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The blockchain runs in a complex topological network which is affected by the principle of consensus, and data storage between nodes needs to maintain global consistency in the entire network, which causes the data storage inefficient. At the same time, the information exchange between large-scale communication node groups leads to the problems of bandwidth expropriation and excessive network load. In response to these problems, this article proposes a second-degree branch structure blockchain expansion model. First, a ternary storage structure is established. Data use the way of fully integrated storage, multi-cell storage, and fully split storage, and data are classified and stored in parallel between the structures. Second, a second-degree branch chain model is constructed. The main chain forks into multiple sub-chains, and a free competition chain structure and a Z-type chain structure are defined; a two-way rotation mechanism is introduced to realize the integration and transition between chain structures. Finally, a set of malicious attacks is simulated to realize the security constraints of the blockchain, to verify the security of the second-degree branch chain model. Experiment shows that the second-degree branch structure expansion model proposed in this article has great advantages in data storage efficiency and network load.

Tracheobronchopathia osteochondroplastica (TO) is a rare disease that may cause unexpected difficult intubation. There is no available consensus on the management of difficult intubation that is associated with TO. A 45-year-old woman was scheduled for modified radical mastoidectomy, canaloplasty, and tympanoplasty under general anesthesia. We encountered significant resistance during tracheal intubation, although the laryngeal view was normal with the video laryngoscope. A fiberoptic bronchoscope was then used to facilitate intubation, and we noted that the trachea was obviously narrowed due to cartilaginous ring hypertrophy. The tracheal tube was fully lubricated with tetracaine gel, and smoothly inserted into the trachea. After the operation, bronchoscopy and a computed tomography (CT) scan were performed to confirm the diagnosis of TO. Fiberoptic bronchoscopy-assisted tracheal intubation is safe and effective choice for the patients in whom subglottic intubation is difficult. CT scan and bronchoscopy might be helpful for preoperative airway assessment. Identifying patients with TO is important to avoid unexpected tracheal intubation impediment. Assessment of the subglottic airway should also be taken seriously.

Background: Although N6-methyladenosine (m6A) plays a very important role in different biological processes, its function in the brain has not been fully explored. Thus, we investigated the roles of the RNA demethylases Alkbh5/Fto in cerebral ischemia-reperfusion injury. Methods: We used a rat model and primary neuronal cell culture to study the role of m6A and Alkbh5/Fto in the cerebral cortex ischemic penumbra after cerebral ischemia-reperfusion injury. We used Alkbh5-shRNA and Lv-Fto ( in vitro) to regulate the expression of Alkbh5/Fto to study their regulation of m6A in the cerebral cortex and to study brain function after ischemia-reperfusion injury. Results: We found that RNA m6A levels increased consecutive to the increase of Alkbh5 expression in both the cerebral cortex of rats after middle cerebral artery occlusion, and in primary neurons after oxygen deprivation/reoxygenation. In contrast, Fto expression decreased after these perturbations. Our results suggest that knocking down Alkbh5 can aggravate neuronal damage. This is due to the demethylation of Alkbh5 and Fto, which selectively demethylate the Bcl2 transcript, preventing Bcl2 transcript degradation and enhancing Bcl2 protein expression. Conclusion: Collectively, our results demonstrate that the demethylases Alkbh5/Fto co-regulate m6A demethylation, which plays a crucial role in cerebral ischemia-reperfusion injury. The results provide novel insights into potential therapeutic mechanisms for stroke.

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events (“the coal mine disaster events”). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time.

As the technology of the Internet of Things (IoT) becomes more widely used in large-scale monitoring networks, this paper proposes an optimized obtaining strategy (OFS) for large-scale sensor monitoring networks. First, because of the large-scale features of sensor node network, this paper proposes a large-scale monitoring network area clustering optimization strategy. Second, based on the characteristics of regular changes in the sensed data in large-scale monitoring networks, this paper proposes a strategy for acquiring sensor data based on an adaptive frequency conversion. The OFS optimization strategy can prolong network lifetime, reduce the transmission bandwidth resources, and reduce average energy consumption of the cluster head and network energy consumption.