938 resultados para Multivariate Linkage Analysis
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Abstract: INTRODUCTION Characterization of Mycobacterium tuberculosis (MTB) isolates by DNA fingerprinting has contributed to tuberculosis (TB) control. The aim of this study was to determine the genetic diversity of MTB isolates from Tehran province in Iran. METHODS MTB isolates from 60 Iranian and 10 Afghan TB patients were fingerprinted by standard IS6110-restriction fragment length polymorphism (RFLP) analysis and spoligotyping. RESULTS The copy number of IS6110 ranged from 10-24 per isolate. The isolates were classified into 22 clusters showing ≥ 80% similarity by RFLP analysis. Fourteen multidrug-resistant (MDR) isolates were grouped into 4 IS6110-RFLP clusters, with 10 isolates [71% (95% CI: 45-89%)] in 1 cluster, suggesting a possible epidemiological linkage. Eighteen Iranian isolates showed ≥ 80% similarity with Afghan isolates. There were no strains with identical fingerprints. Spoligotyping of 70 isolates produced 23 distinct patterns. Sixty (85.7%) isolates were grouped into 13 clusters, while the remaining 10 isolates (14.2%) were not clustered. Ural (formerly Haarlem4) (n = 22, 31.4%) was the most common family followed by Central Asian strain (CAS) (n = 18, 25.7%) and T (n = 9, 12.8%) families. Only 1strain was characterized as having the Beijing genotype. Among 60 Iranian and 10 Afghan MTB isolates, 25% (95% CI: 16-37) and 70% (95% CI: 39-89) were categorized as Ural lineage, respectively. CONCLUSIONS A higher prevalence of Ural family MTB isolates among Afghan patients than among Iranian patients suggests the possible transmission of this lineage following the immigration of Afghans to Iran.
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In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.
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OBJECTIVE: To study the influence of immune and nonimmune risk factors on the development of allograft vasculopathy after cardiac transplantation. METHODS: We studied 39 patients with a mean age of 46±12 years. The following variables were analyzed: weight (kg), body mass index (kg/m²), donor's age and sex, rejection episodes in the first and second years after transplantation, systolic and diastolic blood pressures (mmHg), total cholesterol and fractions (mg/dL), triglycerides (mg/dL), diabetes, and cytomegalovirus infection. The presence of allograft vasculopathy was established through coronary angiography. RESULTS: Allograft vasculopathy was observed in 15 (38%) patients. No statistically significant difference was observed between the two groups in regard to hypertension, cytomegalovirus infection, diabetes, donor's sex and age, rejection episodes in the first and second years after transplantation, and cholesterol levels. We observed a tendency toward higher levels of triglycerides in the group with disease. Univariate and multivariate analyses showed statistically significant differences between the two groups when we analyzed the body mass index (24.53±4.3 versus 28.11±4.6; p=0.019). CONCLUSION: Body mass index was an important marker of allograft vasculopathy in the population studied.
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Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.
