An analysis of the laminations of the masseteric, zygomaticomandibular and temporalis muscles of the Red Kangaroo (Macropus Rufus) and all of the masticatory muscles of the Eastern Gray Kangaroo (Macropus Giganteus) was carried out based on their innervation. The masseteric muscle was divided into superficial and deep layers; the superficial layer was further subdivided into three laminae from the rostro-lateral portion to caudo-internal portion. The deep layer was divided into lateral, caudo-internal and rostro-internal laminae. The zygomaticomandibular muscle which was located between the masseteric and temporal muscles was divided into lateral, internal and rostral laminae, on the basis of its innervation. The lateral and internal laminae were innervated by the nerve which arises between the masseteric nerve and the posterior deep temporal nerve. A small rostral portion of the muscle was innervated by masseteric nerves, which passed through the internal lamina of the deep layer of the masseteric muscle. The temporalis muscle was innervated by an anterior deep temporal nerve and posterior deep temporal nerve. Only the most rostro-internal lamina of the temporalis muscle was innervated by the anterior deep temporal nerve. The anterior deep temporal nerve and lateral pterygoid nerve had a common trunk. We believe that the rostro-internal lamina was closely related to the lateral pterygoid muscle. The lateral pterygoid muscle displayed one lamina, whereas the medial pterygoid muscle was divided into internal and lateral laminae. The lateral lamina was further divided into rostro-internal and caudo-lateral laminae.

Download full-text PDF

Source
http://dx.doi.org/10.2535/ofaj1936.76.6_303DOI Listing

Publication Analysis

Top Keywords

deep temporal
20
temporal nerve
20
muscle innervated
12
anterior deep
12
lateral pterygoid
12
pterygoid muscle
12
muscle
9
masticatory muscles
8
kangaroo macropus
8
masseteric muscle
8

Similar Publications

Unveiling the Content of Frontal Feedback in Challenging Object Recognition.

Neuroimage

January 2025

School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran. Electronic address:

Object recognition under challenging real-world conditions, including partial occlusion, remains an enduring focus of investigation in cognitive visual neuroscience. This study addresses the insufficiently elucidated neural mechanisms and temporal dynamics involved in this complex process, concentrating on the persistent challenge of recognizing objects obscured by occlusion. Through the analysis of human EEG data, we decode feedback characteristics within frontotemporal networks, uncovering intricate neural mechanisms during occlusion coding, with a specific emphasis on processing complex stimuli such as occluded faces.

View Article and Find Full Text PDF

Temporal echocardiography image registration is important for cardiac motion estimation, myocardial strain assessments, and stroke volume quantifications. Deep learning image registration (DLIR) is a promising way to achieve consistent and accurate registration results with low computational time. DLIR seeks the image deformation that enables the moving image to be warped to match the fixed image.

View Article and Find Full Text PDF

Hip prosthesis failure prediction through radiological deep sequence learning.

Int J Med Inform

January 2025

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Golgi 39, 20131 Milan, MI, Italy; Cardio Tech-Lab, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4, 20138 Milan, Italy. Electronic address:

Background: Existing deep learning studies for the automated detection of hip prosthesis failure only consider the last available radiographic image. However, using longitudinal data is thought to improve the prediction, by combining temporal and spatial components. The aim of this study is to develop artificial intelligence models for predicting hip implant failure from multiple subsequent plain radiographs.

View Article and Find Full Text PDF

Air pollution is a critical global environmental issue, further exacerbated by rapid industrialization and urbanization. Accurate prediction of air pollutant concentrations is essential for effective pollution prevention and control measures. The complex nature of pollutant data is influenced by fluctuating meteorological conditions, diverse pollution sources, and propagation processes, underscores the crucial importance of the spatial and temporal feature extraction for accurately predicting air pollutant concentrations.

View Article and Find Full Text PDF

Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!