of healthy life style should be given more prominence in the curriculum at the undergraduate level to better develop health-sensitive personality in future teachers.
- MeSH
- Adult MeSH
- Menu Planning MeSH
- Smoking MeSH
- Humans MeSH
- Motor Activity MeSH
- Attitude to Health * MeSH
- Surveys and Questionnaires MeSH
- Self Efficacy MeSH
- Cluster Analysis MeSH
- Schools manpower MeSH
- Statistics as Topic MeSH
- Feeding Behavior MeSH
- Students MeSH
- Education MeSH
- Health Behavior MeSH
- Health Education * manpower MeSH
- Life Style MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Comparative Study MeSH
... 4.6 Gaussian Quadratures and Orthogonal Polynomials 179 -- 4.7 Adaptive Quadrature 194 -- 4.8 Multidimensional ... ... Classification and Inference 840 -- 16.0 Introduction 840 -- 16.1 Gaussian Mixture Models and k-Means Clustering ... ... Viterbi Decoding 850 -- 16.3 Markov Models and Hidden Markov Modeling 856 -- 16.4 Hierarchical Clustering ...
3rd ed. xxi, 1235 s. : il. ; 27 cm + 1 CD-ROM
- MeSH
- Mathematical Computing MeSH
- Mathematics MeSH
- Numerical Analysis, Computer-Assisted * MeSH
- Publication type
- Monograph MeSH
- Conspectus
- Počítačová věda. Výpočetní technika. Informační technologie
- NML Fields
- přírodní vědy
- přírodní vědy
In vitro fertilization (IVF) is fraught with problems and currently proteomics approaches are being tried out to examine the microenvironment of the follicle in order to assess biological and immunological parameters that may affect its development. Additionally, better understanding of reproductive process may help increase IVF birth rate per embryo transfer and at the same time avoid spontaneous miscarriages or life threatening conditions such as ovarian hyperstimulation syndrome. The primary aim of this study was to search for specific differences in protein composition of human follicular fluid (HFF) and plasma in order to identify proteins that accumulate or are absent in HFF. Depletion of abundant proteins combined with multidimensional protein fractionation allowed the study of middle- and lower-abundance proteins. Paired comparison study examining HFF with plasma/serum from women undergoing successful IVF revealed important differences in the protein composition which may improve our knowledge of the follicular microenvironment and its biological role. This study showed involvement of innate immune function of complement cascade in HFF. Complement inhibition and the presence of C-terminal fragment of perlecan suggested possible links to angiogenesis which is a vital process in folliculogenesis and placental development. Differences in proteins associated with blood coagulation were also found in the follicular milieu. Several specific proteins were observed, many of which have not yet been associated with follicle/oocyte maturation. These proteins together with their regulatory pathways may play a vital role in the reproductive process.
- MeSH
- Electrophoresis, Gel, Two-Dimensional MeSH
- Fertilization in Vitro MeSH
- Follicular Fluid chemistry metabolism MeSH
- Hemolysis MeSH
- Heparan Sulfate Proteoglycans analysis MeSH
- Immunoblotting MeSH
- Clusterin analysis MeSH
- Complement C4 analysis metabolism MeSH
- Complement C3 analysis metabolism MeSH
- Humans MeSH
- Proteome analysis metabolism MeSH
- Reproducibility of Results MeSH
- Cluster Analysis MeSH
- Signal Transduction MeSH
- Chromatography, High Pressure Liquid MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning and to identify the differences between sexes, academic years, socioeconomic strata, and the generated profiles. A total of 542 healthy and physically active students (445 males, 97 females; 19.8 [2.2] years; 66.0 [10.3] kg; 169.5 [7.8] cm) participated in this cross-sectional study. Their indirect VO2max (Cooper and Shuttle-Run 20 m tests), lower-limb power (horizontal jump), sprint (30 m), agility (shuttle run), and flexibility (sit-and-reach) were assessed. The participants were profiled using clustering algorithms after setting the optimal number of clusters through an internal validation using R packages. Non-parametric tests were used to identify the differences (p < 0.05). The higher percentage of the population were freshmen (51.4%) and middle-income (64.0%) students. Seniors and juniors showed a better physical fitness than first-year students. No significant differences were found between their socioeconomic strata (p > 0.05). Two profiles were identified using hierarchical clustering (Cluster 1 = 318 vs. Cluster 2 = 224). The matching analysis revealed that physical fitness explained the variation in the data, with Cluster 2 as a sex-independent and more physically fit group. All variables differed significantly between the sexes (except the body mass index [p = 0.218]) and the generated profiles (except stature [p = 0.559] and flexibility [p = 0.115]). A multidimensional analysis showed that the body mass, cardiorespiratory fitness, and agility contributed the most to the data variation so that they can be used as profiling variables. This profiling method accurately identified the relevant variables to reinforce exercise recommendations in a low physical performance and overweight majors.
OBJECTIVES: The study presents the findings of a quantitative research conducted among people aged over 90, who live in a large town of Hungary, Debrecen. The aim of the research was to examine the lifestyle, attitudes, values, and physical and mental condition of old and long-lived people. We laid a special emphasis on the exploration of the life perspectives, mood and mental youth, and their interconnections. METHODS: The sociological questionnaire used for data collection (159 questions) was intended to inquire socio-demographic characteristics, dietary habits, health condition, physical activity, and identity features. Further examinations were conducted in order to measure the level of depression using the Geriatric Depression Scale (GDS) and mental condition using the Mini-Mental State Examination (MMSE). We managed to reach out to the elderly living in the town on the basis of family doctors' districts (N = 212). We dealt with a subsample of 115 people since we got answers for all questions from them. During data processing, we applied multivariate statistical methods, first of all linear regression analysis and cluster analysis. We examined the differences between clusters using variation analysis. RESULTS: According to our results, the extremely low educational level of the elderly belonging to the target group did not decrease their life perspectives, but it had a significant impact on the age when their illness begun. We revealed a connection between the mental condition and the level of depression. Better mental condition (higher MMSE) resulted in lower depression level (low GDS). One of our main finding is that the change in the level of depression (GDS) is 13.4% due to the change in the mental condition (MMSE). CONCLUSIONS: Physical and mental activity, personal autonomy, a wide range of activities, and avoiding isolation and solitude allow people to experience quality ageing; all these factors can be substantially influenced by the status acquired at a younger age. We believe that it is extremely important for the society to develop guarantees for active old age, which would ensure the optimal balance between the possibilities of physical and mental health, social participation and safety.
- MeSH
- Depression diagnosis epidemiology MeSH
- Mental Health * MeSH
- Geriatric Assessment MeSH
- Cognition physiology MeSH
- Quality of Life psychology MeSH
- Humans MeSH
- Surveys and Questionnaires MeSH
- Psychiatric Status Rating Scales MeSH
- Aged MeSH
- Aging physiology psychology MeSH
- Health Status MeSH
- Life Style MeSH
- Check Tag
- Humans MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Hungary MeSH
A better description of the leukemia cell surface proteome (surfaceome) is a prerequisite for the development of diagnostic and therapeutic tools. Insights into the complexity of the surfaceome have been limited by the lack of suitable methodologies. We combined a leukemia xenograft model with the discovery-driven chemoproteomic Cell Surface Capture technology to explore the B-cell precursor acute lymphoblastic leukemia (BCP-ALL) surfaceome; 713 cell surface proteins, including 181 CD proteins, were detected through combined analysis of 19 BCP-ALL cases. Diagnostic immunophenotypes were recapitulated in each case, and subtype specific markers were detected. To identify new leukemia-associated markers, we filtered the surfaceome data set against gene expression information from sorted, normal hematopoietic cells. Nine candidate markers (CD18, CD63, CD31, CD97, CD102, CD157, CD217, CD305, and CD317) were validated by flow cytometry in patient samples at diagnosis and during chemotherapy. CD97, CD157, CD63, and CD305 accounted for the most informative differences between normal and malignant cells. The ALL surfaceome constitutes a valuable resource to assist the functional exploration of surface markers in normal and malignant lymphopoiesis. This unbiased approach will also contribute to the development of strategies that rely on complex information for multidimensional flow cytometry data analysis to improve its diagnostic applications.
- MeSH
- Precursor Cell Lymphoblastic Leukemia-Lymphoma immunology metabolism MeSH
- Antigens, CD analysis MeSH
- Immunophenotyping MeSH
- Humans MeSH
- Membrane Proteins * analysis metabolism MeSH
- Mice MeSH
- Biomarkers, Tumor * analysis MeSH
- Proteome * analysis metabolism MeSH
- Flow Cytometry MeSH
- Xenograft Model Antitumor Assays MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
