The Bulk Osteosarcoma and Osteosarcoma Stem Cell Activity of a Necroptosis-Inducing Nickel(II)–Phenanthroline Complex

We report the anti-osteosarcoma and anti-osteosarcoma stem cell (OSC) properties of a nickel(II) complex, 1 . Complex 1 displays similar potency towards bulk osteosarcoma cells and OSCs, in the micromolar range. Notably, 1 displays similar or better OSC potency than the clinically approved platinum(II) anticancer drugs cisplatin and carboplatin in two- and three-dimensional osteosarcoma cell cultures. Mechanistic studies revealed that 1 induces osteosarcoma cell death by necroptosis, an ordered form of necrosis. The nickel(II) complex, 1 triggers necrosome-dependent mitrochondrial membrane depolarisation and propidium iodide uptake. Interestingly, 1 does not evoke necroptosis by elevating intracellular reactive oxygen species (ROS) or hyperactivation of poly ADP ribose polymerase (PARP-1). ROS elevation and PARP-1 activity are traits that have been observed for established necroptosis inducers such as shikonin, TRAIL and glutamate. Thus the necroptosis pathway evoked by 1 is distinct. To the best of our knowledge, this is the first report into the anti-osteosarcoma and anti-OSC properties of a nickel complex.     

An evidential approach for detection of abnormal behaviour in the presence of unreliable sensors

We address the problem of abnormal behaviour recognition of the inhabitant of a smart home in the presence of unreliable sensors. The corner stone of this work is a two-level architecture sensor fusion based on the Transferable Belief Model (TBM). The novelty of our work lies in the way we detect both unreliable sensors and abnormal behaviour within our architecture by using a temporal analysis of conflict resulting from the fusion of sensors. Detection of abnormal behaviour is based on a prediction/observation process and the influence of the faulty sources is discarded by discounting coefficients. Our architecture is tested in a real-life setting using three heterogeneous sensors enabling the detection of impossible transitions between three possible postures: Sitting, Standing and Lying. The impact of having a faulty sensor management is also tested in the real-life experiment for posture detection.     

Automatic analysis of human posture equilibrium using empirical mode decomposition

The present study proposes a new approach for the assessment of the human balance control. This approach is based on the decomposition of the center of pressure displacement using empirical mode decomposition (EMD) that provides an effective time-frequency analysis of non-stationary signals. Twenty-eight healthy subjects performed quiet standing in four conditions—feet apart/together with respect to eyes open/closed—while recording the stabilometric signals in the anteroposterior (AP) and mediolateral (ML) directions. The EMD method decomposes each stabilometric signal into several subsignals called intrinsic mode functions (IMFs). Stabilogram-diffusion analysis technique is applied to generate the diffusion curve of each IMF signal. Each diffusion curve is modeled as a second-order system and provides representative features, such as the gain parameter. Analysis of the gain parameter shows the major effect of visual input and feet conditions on the strategy to control/stabilize the balance. Significant differences were found between young and elderly, and between women and men. In addition, the impact of feet position seems to be higher in ML direction than in AP direction.