The tracking of a moving target in a wireless sensor network (WSN) requires exact knowledge of sensor positions. However, precise information about sensor locations is not always available. Given the observation that a series of measurements are generated in the sensors when the target moves through the network field, we propose an algorithm that exploits these measurements to simultaneously localize the detecting sensors and track the target (SLAT). The main difficulties that are encountered in this problem are the ambiguity of sensor locations, the unrestricted target moving manner, and the extremely constrained resources in WSNs. Therefore, a general state evolution model is employed to describe the dynamical system with neither prior knowledge of the target moving manner nor precise location information of the sensors. The joint posterior distribution of the parameters of interest is updated online by incorporating the incomplete and inaccurate measurements between the target and each of the sensors into a Bayesian filtering framework. A variational approach is adopted in the framework to approximate the filtering distribution, thus minimizing the intercluster communication and the error propagation. By executing the algorithm on a fully distributed cluster scheme, energy and bandwidth consumption in the network are dramatically reduced, compared with a centralized approach. Experiments on an SLAT problem validate the effectiveness of the proposed algorithm in terms of tracking accuracy, localization precision, energy consumption, and execution time.

The prime motivation of our work is to balance the inherent trade-off between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through three phases. First, a cluster-based scheme is exploited. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster, we propose a nonmyopic rule, which is based on not only the target state prediction but also its future tendency. Second, the variational filtering algorithm is capable of precise tracking even in the highly nonlinear case. Furthermore, since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved. The intercluster information exchange is thus reduced to one single Gaussian statistic, dramatically cutting down the resource consumption. Third, a binary proximity observation model is employed by the activated slave sensors to reduce the energy consumption and to minimize the intracluster communication. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art algorithms in terms of tracking accuracy, internode communication, and computation complexity.

Objective: We evaluated the prognostic value of lymph node ratio (LNR) for the survival of breast cancer patients using Bayesian inference. Methods: Data on 5,279 women with infiltrating duct and lobular carcinoma breast cancer, diagnosed from 2006–2010, was obtained from the NCI SEER Cancer Registry. A prognostic modeling framework was proposed using Bayesian inference to estimate the impact of LNR in breast cancer survival. Based on the proposed model, we then developed a web application for estimating LNR and predicting overall survival. Results: The final survival model with LNR outperformed the other models considered (C-statistic 0.71). Compared to directly measured LNR, estimated LNR slightly increased the accuracy of the prognostic model. Model diagnostics and predictive per- formance confirmed the effectiveness of Bayesian modeling and the prognostic value of the LNR in predicting breast cancer survival. Conclusion: The estimated LNR was found to have a significant predictive value for the overall survival of breast cancer patients. Significance: We used Bayesian inference to estimate LNR which was then used to predict overall survival. The models were developed from a large population-based cancer registry. We also built a user-friendly web application for individual patient survival prognosis. The diagnostic value of the LNR and the effectiveness of the proposed model were evaluated by comparisons with existing prediction models.

Various cognitive disorders have been reported for mild traumatic brain injury (mTBI) patients during the acute stage. This acute stage provides an opportunity for clinicians to optimize treatment protocols, which are based on the evaluation of brain structural connectivity. So far, most brain functional magnetic resonance imaging studies are focused on moderate to severe traumatic brain injuries (TBIs). In this study, we prospectively collected resting state data on 50 mTBI within 3 days of injury and 50 healthy volunteers and analyzed them using Amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), graph theory methods and behavior measure, to explore the dysfunctional brain regions in acute mTBI. In our study, a total of 50 patients suffering <3 days mTBI and 50 healthy subjects were tested in rs-fMRI, as well as under neuropsychological examinations including the Wechsler Intelligence Scale and Stroop Color and Word Test. The correlation analysis was conducted between graph theoretic parameters and neuropsychological results. For the mTBI group, the ReHo of the inferior temporal gyrus and the cerebellum superior are significantly lower than in the control group, and the ALFF of the left insula, the cerebellum inferior, and the middle occipital gyrus were significantly higher than in the control group, which implies the dysfunctionality usually observed in Parkinson's disease. Executive function disorder was significantly correlated with the global efficiencies of the dorsolateral superior frontal gyrus and the anterior cingulate cortex, which is consistent with the literature: the acute mTBI patients demonstrate abnormality in terms of motor speed, association, information processing speed, attention, and short-term memory function. Correlation analysis between the neuropsychological outcomes and the network efficiency for the mTBI group indicates that executive dysfunction might be caused by local brain changes. Our data support the idea that the cerebral internal network has compensatory reactions in response to sudden pathological and neurophysiological changes. In the future, multimode rs-fMRI analysis could be a valuable tool for evaluating dysfunctional brain regions after mTBI.

Objective : Predicting breast cancer survival and targeting patients at high-risk of mortality is of crucial importance. Methods : We built a Bayesian Dynamic Cox (BDCox) model for predicting 5-year overall survival in breast cancer patients using data of the SEER Cancer Registry with 12,840 women diagnosed with infiltrating duct and lobular carcinoma breast cancer from 2010-2015. Four feature selection methods were used to identify predictors and enhance parsimony: fast backward variable selection, elastic net, Bayesian Model Average (BMA), and clinical expertise. All resulting models and a baseline full model containing all features were internally validated via bootstrapping and externally validated in the Shanghai Breast Cancer Survival Study. Results : BMA outperformed other feature selection methods in both internal and external validations. The BDCox model with 12 predictors: chemotherapy, N Stage, T Stage, marital status, tumor malignancy, number of positive nodes, radiation, race, age, progesterone receptor, tumor grade, and estrogen receptor, had the best performance. Several predictors showed time-varying associations with survival that are in agreement with previous studies. Conclusion : The model developed using BDCox outperformed other prognostic models considered in our study. The internal validation results indicate that the BDCox model is capable of achieving high prediction accuracy (C-statistic: 0.802), and the external validation results showed excellent generalizability of the BDCox model (C-statistic: 0.739). Significance : We built a dynamic Bayesian model from the large population-based registry SEER for predicting 5-year breast cancer overall survival. The prediction performance of the BDCox model is significantly better than other survival models.

The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind’s direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate’s accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.