Objectives/Hypothesis The pathophysiology underlying human olfactory disorders is poorly understood because biopsying the olfactory epithelium (OE) can be unrepresentative and extensive immunohistochemical analysis is lacking. Autopsy tissue enriches our grasp of normal and abnormal olfactory immunohistology and guides the sampling of the OE by biopsy. Furthermore, a comparison of the molecular phenotype of olfactory epithelial cells between rodents and humans will improve our ability to correlate human histopathology with olfactory dysfunction. Study Design An immunohistochemical analysis of human olfactory tissue using a comprehensive battery of proven antibodies. Methods Human olfactory mucosa obtained from 21 autopsy specimens was analyzed with immunohistochemistry. The position and extent of olfactory mucosa was assayed by staining whole mounts with neuronal markers. Sections of the OE were analyzed with an extensive group of antibodies directed against cytoskeletal proteins and transcription factors, as were surgical specimens from an esthesioneuroblastoma. Results Neuron-rich epithelium is always found inferior to the cribriform plate, even at advanced age, despite the interruptions in the neuroepithelial sheet caused by patchy respiratory metaplasia. The pattern of immunostaining with our antibody panel identifies two distinct types of basal cell progenitors in human OE similar to rodents. The panel also clarifies the complex composition of the esthesioneuroblastoma. Conclusion The extent of human olfactory mucosa at autopsy can easily be delineated as a function of age and neurological disease. The similarities in human vs. rodent OE will enable us to translate knowledge from experimental animals to humans and will extend our understanding of human olfactory pathophysiology.

In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.INDEX TERMS Cluster control, virtual core algorithm, cluster reconstruction, track planning, improved APF

Aspect-based sentiment analysis aims to predict sentiment polarity for every aspect in a sentence review. Most existing approaches are based on the sequence models, which may superimpose the emotional semantics of different tendencies and lack syntactic structure information. And most models adopt coarse-grained attention mechanism which still face the issues of weakness interaction between aspect and context. In this paper, we propose a transformer based multi-grained attention network (T-MGAN), which utilizes the Transformer module to learn the word-level representations of aspects and context respectively, and further utilizes the Tree Transformer module to obtain the phrase-level representations of contexts. It is capable of extracting the syntactic structure features and syntax information of aspect and context. In addition, we adopt dual-pooling method and multi-grained attention network to extract high quality aspect-context interactive representations. We evaluate the proposed model on three datasets and prove the effectiveness of the proposed model. INDEX TERMS Aspect-based sentiment analysis, transformer, tree transformer, attention mechanism, nature language processing.

Spray field of the shower nozzle has its own unique characteristics, which are instantaneity and speediness, gas-liquid two-phase nature and complexity of multiple jets. Using the traditional artificial observational method to detect the water-saving performance can not get high precision and can only extract limited characteristics. A method of image acquisition and analysis technology is described in this article, which can provide foundation for testing water-saving performance of the nozzle. By the image analysis method, it can obtain both the flow characteristic of the jet in the spray field and the relationships of pressure, flow rate and spray angle.