Human skeletal aging is characterized as a gradual loss of bone mass due to an excess of bone resorption not balanced by new bone formation. Using human marrow cells, we tested the hypothesis that there is an age-dependent increase in osteoclastogenesis due to intrinsic changes in regulatory factors [macrophage-colony stimulating factor (M-CSF), receptor activator of NF-κB ligand (RANKL), and osteoprotegerin (OPG)] and their receptors [c-fms and RANK]. In bone marrow cells (BMCs), c-fms (r=0.61, p=0.006) and RANK expression (r=0.59, p=0.008) were increased with age (27-82 years, n=19). In vitro generation of osteoclasts was increased with age (r=0.89, p=0.007). In enriched marrow stromal cells (MSCs), constitutive expression of RANKL was increased with age (r=0.41, p=0.049) and expression of OPG was inversely correlated with age (r=-0.43, p=0.039). Accordingly, there was an age-related increase in RANKL/OPG (r=0.56, p=0.005). These data indicate an age-related increase in human osteoclastogenesis that is associated with an intrinsic increase in expression of c-fms and RANK in osteoclast progenitors, and, in the supporting MSCs, an increase in pro-osteoclastogenic RANKL expression and a decrease in anti-osteoclastogenic OPG. These findings support the hypothesis that human marrow cells and their products can contribute to skeletal aging by increasing the generation of bone-resorbing osteoclasts. These findings help to explain underlying molecular mechanisms of progressive bone loss with advancing age in humans.

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Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments.