The temporomandibular joint and its associated musculature are described in a neonate gray whale (Eschrichtius robustus) and serve as the basis for direct anatomical comparisons with the temporomandibular region in other clades of baleen whales (Mysticeti). Members of the right whale/bowhead whale clade (Balaenidae) are known to possess a synovial lower jaw joint, while members of the rorqual clade (Balaenopteridae) have a nonsynovial temporomandibular joint characterized by a highly flexible fibrocartilaginous pad and no joint capsule. In contrast, the gray whale possesses a modified temporomandibular joint (intermediate condition), with a vestigial joint cavity lacking a fibrous capsule, synovial membrane, and articular disk. In addition, the presence of a rudimentary fibrocartilaginous pad appears to be homologous to that seen in balaenopterid mysticetes. The intrinsic temporomandibular musculature in the gray whale was found to include a multibellied superficial masseter and a single-bellied deep masseter. The digastric and internal pterygoid muscles in E. robustus are enlarged relative to the condition documented in species of Balaenoptera. A relatively complex insertion of the temporalis muscle on the dentary is documented in the gray whale and the low, knob-like process on the gray whale dentary is determined to be homologous with the prominent coronoid process of rorquals. Comparison with the anatomy of the temporomandibular musculature in rorquals reveals an increased importance of alpha rotation of the dentary in the gray whale. This difference in muscular morphology and lines of muscle action is interpreted as representing adaptations for suction feeding.
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Behav Processes
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Institute of Knowledge Technology, University Complutense of Madrid, 28040 Madrid, Spain.
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School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541000, China.
Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO).
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