Publications by authors named "Huikai Liu"

The submandibular gland (SMG) and sublingual gland (SLG) are two of three major salivary glands in mammals and comprise serous and mucous acinar cells. The two glands share some functional properties, which are largely dependent on the types of acinar cells. In recent years, while ScRNA-seq (single-cell sequencing) with a 10 × platform has been used to explore molecular markers in salivary glands, few studies have examined the acinar heterogeneity and unique molecular markers between SMG and SLG.

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Introduction: Oral microbial homeostasis is a key factor affecting oral health, and saliva plays a significant role in maintaining oral microbial homeostasis. The submandibular gland (SMG) and sublingual gland (SLG) together produce the most saliva at rest. Organic ingredients, including antimicrobial proteins, are rich and distinctive and depend on the type of acinar cells in the SMG and SLG.

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Saliva is crucial for lubricating the mouth and aiding in food digestion. However, the occurrence of oral dysfunction, such as xerostomia, dysphagia or oral infection can markedly reduce the quality of life of affected individuals. The major salivary glands include the submandibular gland (SMG), and sublingual and parotid glands; they are the larger glands in mammals that produce the majority of the saliva.

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The submandibular gland (SMG) and the sublingual gland (SLG) are two of the three major salivary glands in mammals. In mice, they are adjacent to each other and open into the oral cavity, producing saliva to lubricate the mouth and aid in food digestion. Though salivary gland dysfunction accompanied with fibrosis and metabolic disturbance is common in clinic, in-depth mechanistic research is lacking.

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Egocentric activity recognition in first-person video (FPV) requires fine-grained matching of the camera wearer's action and the objects being operated. The traditional method used for third-person action recognition does not suffice because of (1) the background ego-noise introduced by the unstructured movement of the wearable devices caused by body movement; (2) the small-sized and fine-grained objects with single scale in FPV. Size compensation is performed to augment the data.

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Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation.

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