Purpose: The movement of the jaw during speech and chewing has frequently been studied by tracking surface landmarks on the chin. However, the extent to which chin motions accurately represent those of the underlying mandible remains in question. In this investigation, the movements of a pellet attached to the incisor of the mandible were compared with those of pellets attached to different regions of the chin.
Method: Ten healthy talkers served as participants. Three speaking contexts were recorded from each participant: word, sentence, and paragraph. Chin position errors were estimated by computing the standard distance between the mandibular incisor pellet and the chin pellets.
Results: Relative to the underlying mandible, chin pellets moved with an average absolute and relative error of 0.81 mm and 7.30%, respectively. The movements of chin and mandibular pellets were tightly coupled in time.
Conclusion: The chin tracking errors observed in this investigation are considered acceptable for descriptive studies of oromotor behavior, particularly in situations where mandibular placements are not practical (e.g., young children or edentulous adults). The observed amount of error, however, may not be tolerable for fine-grained analyses of mandibular biomechanics. Several guidelines are provided for minimizing error associated with tracking surface landmarks on the chin.
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http://dx.doi.org/10.1044/1092-4388(2007/066) | DOI Listing |
Epilepsia
January 2025
National Center for Epilepsy, Division of Clinical Neuroscience, full member of European Reference Network EpiCARE, Oslo University Hospital, Oslo, Norway.
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Methods: The study platform is the Norwegian Mother, Father, and Child Cohort Study (MoBa). Epilepsy cases were identified through registry linkages facilitated by Norway's universal health care system and mandatory reporting to the Norwegian Patient Registry.
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Hansjörg Wyss Department of Plastic Surgery, New York University Langone Health, New York, NY 10016, USA.
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Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.
This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.
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Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human-computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps.
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Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
We conducted a phase I trial to determine the optimal dose of triplet therapy with the tyrosine kinase inhibitor sitravatinib plus nivolumab plus ipilimumab in 22 previously untreated patients with advanced clear cell renal cell carcinoma. The primary endpoint was safety. Secondary endpoints were objective response rate (ORR), disease control rate (DCR), duration of response (DOR), progression-free survival (PFS), overall survival (OS), 1-year survival probability, and sitravatinib pharmacokinetics.
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