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Primary myelofibrosis (PMF) is a Ph‐negative myeloproliferative neoplasm (MPN), characterized by advanced bone marrow fibrosis and extramedullary haematopoiesis. The bone marrow fibrosis results from excessive proliferation of fibroblasts that are influenced by several cytokines in the microenvironment, of which transforming growth factor‐β (TGF‐β) is the most important. Micromechanics related to the niche has not yet been elucidated. In this study, we hypothesized that mechanical stress modulates TGF‐β signalling leading to further activation and subsequent proliferation and invasion of bone marrow fibroblasts, thus showing the important role of micromechanics in the development and progression of PMF, both in the bone marrow and in extramedullary sites. Using three PMF‐derived fibroblast cell lines and transforming growth factor‐β receptor (TGFBR) 1 and 2 knock‐down PMF‐derived fibroblasts, we showed that mechanical stress does stimulate the collagen synthesis by the fibroblasts in patients with myelofibrosis, through the TGFBR1, which however seems to be activated through alternative pathways, other than TGFBR2. [ABSTRACT FROM AUTHOR]

This study proposes two control techniques for a buck converter operating in continuous conduction mode at a fixed switching frequency. The non-linear behaviour of this switched system is represented by a discrete piecewise affine (PWA) model. The PWA representation offers a precise approximation of the converter's dynamics in the whole operating domain and also allows the investigation of system's stability and the design of different control laws. The first control approach corresponds to a piecewise linear (PWL) state-feedback controller, designed by using a piecewise quadratic Lyapunov function and by solving a set of linear matrix inequalities. This control method guarantees the stability of the closed-loop system for a wide range of operating points. The second control strategy is a model predictive control. The constrained optimal control problem is formulated and solved using the PWA approximation as a prediction model. The explicit form of the control law is derived off-line as an affine state-feedback controller and stored in a look-up table for implementation. Both PWL and PWA controllers are validated experimentally, showing better performances in comparison with a proportional-integral or constant state-feedback controller. [ABSTRACT FROM AUTHOR]