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BACKGROUND: First hospitalisation for a psychotic episode causes intense distress to patients and families, but offers an opportunity to make a diagnosis and start treatment. However, linkage to outpatient psychiatric care remains a notoriously difficult step for young psychotic patients, who frequently interrupt treatment after hospitalisation. Persistence of symptoms, and untreated psychosis may therefore remain a problem despite hospitalisation and proper diagnosis. With persisting psychotic symptoms, numerous complications may arise: breakdown in relationships, loss of family and social support, loss of employment or study interruption, denial of disease, depression, suicide, substance abuse and violence. Understanding mechanisms that might promote linkage to outpatient psychiatric care is therefore a critical issue, especially in early intervention in psychotic disorders. OBJECTIVE: To study which factors hinder or promote linkage of young psychotic patients to outpatient psychiatric care after a first hospitalisation, in the absence of a vertically integrated program for early psychosis. Method. File audit study of all patients aged 18 to 30 who were admitted for the first time to the psychiatric University Hospital of Lausanne in the year 2000. For statistical analysis, chi2 tests were used for categorical variables and t-test for dimensional variables; p<0.05 was considered as statistically significant. RESULTS: 230 patients aged 18 to 30 were admitted to the Lausanne University psychiatric hospital for the first time during the year 2000, 52 of them with a diagnosis of psychosis (23%). Patients with psychosis were mostly male (83%) when compared with non-psychosis patients (49%). Furthermore, they had (1) 10 days longer mean duration of stay (24 vs 14 days), (2) a higher rate of compulsory admissions (53% vs 22%) and (3) were more often hospitalised by a psychiatrist rather than by a general practitioner (83% vs 53%). Other socio-demographic and clinical features at admission were similar in the two groups. Among the 52 psychotic patients, 10 did not stay in the catchment area for subsequent treatment. Among the 42 psychotic patients who remained in the catchment area after discharge, 20 (48%) did not attend the scheduled or rescheduled outpatient appointment. None of the socio demographic characteristics were associated with attendance to outpatient appointments. On the other hand, voluntary admission and suicidal ideation before admission were significantly related to attending the initial appointment. Moreover, some elements of treatment seemed to be associated with higher likelihood to attend outpatient treatment: (1) provision of information to the patient regarding diagnosis, (2) discussion about the treatment plan between in- and outpatient staff, (3) involvement of outpatient team during hospitalisation, and (4) elaboration of concrete strategies to face basic needs, organise daily activities or education and reach for help in case of need. CONCLUSION: As in other studies, half of the patients admitted for a first psychotic episode failed to link to outpatient psychiatric care. Our study suggests that treatment rather than patient's characteristics play a critical role in this phenomenon. Development of a partnership and involvement of patients in the decision process, provision of good information regarding the illness, clear definition of the treatment plan, development of concrete strategies to cope with the illness and its potential complications, and involvement of the outpatient treating team already during hospitalisation, all came out as critical strategies to facilitate adherence to outpatient care. While the current rate of disengagement after admission is highly concerning, our finding are encouraging since they constitute strategies that can easily be implemented. An open approach to psychosis, the development of partnership with patients and a better coordination between inpatient and outpatient teams should therefore be among the targets of early intervention programs. These observations might help setting up priorities when conceptualising new programs and facilitate the implementation of services that facilitate engagement of patients in treatment during the critical initial phase of psychotic disorders.
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This paper seeks to identify whether there is a representative empirical Okun’s Law coefficient (OLC) and to measure its size. We carry out a meta regression analysis on a sample of 269 estimates of the OLC to uncover reasons for differences in empirical results and to estimate the ‘true’ OLC. On statistical (and other) grounds, we find it appropriate to investigate two separate subsamples, using respectively (some measure of) unemployment or output as dependent variable. Our results can be summarized as follows. First, there is evidence of type II publication bias in both sub-samples, but a type I bias is present only among the papers using some measure of unemployment as the dependent variable. Second, after correction for publication bias, authentic and statistically significant OLC effects are present in both sub-samples. Third, bias-corrected estimated true OLCs are significantly lower (in absolute value) with models using some measure of unemployment as the dependent variable. Using a bivariate MRA approach, the estimated true effects are -0.25 for the unemployment sub-sample and -0.61 for the output-sub sample; with a multivariate MRA methodology, the estimated true effects are -0.40 and -1.02 for the unemployment and the output-sub samples respectively.
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BACKGROUND: The human condition known as Premature Ovarian Failure (POF) is characterized by loss of ovarian function before the age of 40. A majority of POF cases are sporadic, but 10-15% are familial, suggesting a genetic origin of the disease. Although several causal mutations have been identified, the etiology of POF is still unknown for about 90% of the patients.¦METHODOLOGY/PRINCIPAL FINDINGS: We report a genome-wide linkage and homozygosity analysis in one large consanguineous Middle-Eastern POF-affected family presenting an autosomal recessive pattern of inheritance. We identified two regions with a LOD(max) of 3.26 on chromosome 7p21.1-15.3 and 7q21.3-22.2, which are supported as candidate regions by homozygosity mapping. Sequencing of the coding exons and known regulatory sequences of three candidate genes (DLX5, DLX6 and DSS1) included within the largest region did not reveal any causal mutations.¦CONCLUSIONS/SIGNIFICANCE: We detect two novel POF-associated loci on human chromosome 7, opening the way to the identification of new genes involved in the control of ovarian development and function.
