Feller Processes: The Next Generation in Modeling. Brownian Motion, L\'evy Processes and Beyond

We present a simple construction method for Feller processes and a framework for the generation of sample paths of Feller processes. The construction is based on state space dependent mixing of L\'evy processes. Brownian Motion is one of the most frequently used continuous time Markov processes in applications. In recent years also L\'evy processes, of which Brownian Motion is a special case, have become increasingly popular. L\'evy processes are spatially homogeneous, but empirical data often suggest the use of spatially inhomogeneous processes. Thus it seems necessary to go to the next level of generalization: Feller processes. These include L\'evy processes and in particular Brownian motion as special cases but allow spatial inhomogeneities. Many properties of Feller processes are known, but proving the very existence is, in general, very technical. Moreover, an applicable framework for the generation of sample paths of a Feller process was missing. We explain, with practitioners in mind, how to overcome both of these obstacles. In particular our simulation technique allows to apply Monte Carlo methods to Feller processes.Comment: 22 pages, including 4 figures and 8 pages of source code for the generation of sample paths of Feller processe

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases

Mortality following Stroke, the Weekend Effect and Related Factors: Record Linkage Study

Increased mortality following hospitalisation for stroke has been reported from many but not all studies that have investigated a 'weekend effect' for stroke. However, it is not known whether the weekend effect is affected by factors including hospital size, season and patient distance from hospital.To assess changes over time in mortality following hospitalisation for stroke and how any increased mortality for admissions on weekends is related to factors including the size of the hospital, seasonal factors and distance from hospital.A population study using person linked inpatient, mortality and primary care data for stroke from 2004 to 2012. The outcome measures were, firstly, mortality at seven days and secondly, mortality at 30 days and one year.Overall mortality for 37 888 people hospitalised following stroke was 11.6% at seven days, 21.4% at 30 days and 37.7% at one year. Mortality at seven and 30 days fell significantly by 1.7% and 3.1% per annum respectively from 2004 to 2012. When compared with week days, mortality at seven days was increased significantly by 19% for admissions on weekends, although the admission rate was 21% lower on weekends. Although not significant, there were indications of increased mortality at seven days for weekend admissions during winter months (31%), in community (81%) rather than large hospitals (8%) and for patients resident furthest from hospital (32% for distances of >20 kilometres). The weekend effect was significantly increased (by 39%) for strokes of 'unspecified' subtype.Mortality following stroke has fallen over time. Mortality was increased for admissions at weekends, when compared with normal week days, but may be influenced by a higher stroke severity threshold for admission on weekends. Other than for unspecified strokes, we found no significant variation in the weekend effect for hospital size, season and distance from hospital

High Cleavage Efficiency of a 2A Peptide Derived from Porcine Teschovirus-1 in Human Cell Lines, Zebrafish and Mice

When expression of more than one gene is required in cells, bicistronic or multicistronic expression vectors have been used. Among various strategies employed to construct bicistronic or multicistronic vectors, an internal ribosomal entry site (IRES) has been widely used. Due to the large size and difference in expression levels between genes before and after IRES, however, a new strategy was required to replace IRES. A self-cleaving 2A peptide could be a good candidate to replace IRES because of its small size and high cleavage efficiency between genes upstream and downstream of the 2A peptide. Despite the advantages of the 2A peptides, its use is not widespread because (i) there are no publicly available cloning vectors harboring a 2A peptide gene and (ii) comprehensive comparison of cleavage efficiency among various 2A peptides reported to date has not been performed in different contexts. Here, we generated four expression plasmids each harboring different 2A peptides derived from the foot-and-mouth disease virus, equine rhinitis A virus, Thosea asigna virus and porcine teschovirus-1, respectively, and evaluated their cleavage efficiency in three commonly used human cell lines, zebrafish embryos and adult mice. Western blotting and confocal microscopic analyses revealed that among the four 2As, the one derived from porcine teschovirus-1 (P2A) has the highest cleavage efficiency in all the contexts examined. We anticipate that the 2A-harboring cloning vectors we generated and the highest efficiency of the P2A peptide we demonstrated would help biomedical researchers easily adopt the 2A technology when bicistronic or multicistronic expression is required

Multislice CT angiography in the selection of patients with ruptured intracranial aneurysms suitable for clipping or coiling

Introduction We sought to establish whether CT angiography (CTA) can be applied to the planning and performance of clipping or coiling in ruptured intracranial aneurysms without recourse to intraarterial digital subtraction angiography (IA-DSA). Methods Over the period April 2003 to January 2006 in all patients presenting with a subarachnoid haemorrhage CTA was performed primarily. If CTA demonstrated an aneurysm, coiling or clipping was undertaken. IA-DSA was limited to patients with negative or inconclusive CTA findings. We compared CTA images with findings at surgery or coiling in patients with positive CTA findings and in patients with negative and inconclusive findings in whom IA-DSA had been performed. Results In this study, 224 consecutive patients (mean age 52.7 years, 135 women) were included. In 133 patients (59%) CTA demonstrated an aneurysm, and CTA was followed directly by neurosurgical (n=55) or endovascular treatment (n=78). In 31 patients (14%) CTA findings were categorized as inconclusive, and in 60 (27%) CTA findings were negative. One patient received surgical treatment on the basis of false-positive CTA findings. In 17 patients in whom CTA findings were inconclusive, IA-DSA provided further diagnostic information required for correct patient selection for any therapy. Five ruptured aneurysms in patients with a nonperimesencephalic SAH were negative on CTA, and four of these were also false-negative on IA-DSA. On a patient basis the positive predictive value, negative predictive value, sensitivity, specificity and accuracy of CTA for symptomatic aneurysms were 99%, 90%, 96%, 98% and 96%, respectively. Conclusion CTA should be used as the first diagnostic modality in the selection of patients for surgical or endovascular treatment of ruptured intracranial aneurysms. If CTA renders inconclusive results, IA-DSA should be performed. With negative CTA results the complementary value of IA-DSA is marginal. IA-DSA is not needed in patients with negative CTA and classic perimesencephalic SAH. Repeat IA-DSA or CTA should still be performed in patients with a nonperimesencephalic SAH

Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy

The detection of epistatic interactive effects of multiple genetic variants on the susceptibility of human complex diseases is a great challenge in genome-wide association studies (GWAS). Although methods have been proposed to identify such interactions, the lack of an explicit definition of epistatic effects, together with computational difficulties, makes the development of new methods indispensable. In this paper, we introduce epistatic modules to describe epistatic interactive effects of multiple loci on diseases. On the basis of this notion, we put forward a Bayesian marker partition model to explain observed case-control data, and we develop a Gibbs sampling strategy to facilitate the detection of epistatic modules. Comparisons of the proposed approach with three existing methods on seven simulated disease models demonstrate the superior performance of our approach. When applied to a genome-wide case-control data set for Age-related Macular Degeneration (AMD), the proposed approach successfully identifies two known susceptible loci and suggests that a combination of two other loci—one in the gene SGCD and the other in SCAPER—is associated with the disease. Further functional analysis supports the speculation that the interaction of these two genetic variants may be responsible for the susceptibility of AMD. When applied to a genome-wide case-control data set for Parkinson's disease, the proposed method identifies seven suspicious loci that may contribute independently to the disease

Imaging and Modeling of Myocardial Metabolism

Current imaging methods have focused on evaluation of myocardial anatomy and function. However, since myocardial metabolism and function are interrelated, metabolic myocardial imaging techniques, such as positron emission tomography, single photon emission tomography, and magnetic resonance spectroscopy present novel opportunities for probing myocardial pathology and developing new therapeutic approaches. Potential clinical applications of metabolic imaging include hypertensive and ischemic heart disease, heart failure, cardiac transplantation, as well as cardiomyopathies. Furthermore, response to therapeutic intervention can be monitored using metabolic imaging. Analysis of metabolic data in the past has been limited, focusing primarily on isolated metabolites. Models of myocardial metabolism, however, such as the oxygen transport and cellular energetics model and constraint-based metabolic network modeling, offer opportunities for evaluation interactions between greater numbers of metabolites in the heart. In this review, the roles of metabolic myocardial imaging and analysis of metabolic data using modeling methods for expanding our understanding of cardiac pathology are discussed

124I-HuCC49deltaCH2 for TAG-72 antigen-directed positron emission tomography (PET) imaging of LS174T colon adenocarcinoma tumor implants in xenograft mice: preliminary results

<p>Abstract</p> <p>Background</p> <p>¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) is widely used in diagnostic cancer imaging. However, the use of ¹⁸F-FDG in PET-based imaging is limited by its specificity and sensitivity. In contrast, anti-TAG (tumor associated glycoprotein)-72 monoclonal antibodies are highly specific for binding to a variety of adenocarcinomas, including colorectal cancer. The aim of this preliminary study was to evaluate a complimentary determining region (CDR)-grafted humanized C_H2-domain-deleted anti-TAG-72 monoclonal antibody (HuCC49deltaC_H2), radiolabeled with iodine-124 (¹²⁴I), as an antigen-directed and cancer-specific targeting agent for PET-based imaging.</p> <p>Methods</p> <p>HuCC49deltaC_H2 was radiolabeled with ¹²⁴I. Subcutaneous tumor implants of LS174T colon adenocarcinoma cells, which express TAG-72 antigen, were grown on athymic Nu/Nu nude mice as the xenograft model. Intravascular (i.v.) and intraperitoneal (i.p.) administration of ¹²⁴I-HuCC49deltaC_H2 was then evaluated in this xenograft mouse model at various time points from approximately 1 hour to 24 hours after injection using microPET imaging. This was compared to i.v. injection of ¹⁸F-FDG in the same xenograft mouse model using microPET imaging at 50 minutes after injection.</p> <p>Results</p> <p>At approximately 1 hour after i.v. injection, ¹²⁴I-HuCC49deltaC_H2 was distributed within the systemic circulation, while at approximately 1 hour after i.p. injection, ¹²⁴I-HuCC49deltaC_H2 was distributed within the peritoneal cavity. At time points from 18 hours to 24 hours after i.v. and i.p. injection, ¹²⁴I-HuCC49deltaC_H2 demonstrated a significantly increased level of specific localization to LS174T tumor implants (p = 0.001) when compared to the 1 hour images. In contrast, approximately 50 minutes after i.v. injection, ¹⁸F-FDG failed to demonstrate any increased level of specific localization to a LS174T tumor implant, but showed the propensity toward more nonspecific uptake within the heart, Harderian glands of the bony orbits of the eyes, brown fat of the posterior neck, kidneys, and bladder.</p> <p>Conclusions</p> <p>On microPET imaging, ¹²⁴I-HuCC49deltaC_H2 demonstrates an increased level of specific localization to tumor implants of LS174T colon adenocarcinoma cells in the xenograft mouse model on delayed imaging, while ¹⁸F-FDG failed to demonstrate this. The antigen-directed and cancer-specific ¹²⁴I-radiolabled anti-TAG-72 monoclonal antibody conjugate, ¹²⁴I-HuCC49deltaC_H2, holds future potential for use in human clinical trials for preoperative, intraoperative, and postoperative PET-based imaging strategies, including fused-modality PET-based imaging platforms.</p

Genetic Signatures of Exceptional Longevity in Humans

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures” of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity