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Physical activity (PA) Web sites were assessed for their use of behavior change theories, including constructs of the health belief model, Transtheoretical Model, social cognitive theory, and the theory of reasoned action and planned behavior. An evaluation template for assessing PA Web sites was developed, and content validity and interrater reliability were demonstrated. Two independent raters evaluated 24 PA Web sites. Web sites varied widely in application of theory-based constructs, ranging from 5 to 48 on a 100-point scale. The most common intervention strategies were general information, social support, and realistic goal areas. Coverage of theory-based strategies was low, varying from 26% for social cognitive theory to 39% for health belief model. Overall, PA Web sites provided little assessment, feedback, or individually tailored assistance for users. They were unable to substantially tailor the on-line experience for users at different stages of change or different demographic characteristics.

This paper proposes a deep learning-based channel estimation method for multi-cell interference-limited massive MIMO systems, in which base stations equipped with a large number of antennas serve multiple single-antenna users. The proposed estimator employs a specially designed deep neural network (DNN) to first denoise the received signal, followed by a conventional least-squares (LS) estimation. We analytically prove that our LS-type deep channel estimator can approach minimum mean square error (MMSE) estimator performance for high-dimensional signals, while avoiding MMSE's requirement for complex channel inversions and knowledge of the channel covariance matrix. This analytical result, while asymptotic, is observed in simulations to be operational for just 64 antennas and 64 subcarriers per OFDM symbol. The proposed method also does not require any training and utilizes several orders of magnitude fewer parameters than conventional DNNs. The proposed deep channel estimator is also robust to pilot contamination and can even completely eliminate it under certain conditions. Index TermsDeep learning, channel estimation, massive MIMO, OFDM.The authors are with the University of Texas at Austin, TX, USA.

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Mechanical stimulation has been shown to affect the differentiation and development of mesenchymal tissue. In the present study, we compared the histological and histomorphometric results of tissue ingrowth into micromotion chambers that were moved at 0 cycles per day, 20 cycles once per day, and 20 cycles twice per day over 20-30 sec, for 3 weeks. In each case, a chamber having a 1 x 1 x 5 mm square-holed groove for tissue ingrowth was used. The total amplitude of motion was 0.75 mm. Histological sections from nonmoved chambers contained extensive trabecular bone, embedded in a fibrovascular stroma. Histomorphometric analysis disclosed that bone comprised a mean of 31 +/- 2% (mean +/- SEM) of the ingrown tissue. Twenty movements per day appeared to further stimulate bone ingrowth (46 +/- 5%). Extensive ingrowth of more immature woven and trabecular bone was noted in a more cellular stroma. In general, increasing the degree of micromotion to 20 movements twice per day resulted in a decreased amount of bone formation (19 +/- 7%). In several of these specimens, little or no bone could be found. These experiments have demonstrated that, for the parameters chosen in this study, a short daily period of low frequency, micromotion may facilitate bone ingrowth; however, when the same motion is delivered twice daily, bone ingrowth is depressed. Thus a "window" of externally applied strain appears to exist, which may facilitate or discourage tissue differentiation to bone.