Bone senses and adapts to meet mechanical needs by means of an extensive mechanotransduction network comprising osteocytes (former osteoblasts entrapped in mineral) and their cytoplasmic projections through which osteocytes communicate with osteoblasts and osteoclasts on the bone surface. Mechanical stimulation promotes osteocyte (and osteoblast) survival by activating the extracellular signal-regulated kinases, ERKs. Estrogens have similar effects and, intriguingly, the adaptive response of bone to mechanical forces is defective in mice lacking estrogen receptor (ER) ␣ or ER␤. We report that ERKs are not activated by stretching in osteocytic and osteoblastic cells in which both ER␣ and ER␤ have been knocked out or knocked down and this is reversed partially by transfection of either one of the two human ERs and fully by transfection of both receptors. ERK activation in response to stretching is also recovered by transfecting the ligand-binding domain (E) of either receptor or an ER␣ mutant that does not bind estrogens. Furthermore, mechano-responsiveness is restored by transfecting the E␣ targeted to the plasma membrane, but not to the nucleus, whereas ER␣ mutants with impaired plasma membrane localization or binding to caveolin-1 fail to confer ERK activation in response to stretching. Lastly, the ER antagonist ICI 182,780 abrogates ERK activation and the anti-apoptotic effect of mechanical stimulation. We conclude that in addition to their role as ligand-dependent mediators of the effects of estrogens, the ERs participate in the transduction of mechanical forces into pro-survival signaling in bone cells, albeit in a ligand-independent manner.That the skeleton adapts to meet mechanical needs was first recognized by Wolff (1) and later expanded by Frost (2) in the mechanostat hypothesis. Bone adjusts to load by changing its mass, shape, or microarchitecture (3, 4), and it responds differently depending on the magnitude of strain. Whereas insufficient or excessive levels of strain induce bone resorption, physiological levels of strain maintain bone mass (5). Osteocytes (former osteoblasts buried in the mineral) are thought to be the cells acting as mechanosensors. Osteoblasts and osteoclasts, the executive cells for bone formation and resorption, are present on bone for relatively short periods and occur in low number and only in locations that undergo remodeling at a given time point, which represent ϳ10% of the bone surface. On the other hand, osteocytes are by far the most abundant resident cells and are present throughout the entire bone tissue. Importantly, osteocytes are the core of a functional syncytium that extends from the mineralized bone matrix to the bone surface and the bone marrow, all the way to the blood vessels. This strategic location permits the detection of variations in the level of strain as well as the dispersion of the signals leading to adaptive responses. Through such network, osteocytes might continually compare present mechanical strains to usual levels of strain (the "set point" of ...

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This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on principal component analysis and linear discriminant analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a feed-forward neural network and one-class support vector machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.