Determining the effective dimensionality of the genetic variance-covariance matrix

Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.

Pre-term delivery and periodontal disease: a case-control study from Croatia

Aim: The aim of this report was to assess the strength and influence of periodontitis as a possible risk factor for pre-term birth (PTB) in a cohort of 81 primiparous Croatian mothers aged 18-39 years. Methods: PTB cases (n=17; mean age 25 +/- 2.9 years; age range 20-33 years) were defined as spontaneous delivery after less than 37 completed weeks of gestation that were followed by spontaneous labour or spontaneous rupture of membranes. Controls (full-time births) were normal births at or after 37 weeks of gestation (n=64; mean age 25 +/- 2.9 years; age range 19-39 years). Information on known risk factors and obstetric factors included the current pregnancy history, maternal age at delivery, pre-natal care, nutritional status, tobacco use, alcohol use, genitourinary infections, vaginosis, gestational age, and birth weight. Full-mouth periodontal examination was performed on all mothers within 2 days of delivery. Results: PTB cases had significantly worse periodontal status than controls (p=0.008). Multivariate logistic regression model, after controlling for other risk factors, demonstrated that periodontal disease is a significant independent risk factor for PTB, with an adjusted odds ratio of 8.13 for the PTB group (95% confidence interval 2.73-45.9). Conclusion: Periodontal disease represents a strong, independent, and clinically significant risk factor for PTB in the studied cohort. There are strong indicators that periodontal therapy should form a part of preventive prenatal care in Croatia.

Application of MARS for the Construction of Nonparametric Models

2000 Mathematics Subject Classification: 62G08, 62P30.

Potential causes of ethnic group disparities in infertility:a hospital database study

Introduction - Lower success rates of in vitro fertilisation (IVF) in South East Asian countries compared to Western countries in informal studies and surveys was considered a reflection of variations in methodology and expertise. However, recent studies on the effects of ethnicity on success rates of infertility procedures in western countries have suggested other inherent contributing factors to the ethnic disparity but the evidence evaluating these is lacking. In our study we aim to investigate some of the comorbidities that might cause ethnic disparity to infertility and related procedures from hospital admissions data. Methods - Anonymous hospital admissions data on patients of various ethnic groups with infertility, comorbidities and infertility procedures from multiple hospitals in Birmingham andManchester, UK between 2000 and 2013 were obtained from the local health authority computerised hospital activity analysis register using ICD-10 and OPCS coding systems. Statistical analysis was performed using SPSS version 20.Results Of 522 223 female patients aged 18 and over, there were44 758 (8.4%) patients from South Asian (SA) community. 1156(13.4%) of the 8653 patients coded for infertility were SA, whichis a considerably higher proportion of the background SA population. For IVF procedures, the percentage of SA increased to15.4% (233 of the total 1479 patients). The mean age of SA codedfor infertility (30.6 ± 4.7 SD years versus 32.8 ± 4.9 SD years)and IVF (30.4 ± 4.3 SD years versus 32.7 ± 4.4 SD years) was significantly lower than caucasian patien ts (P < 0.001). A multivariate logistic regression model looking at patients with infertility, accounting for variations in age, showed that SA have significantly higher prevalence of hypothyroidism, obesity andiron-deficiency anaemia compared to caucasians but lower prevalence of endometriosis. Interestingly, psychiatric and psychological conditions diagnoses were seldom registered in infertility patients. Conclusion - Other studies suggest that various cultural, lifestyles, psychosocial and socio-economic factors may explain the disparities in IVF success rates between South Asians and caucasians. The fact that SA infertility and IVF patients, in ou rstudy, were significantly younger than caucasians and that their proportion is considerably higher than the background South Asian population suggests the influence of these factors. A significant psychiatric disease burden in other conditions and low numbers in our data suggest under diagnosis in this group.Despite the limitations of the coding data, from our study, we propose that hypothyroidism, obesity and/or iron-deficiency anaemia should be considered for the ethnic disparity. Further research in this topic is essential to fully investigate the reasons for such ethnic disparities.

Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Outcomes and Predictors of Mortality in Neurosurgical Patients at Mbarara Regional Referral Hospital

Background:

Knowing the scope of neurosurgical disease at Mbarara Hospital is critical for infrastructure planning, education and training. In this study, we aim to evaluate the neurosurgical outcomes and identify predictors of mortality in order to potentiate platforms for more effective interventions and inform future research efforts at Mbarara Hospital.

