Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Hypotheses, rationale, design, and methods for prognostic evaluation of cardiac biomarker elevation after percutaneous and surgical revascularization in the absence of manifest myocardial infarction. A comparative analysis of biomarkers and cardiac magnetic resonance. The MASS-V Trial

Background: Although the release of cardiac biomarkers after percutaneous (PCI) or surgical revascularization (CABG) is common, its prognostic significance is not known. Questions remain about the mechanisms and degree of correlation between the release, the volume of myocardial tissue loss, and the long-term significance. Delayed-enhancement of cardiac magnetic resonance (CMR) consistently quantifies areas of irreversible myocardial injury. To investigate the quantitative relationship between irreversible injury and cardiac biomarkers, we will evaluate the extent of irreversible injury in patients undergoing PCI and CABG and relate it to postprocedural modifications in cardiac biomarkers and long-term prognosis. Methods/Design: The study will include 150 patients with multivessel coronary artery disease (CAD) with left ventricle ejection fraction (LVEF) and a formal indication for CABG; 50 patients will undergo CABG with cardiopulmonary bypass (CPB); 50 patients with the same arterial and ventricular condition indicated for myocardial revascularization will undergo CABG without CPB; and another 50 patients with CAD and preserved ventricular function will undergo PCI using stents. All patients will undergo CMR before and after surgery or PCI. We will also evaluate the release of cardiac markers of necrosis immediately before and after each procedure. Primary outcome considered is overall death in a 5-year follow-up. Secondary outcomes are levels of CK-MB isoenzyme and I-Troponin in association with presence of myocardial fibrosis and systolic left ventricle dysfunction assessed by CMR. Discussion: The MASS-V Trial aims to establish reliable values for parameters of enzyme markers of myocardial necrosis in the absence of manifest myocardial infarction after mechanical interventions. The establishments of these indices have diagnostic value and clinical prognosis and therefore require relevant and different therapeutic measures. In daily practice, the inappropriate use of these necrosis markers has led to misdiagnosis and therefore wrong treatment. The appearance of a more sensitive tool such as CMR provides an unprecedented diagnostic accuracy of myocardial damage when correlated with necrosis enzyme markers. We aim to correlate laboratory data with imaging, thereby establishing more refined data on the presence or absence of irreversible myocardial injury after the procedure, either percutaneous or surgical, and this, with or without the use of cardiopulmonary bypass.

MATHEMATICAL MODELS AND POLYHEDRAL STUDIES FOR INTEGRAL SHEET METAL DESIGN

We deal with the optimization of the production of branched sheet metal products. New forming techniques for sheet metal give rise to a wide variety of possible profiles and possible ways of production. In particular, we show how the problem of producing a given profile geometry can be modeled as a discrete optimization problem. We provide a theoretical analysis of the model in order to improve its solution time. In this context we give the complete convex hull description of some substructures of the underlying polyhedron. Moreover, we introduce a new class of facet-defining inequalities that represent connectivity constraints for the profile and show how these inequalities can be separated in polynomial time. Finally, we present numerical results for various test instances, both real-world and academic examples.

Weibull and generalised exponential overdispersion models with an application to ozone air pollution

We consider the problem of estimating the mean and variance of the time between occurrences of an event of interest (inter-occurrences times) where some forms of dependence between two consecutive time intervals are allowed. Two basic density functions are taken into account. They are the Weibull and the generalised exponential density functions. In order to capture the dependence between two consecutive inter-occurrences times, we assume that either the shape and/or the scale parameters of the two density functions are given by auto-regressive models. The expressions for the mean and variance of the inter-occurrences times are presented. The models are applied to the ozone data from two regions of Mexico City. The estimation of the parameters is performed using a Bayesian point of view via Markov chain Monte Carlo (MCMC) methods.

Comparative analysis of the pathological events involved in immune and non-immune TRALI models

Background and Objectives Transfusion-related acute lung injury (TRALI) is characterized by leukocyte transmigration and alveolar capillary leakage shortly after transfusion. TRALI pathogenesis has not been fully elucidated. In some cases, the infusion of alloantibodies (immune model), whereas in others the combination of neutrophil priming by proinflammatory molecules with the subsequent infusion of biological response modifiers (BRMs) in the hemocomponent (non-immune model) have been implicated. Our aim was to compare the pathological events involved in TRALI induced by antibodies or BRMs using murine models. Materials and Methods In the immune model, human HNA-2+ neutrophils were incubated in vitro with a monoclonal antibody (anti-CD177, clone 7D8) directed against the HNA-2 antigen and injected i.v. in NOD/SCID mice. In the non-immune model, BALB/c mice were treated with low doses of lipopolysaccharide (LPS) followed by platelet-activating factor (PAF) infusion 2 h later. Forty minutes after PAF administration, or 6 h after neutrophil injection, lungs were isolated and histological analysis, determination of a variety of cytokines and chemokines including keratinocyte-derived chemokine (KC), MIP-2, the interleukins IL-1 beta, IL-6, IL-8 as well as TNFa, cell influx and alveolar capillary leakage were performed. Results In both models, characteristic histological findings of TRALI and an increase in KC and MIP-2 levels were detected. In contrast to the immune model, in the non-immune model, there was a dramatic increase in IL-1 beta and TNFa. However, capillary leakage was only detected if PAF was administrated. Conclusions Regardless of the triggering event(s), KC, MIP-2 and integrins participate in TRALI pathogenesis, whereas PAF is essential for capillary leakage when two events are involved.

Covariates of high-risk human papillomavirus (HPV) infections are distinct for incident CIN1, CIN2 and CIN3 as disclosed by competing-risks regression models

Background: In addition to the oncogenic human papillomavirus (HPV), several cofactors are needed in cervical carcinogenesis, but whether the HPV covariates associated with incident i) CIN1 are different from those of incident ii) CIN2 and iii) CIN3 needs further assessment. Objectives: To gain further insights into the true biological differences between CIN1, CIN2 and CIN3, we assessed HPV covariates associated with incident CIN1, CIN2, and CIN3. Study Design and Methods: HPV covariates associated with progression to CIN1, CIN2 and CIN3 were analysed in the combined cohort of the NIS (n = 3,187) and LAMS study (n = 12,114), using competing-risks regression models (in panel data) for baseline HR-HPV-positive women (n = 1,105), who represent a sub-cohort of all 1,865 women prospectively followed-up in these two studies. Results: Altogether, 90 (4.8%), 39 (2.1%) and 14 (1.4%) cases progressed to CIN1, CIN2, and CIN3, respectively. Among these baseline HR-HPV-positive women, the risk profiles of incident GIN I, CIN2 and CIN3 were unique in that completely different HPV covariates were associated with progression to CIN1, CIN2 and CIN3, irrespective which categories (non-progression, CIN1, CIN2, CIN3 or all) were used as competing-risks events in univariate and multivariate models. Conclusions: These data confirm our previous analysis based on multinomial regression models implicating that distinct covariates of HR-HPV are associated with progression to CIN1, CIN2 and CIN3. This emphasises true biological differences between the three grades of GIN, which revisits the concept of combining CIN2 with CIN3 or with CIN1 in histological classification or used as a common end-point, e.g., in HPV vaccine trials.