8-Prenylnaringenin (8-PN) is a phytoestrogen with the highest estrogenic activity. The objective of the present study was to confirm the superiority of 8-PN on bone metabolisms and the estrogen receptor (ER) subtype mediating effects of 8-PN. The osteoblast MC3T3-E1 and osteoclast-like cell line RAW264.7 were treated with 17β-estradiol (10−8 mol/L), genistein (10−5 mol/L), daidzein (10−5 mol/L), 8-PN (10−5 mol/L) alone or in the presence of ERα antagonist MPP (10−7 mol/L) and ERβ antagonist PTHPP (1.5 × 10−7 mol/L). It has been found that 8-PN did not affect osteoblast proliferation, and that 8-PN increased alkaline phosphatase (ALP) activity, osteocalcin (OCN) concentrations, and the mineralized nodules. 8-PN inhibited RAW264.7 differentiating into osteoclasts and reduced the pit area of bone resorption. 8-PN could also inhibit the protein and mRNA expression of receptor activator of nuclear factor-κB ligand (RANKL) in osteoblasts, and conversely promote the expression of osteoprotegerin (OPG). These effects of 8-PN were mainly inhibited not by PTHPP but by MPP and they were weaker than estrogen's effects but stronger than those of genistein and daidzein. In conclusion, the effects of 8-PN on promoting osteoblastic bone formation and inhibiting osteoclastic bone resorption were mediated by ERα instead of ERβ and the efficacy was more potent than that of the two classic phytoestrogens: genistein and daidzein.

The threat to people’s lives and property posed by fires has become increasingly serious. To address the problem of a high false alarm rate in traditional fire detection, an innovative detection method based on multifeature fusion of flame is proposed. First, we combined the motion detection and color detection of the flame as the fire preprocessing stage. This method saves a lot of computation time in screening the fire candidate pixels. Second, although the flame is irregular, it has a certain similarity in the sequence of the image. According to this feature, a novel algorithm of flame centroid stabilization based on spatiotemporal relation is proposed, and we calculated the centroid of the flame region of each frame of the image and added the temporal information to obtain the spatiotemporal information of the flame centroid. Then, we extracted features including spatial variability, shape variability, and area variability of the flame to improve the accuracy of recognition. Finally, we used support vector machine for training, completed the analysis of candidate fire images, and achieved automatic fire monitoring. Experimental results showed that the proposed method could improve the accuracy and reduce the false alarm rate compared with a state-of-the-art technique. The method can be applied to real-time camera monitoring systems, such as home security, forest fire alarms, and commercial monitoring.

The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases.

Feature extractor and classifier are two major components in a brain-computer interface system, in which the feature extractor plays a critical role. To increase the discriminability of features or feature vectors used for classification, it is necessary to select a suitable number of task-related data recording channels. In this article, a machine-learning algorithm is proposed for optimizing feature extraction in an electrocorticogram-based brain-computer interface. Common spatial pattern was used for feature extraction, and channel selection was performed by genetic algorithm for optimizing the feature extraction. Fisher discriminant analysis was used as classifier, and the channel subset chosen at each generation was evaluated by classification accuracy. The algorithm was applied to three electrocorticogram datasets that were recorded during two kinds of motor imagery tasks. The results suggest that the channel number used for building a brain-computer interface system could be significantly decreased without losing classification accuracy, and the accuracy rate could be noticeably improved by using the optimal channel subsets chosen by genetic algorithm.

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Current methods of human activity recognition face many challenges, such as the need for multiple sensors, poor implementation, unreliable real-time performance, and lack of temporal location. In this research, we developed a method for recognizing and locating human activities based on temporal action recognition. For this work, we used a multilayer convolutional neural network (CNN) to extract features. In addition, we used refined actionness grouping to generate precise region proposals. Then, we classified the candidate regions by employing an activity classifier based on a structured segmented network and a cascade design for end-to-end training. Compared with previous methods of action classification, the proposed method adds the time boundary and effectively improves the detection accuracy. To test this method empirically, we conducted experiments utilizing surveillance video of an offshore oil production plant. Three activities were recognized and located in the untrimmed long video: standing, walking, and falling. The accuracy of the results proved the effectiveness and real-time performance of the proposed method, demonstrating that this approach has great potential for practical application.

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