Methods:

This is retrospective chart review including patients of all ages with a neurosurgical disease or injury presenting to Mbarara Regional Referral Hospital (MRRH) between January 2012 to September 2015. Descriptive statistics were presented. A univariate analysis was used to obtain the odds ratios of mortality and 95% confidence intervals. Predictors of mortality were determined using multivariate logistic regression model.

Results:

A total of 1876 charts were reviewed. Of these, 1854 (had complete data and were?) were included in the analysis. The overall mortality rate was 12.75%; the mortality rates among all persons who underwent a neurosurgical procedure was 9.72%, and was 13.68% among those who did not undergo a neurosurgical procedure. Over 50% of patients were between 19 and 40 years old and the majority of were males (76.10%). The overall median length of stay was 5 days. Of all neurosurgical admissions, 87% were trauma patients. In comparison to mild head injury, closed head injury and intracranial hematoma patients were 5 (95% CI: 3.77, 8.26) and 2.5 times (95% CI: 1.64,3.98) more likely to die respectively. Procedure and diagnostic imaging were independent negative predictors of mortality (P <0.05). While age, ICU admission, admission GCS were positive predictors of mortality (P <0.05).

Conclusions:

The majority of hospital admissions were TBI patients, with RTIs being the most common mechanism of injury. Age, ICU admission, admission GCS, diagnostic imaging and undergoing surgery were independent predictors of mortality. Going forward, further exploration of patient characteristics is necessary to fully describe mortality outcomes and implement resource appropriate interventions that ultimately improve morbidity and mortality.

Analysis of the role of the transcription factor C/EBPβ in controlling uterine functions during early pregnancy

Embryo implantation into the endometrium is a complex biological process involving the integration of steroid hormone signaling, endometrial tissue remodeling and maternal- fetal communications. A successful pregnancy is the outcome of the timely integration of these events during the early stages of implantation. The involvement of ovarian steroid hormones, estrogen (E) and progesterone (P), acting through their cognate receptors, is essential for uterine functions during pregnancy. The molecular mechanisms that control the process of implantation are undergoing active exploration. Through our recent efforts, we identified the transcription factor, CCAAT Enhancer Binding Protein Beta (C/EBPb) as a prominent target of estrogen and progesterone signaling in the uterus. The development of a C/EBPb-null mouse model, which is infertile, presented us with an opportunity to analyze the role of this molecule in uterine function. We discovered that C/EBPb functions in two distinct manners: (i) by acting as a mediator of E-induced proliferation of the uterine epithelium and (ii) by controlling uterine stromal cell differentiation, a process known as decidualization, during pregnancy. My studies have delineated important mechanisms by which E regulates C/EBPb expression to induce DNA replication and prevent apoptosis of uterine epithelial cells during E-induced epithelial growth. In subsequent studies, I analyzed the role of C/EBPb in decidualization and uncovered a unique mechanism by which C/EBPb regulates the synthesis of a unique laminin-containing extracellular matrix (ECM) that supports stromal cell differentiation and embryo invasion. In order to better define the role of laminin in implantation, we developed a laminin gamma 1-conditional knockout mouse model. This is currently an area of ongoing investigation. The information gained from our analysis of C/EBPb function in the uterus provides new insights into the mechanisms of steroid hormone action during early pregnancy. Ultimately, our findings may aid in the understanding of dysregulation of hormone-controlled pathways that underlie early pregnancy loss and infertility in women.

Risk factors associated with negative in-vivo diagnostic results in bovine tuberculosis-infected cattle in Spain

BACKGROUND Despite great effort and investment incurred over decades to control bovine tuberculosis (bTB), it is still one of the most important zoonotic diseases in many areas of the world. Test-and-slaughter strategies, the basis of most bTB eradication programs carried out worldwide, have demonstrated its usefulness in the control of the disease. However, in certain countries, eradication has not been achieved due in part to limitations of currently available diagnostic tests. In this study, results of in-vivo and post-mortem diagnostic tests performed on 3,614 animals from 152 bTB-infected cattle herds (beef, dairy, and bullfighting) detected in 2007-2010 in the region of Castilla y León, Spain, were analyzed to identify factors associated with positive bacteriological results in cattle that were non-reactors to the single intradermal tuberculin test, to the interferon-gamma (IFN-γ) assay, or to both tests applied in parallel (Test negative/Culture + animals, T-/C+). The association of individual factors (age, productive type, and number of herd-tests performed since the disclosure of the outbreak) with the bacteriology outcome (positive/negative) was analyzed using a mixed multivariate logistic regression model. RESULTS The proportion of non-reactors with a positive post-mortem result ranged from 24.3% in the case of the SIT test to 12.9% (IFN-γ with 0.05 threshold) and 11.9% (95% CI 9.9-11.4%) using both tests in parallel. Older (>4.5 years) and bullfighting cattle were associated with increased odds of confirmed bTB infection by bacteriology, whereas dairy cattle showed a significantly lower risk. Ancillary use of IFN-γ assay reduced the proportion of T-/C + animals in high risk groups. CONCLUSIONS These results demonstrate the likelihood of positive bacteriological results in non-reactor cattle is influenced by individual epidemiological factors of tested animals. Increased surveillance on non-reactors with an increased probability of being false negative could be helpful to avoid bTB persistence, particularly in chronically infected herds. These findings may aid in the development of effective strategies for eradication of bTB in Spain.

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.

Strategy and model building in the fourth dimension: a null model for genotype × age interaction as a Gaussian stationary stochastic process

Abstract Background Using univariate and multivariate variance components linkage analysis methods, we studied possible genotype × age interaction in cardiovascular phenotypes related to the aging process from the Framingham Heart Study. Results We found evidence for genotype × age interaction for fasting glucose and systolic blood pressure. Conclusions There is polygenic genotype × age interaction for fasting glucose and systolic blood pressure and quantitative trait locus × age interaction for a linkage signal for systolic blood pressure phenotypes located on chromosome 17 at 67 cM.

Multifractality in the random parameter model for multivariate time series

The Random Parameter model was proposed to explain the structure of the covariance matrix in problems where most, but not all, of the eigenvalues of the covariance matrix can be explained by Random Matrix Theory. In this article, we explore the scaling properties of the model, as observed in the multifractal structure of the simulated time series. We use the Wavelet Transform Modulus Maxima technique to obtain the multifractal spectrum dependence with the parameters of the model. The model shows a scaling structure compatible with the stylized facts for a reasonable choice of the parameter values. (C) 2009 Elsevier B.V. All rights reserved.

Class attainment among British men - A multivariate extension of the CASMIN model of intergenerational class mobility

The CASMIN Project is arguably the most influential contemporary study of class mobility in the world. However, CASMIN results with respect to weak vertical status effects on class mobility have been extensively criticized. Drawing on arguments about how to model vertical mobility, Hout and Hauser (1992) show that class mobility is strongly determined by vertical socioeconomic differences. This paper extends these arguments by estimating the CASMIN model while explicitly controlling for individual determinants of socioeconomic attainment. Using the 1972 Oxford Mobility Data and the 1979 and 1983 British Election Studies, the paper employs mixed legit models to show how individual socioeconomic factors and categorical differences between classes shape intergenerational mobility. The findings highlight the multidimensionality of class mobility and its irreducibility to vertical movement up and down a stratification hierarchy.

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization.

The goal of the present work was assess the feasibility of using a pseudo-inverse and null-space optimization approach in the modeling of the shoulder biomechanics. The method was applied to a simplified musculoskeletal shoulder model. The mechanical system consisted in the arm, and the external forces were the arm weight, 6 scapulo-humeral muscles and the reaction at the glenohumeral joint, which was considered as a spherical joint. The muscle wrapping was considered around the humeral head assumed spherical. The dynamical equations were solved in a Lagrangian approach. The mathematical redundancy of the mechanical system was solved in two steps: a pseudo-inverse optimization to minimize the square of the muscle stress and a null-space optimization to restrict the muscle force to physiological limits. Several movements were simulated. The mathematical and numerical aspects of the constrained redundancy problem were efficiently solved by the proposed method. The prediction of muscle moment arms was consistent with cadaveric measurements and the joint reaction force was consistent with in vivo measurements. This preliminary work demonstrated that the developed algorithm has a great potential for more complex musculoskeletal modeling of the shoulder joint. In particular it could be further applied to a non-spherical joint model, allowing for the natural translation of the humeral head in the glenoid fossa.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.