Estimation of the incidence for non-terminal events in presence of a terminal event and evaluation of covariate effects: Sub-distribution and marginal distributions based on copulas. An application to disease progression on a breast cancer trial dataset

In clinical studies, during follow-up several kinds of events related to disease progression may be observed. In the semi-competing risks setting, some events, such as death, may prevent the observation of disease progression, thus acting as competitor for the event of interest. Methods of analysis specific for semi-competing risks data referring to marginal distribution of the non-competing events constitute a recent area of methodological research which has received a great impulse in latest years. However in clinical applications the analysis is traditionally based on crude cumulative incidences, and inference on marginal distributions is seldom considered, even when the principal aim concerns the probability of observing disease progression and death occurred without progression is a \u201cnuisance\u201d. Aim of this work is making a comparative review of semi-parametric marginal and sub-distribution methods of analysis, with particular reference to marginal regression models based on copulas. More specifically, two structures were considered for marginal models: in the first one all parameters are time-dependent, while in the second one parameters vary with covariates but does not depend on time. Applications to breast cancer clinical trial data and to a simulated dataset are reported, to show the differences and the similarities among marginal and sub-distribution approaches. Results highlight that, when the competing event acts during the whole follow-up, the marginal approach became essential for the correct estimation of marginal incidences and covariate effects. Regression methods based on copulas are promising, however there is a need of refinements concerning model building strategies, and, of standardised software routine for the practical application of these methods

Cost and EAL based optimization for seismic reinforcement of RC structures

In this paper, a new genetic algorithm-based framework aimed at efficiently design multiple seismic retrofitting interventions is proposed. The algorithm focuses on the minimization of retrofitting intervention costs of reinforced concrete (RC) frame structures. The feasibility of each tentative solution is assessed by considering in an indirect way the expected annual loss (EAL), this evaluation is performed by referring to different limit states whose repairing costs are expressed as a percentage of reconstruction costs and evaluating the respective mean annual frequency of exceedance. As the EAL takes into account the overall structural performances, to involves both serviceability and ultimate limit states, two different seismic retrofitting techniques are considered. In particular, FRP wrapping of columns is employed to increase the ductility of RC elements managing life safety and collapse limit state demands. On the other hand, steel bracings are used to increase the global stiffness of the structure and mainly increase operational and damage limit states performances. The optimization procedure is carried out by the novel genetic algorithm-based framework developed in Matlab® that is connected to a 3D RC frame fiber-section model implemented in OpenSees. For both the retrofitting systems, the algorithm provides their position within the structure (topological optimization) and their sizing. Results will show that seismic retrofitting can be effectively designed to increase the overall structural safety by efficaciously optimizing the intervention costs

A new genetic algorithm framework based on Expected Annual Loss for optimizing seismic retrofitting in reinforced concrete frame structures

The design of seismic retrofitting for existing reinforced concrete frame structures concerns the determination of the position and the arrangement of reinforcements. Currently, this design practice is mainly based on trial-and-error attempts and engineers' experience, without a formal implementation of cost/performance optimization. Though, the implementation of this intervention is associated with significant costs, noticeable downtimes, and elevated invasiveness. This paper presents a new genetic algorithm-based framework for the optimization of two different retrofitting techniques (FRP column wrapping and concentric steel braces) that aims at minimizing costs considering indirectly the lessening of expected annual values. The feasibility of each tentative solution is controlled by the outcomes of static pushover analyses in the framework of the N2 method, achieved by a 3D fiber-section model implemented in OpenSees. Application of the framework in a realistic case study structure will show that the sustainability of retrofitting intervention is achievable by employing artificial intelligence aided structural design

A novel genetic algorithm-based optimization framework for minimizing seismic retrofitting interventions costs in existing masonry structures

The pressing necessity of enhancing the seismic safety of existing masonry structures in earthquake-prone areas has led, in recent years, the research to propose a vast amount of new retrofitting techniques. However, retrofitting interventions are generally associated with important costs. Currently, there are no formal methods to optimize these interventions thus, their design is entrusted only to engineers' intuition. This paper presents a novel optimization framework aimed at the minimization of seismic retrofitting-related costs by an optimal placement (topological optimization) of reinforced plasters in masonry structures. In the proposed framework a 3D equivalent masonry model implemented in OpenSees is handled by a genetic algorithm developed in MATLAB® routine that iterates reinforcement configurations to match the optimal solution. The feasibility of each solution is controlled by the outcomes of a seismic static equivalent analysis by controlling the safety check of masonry walls with respect to both flexural and shear collapse. It is also shown, through a case study, that the proposed approach is efficient to pinpoint optimal retrofitting configurations, significantly reducing invasiveness and downtime

The role of O-linked sugars in determining the very low density lipoprotein receptor stability or release from the cell

AbstractThe very low density lipoprotein receptor is a member of the low density lipoprotein receptor supergene family for which two isoforms have been reported, one lacking and the other containing an O-linked sugar domain. In order to gain insight into their functionality, transient and stable transformants separately overexpressing previously cloned bovine variants were analyzed. We report evidence that the variant lacking the O-linked sugar domain presented a rapid cleavage from the cell and that a large amino-terminal very low density lipoprotein receptor fragment was released into the culture medium. As only minor proteolysis was involved in the other very low density lipoprotein receptor variant, the clustered O-linked sugar domain may be responsible for blocking the access to the protease-sensitive site(s). To test this hypothesis, a mutant Chinese hamster ovary cell line, ldlD, with a reversible defect in the protein O-glycosylation, was used. The instability of the O-linked sugar-deficient very low density lipoprotein receptor on the cell surface was comparable to that induced by the proteolysis of the variant lacking the O-linked sugar domain. Moreover, our data suggest that the O-linked sugar domain may also protect the very low density lipoprotein receptor against unspecific proteolysis. Taken together, these results indicate that the presence of the O-linked sugar domain may be required for the stable expression of the very low density lipoprotein receptor on the cell surface and its absence may be required for release of the receptor to the extracellular space. The exclusive expression of the variant lacking the O-linked sugar domain in the bovine aortic endothelium opens new perspectives in the physiological significance of the very low density lipoprotein receptor