Abstract-Most existing work on sensor networks concentrates on finding efficient ways to forward data from the information source to the data centers, and not much work has been done on collecting local data and generating the data report. This paper studies this issue by proposing techniques to detect and track a mobile target. We introduce the concept of dynamic convoy tree-based collaboration, and formalize it as a multiple objective optimization problem which needs to find a convoy tree sequence with high tree coverage and low energy consumption. We propose an optimal solution which achieves 100% coverage and minimizes the energy consumption under certain ideal situations. Considering the real constraints of a sensor network, we propose several practical implementations: the conservative scheme and the prediction-based scheme for tree expansion and pruning; the sequential and the localized reconfiguration schemes for tree reconfiguration. Extensive experiments are conducted to compare the practical implementations and the optimal solution. The results show that the prediction-based scheme outperforms the conservative scheme and it can achieve similar coverage and energy consumption to the optimal solution. The experiments also show that the localized reconfiguration scheme outperforms the sequential reconfiguration scheme when the node density is high, and the trend is reversed when the node density is low.
Three-dimensional printing (3Dp) is being increasingly used in medical education. Although the use of such lifelike models is beneficial, well-powered, randomized studies supporting this statement are scarce. Two spinal fracture simulation models were generated by 3Dp. Altogether, 120 medical students (54.2% females) were randomized into three teaching module groups [two-dimensional computed tomography images (CT), 3D, or 3Dp] and asked to answer 10 key anatomical and 4 evaluative questions. Students in the 3Dp or 3D group performed significantly better than those in the CT group, although males in the 3D group scored higher than females. Students in the 3Dp group were the first to answer all questions, and there were no sex-related differences. Pleasure, assistance, effect, and confidence were more predominant in students in the 3Dp group than in those in the 3D and CT groups. This randomized study revealed that the 3Dp model markedly improved the identification of complex spinal fracture anatomy by medical students and was equally appreciated and comprehended by both sexes. Therefore, the lifelike fracture model made by 3Dp technology should be used as a means of premedical education.
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Although many researchers have attempted to explain the origins of AD, developing an effective strategy in AD clinical therapy is difficult. Recent studies have revealed a potential link between AD and circRNA-associated-ceRNA networks. However, few genome-wide studies have identified the potential circRNA-associated-ceRNA pairs involved in AD. In this study, we systematically explored the circRNA-associated-ceRNA mechanism in a 7-month-old senescence-accelerated mouse prone 8 (SAMP8) model brain through deep RNA sequencing. We obtained 235 significantly dysregulated circRNA transcripts, 30 significantly dysregulated miRNAs, and 1,202 significantly dysregulated mRNAs. We then constructed the most comprehensive circRNA-associated-ceRNA networks in SAMP8 brain. GO analysis revealed that these networks were involved in regulating the development of AD from various angles, for instance, axon terminus (GO: 0043679) and synapse (GO: 0045202). Following rigorous selection, we discovered that the circRNA-associated-ceRNA networks in this AD mouse model were mainly involved in the regulation of Aβ clearance (Hmgb2) and myelin function (Dio2). This research is the first to provide a systematic dissection of circRNA-associated-ceRNA profiling in SAMP8 mouse brain. The selected circRNA-associated-ceRNA networks can profoundly affect the diagnosis and therapy of AD in the future.
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