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The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.
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When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.
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Bietti crystalline corneoretinal dystrophy (BCD) is an autosomal recessive retinal degeneration characterized by multiple glistening intraretinal dots scattered over the fundus, degeneration of the retina, and sclerosis of the choroidal vessels, ultimately resulting in progressive night blindness and constriction of the visual field. Although BCD has been associated with abnormalities in fatty-acid metabolism and absence of fatty-acid binding by two cytosolic proteins, the genetic basis of BCD is unknown. We report linkage of the BCD locus to D4S426 (maximum LOD score [Z(max)] 4.81; recombination fraction [straight theta] 0), D4S2688 (Zmax=3.97; straight theta=0), and D4S2299 (Zmax=5.31; straight theta=0), on chromosome 4q35-4qtel. Multipoint analysis confirmed linkage to the region telomeric of D4S1652 with a Z(max) of 5.3 located 4 cM telomeric of marker D4S2930.
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This study presents a classification criteria for two-class Cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland, law enforcement authorities regularly ask laboratories to determine cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. In this study, the classification analysis is based on data obtained from the relative proportion of three major leaf compounds measured by gas-chromatography interfaced with mass spectrometry (GC-MS). The aim is to discriminate between drug type (illegal) and fiber type (legal) cannabis at an early stage of the growth. A Bayesian procedure is proposed: a Bayes factor is computed and classification is performed on the basis of the decision maker specifications (i.e. prior probability distributions on cannabis type and consequences of classification measured by losses). Classification rates are computed with two statistical models and results are compared. Sensitivity analysis is then performed to analyze the robustness of classification criteria.
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Independent regulatory agencies are one of the main institutional features of the 'rising regulatory state' in Western Europe. Governments are increasingly willing to abandon their regulatory competencies and to delegate them to specialized institutions that are at least partially beyond their control. This article examines the empirical consistency of one particular explanation of this phenomenon, namely the credibility hypothesis, claiming that governments delegate powers so as to enhance the credibility of their policies. Three observable implications are derived from the general hypothesis, linking credibility and delegation to veto players, complexity and interdependence. An independence index is developed to measure agency independence, which is then used in a multivariate analysis where the impact of credibility concerns on delegation is tested. The analysis relies on an original data set comprising independence scores for thirty-three regulators. Results show that the credibility hypothesis can explain a good deal of the variation in delegation. The economic nature of regulation is a strong determinant of agency independence, but is mediated by national institutions in the form of veto players.
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Elderly individuals display a rapid age-related increase in intraindividual variability (IIV) of their performances. This phenomenon could reflect subtle changes in frontal lobe integrity. However, structural studies in this field are still missing. To address this issue, we computed an IIV index for a simple reaction time (RT) task and performed magnetic resonance imaging (MRI) including voxel based morphometry (VBM) and the tract based spatial statistics (TBSS) analysis of diffusion tensor imaging (DTI) in 61 adults aged from 22 to 88 years. The age-related IIV increase was associated with decreased fractional anisotropy (FA) as well as increased radial (RD) and mean (MD) diffusion in the main white matter (WM) fiber tracts. In contrast, axial diffusion (AD) and grey matter (GM) densities did not show any significant correlation with IIV. In multivariate models, only FA has an age-independent effect on IIV. These results revealed that WM but not GM changes partly mediated the age-related increase of IIV. They also revealed that the association between WM and IIV could not be only attributed to the damage of frontal lobe circuits but concerned the majority of interhemispheric and intrahemispheric corticocortical connections.