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Purpose: This study predicts and simulates the function and relative contributions of the intravelar and extravelar portions of the levator veli palatini (LVP) and palatoglossus (PG) muscles in velic constrictions. Method: A finite element-based model of the 3-dimensional upper airway structures (palate, pharynx, tongue, jaw, maxilla) was implemented, with LVP and PG divided into intravelar and extravelar portions. Simulations were run to investigate the contributions of these muscles in velopharyngeal port (VPP) closure and constriction of the oropharyngeal isthmus (OPI). Results: Simulations reveal that the extravelar portion of LVP, though crucial for lifting the palate, is not sufficient to effect VPP closure. Specifically, the characteristic "bulge" appearing in the posterior soft palate during VPP closure (Pigott, 1969; Serrurier & Badin, 2008) is found to result from activation of the intravelar portion of LVP. Likewise, the intravelar portion of posterior PG is crucial in bending the "veil" or "traverse" (Gick, Francis, Klenin, Mizrahi, & Tom, 2013) of the velum anteriorly to produce uvular constrictions of the OPI (Gick et al., 2014). Conclusions: Simulations support the view that intravelar LVP and PG play significant roles in VPP and OPI constrictions.

Purpose: Bracing of the tongue against opposing vocal-tract surfaces such as the teeth or palate has long been discussed in the context of biomechanical, somatosensory, and aeroacoustic aspects of tongue movement. However, previous studies have tended to describe bracing only in terms of contact (rather than mechanical support), and only in limited phonetic contexts, supporting a widespread view of bracing as an occasional state, peculiar to specific sounds or sound combinations. Method: The present study tests the pervasiveness and effortfulness of tongue bracing in continuous English speech passages using electropalatography and 3-D biomechanical simulations. Results: The tongue remains in continuous contact with the upper molars during speech, with only rare exceptions. Use of the term bracing (rather than merely "contact") is supported here by biomechanical simulations showing that lateral bracing is an active posture requiring dedicated muscle activation; further, loss of lateral contact for onset /l/ allophones is found to be consistently accompanied by contact of the tongue blade against the anterior palate. In the rare instances where direct evidence for contact is lacking (only in a minority of low vowel and postvocalic /l/ tokens), additional biomechanical simulations show that lateral contact is maintained against pharyngeal structures dorsal to the teeth. Conclusion: Taken together, these results indicate that tongue bracing is both pervasive and active in running speech and essential in understanding tongue movement control.

Purpose: The authors' general aim is to use biomechanical models of speech articulators to explore how possible variations in anatomical structure contribute to differences in articulatory strategies and phone systems across human populations. Specifically, they investigated 2 issues: (a) the link between lip muscle anatomy and variability in lip gestures and (b) the constraints of coupled lip/jaw biomechanics on jaw posture in labial sounds. Method: The authors used a model coupling the jaw, tongue, and face. First, the influence of the orbicularis oris (OO) anatomical implementation was analyzed by assessing how changes in depth (from epidermis to the skull) and peripheralness (proximity to the lip horn center) affected lip shaping. Second, the capability of the lip/jaw system to generate protrusion and rounding, or "labial closure," was evaluated for different jaw heights. Results: Results showed that a peripheral and moderately deep OO implementation is most appropriate for protrusion and rounding; a superficial implementation facilitates closure; protrusion and rounding require a high jaw position; and closure is achievable for various jaw heights. Conclusions: Models provide objective information regarding possible links between anatomical and speech production variability across humans. Comparisons with experimental data will illustrate how motor control and cultural factors cope with these constraints. (Contains 4 tables and 10 figures.)

Increased mechanical loading of the temporomandibular joint (TMJ) is often connected with the onset and progression of temporomandibular joint disorders (TMD). The potential role of occlusal factors and sleep bruxism in the onset of TMD are a highly debated topic in literature, but ethical considerations limit in vivo examinations of this problem. The study aims to use an innovative in silico modeling approach to thoroughly investigate the connection between morphological parameters, bruxing direction and TMJ stress. A forward-dynamics tracking approach was used to simulate laterotrusive and mediotrusive tooth grinding for 3 tooth positions, 5 lateral inclination angles, 5 sagittal tilt angles and 3 force levels, giving a total of 450 simulations. Muscle activation patterns, TMJ disc von Mises stress as well as correlations between mean muscle activations and TMJ disc stress are reported. Computed muscle activation patterns agree well with previous literature. The results suggest that tooth inclination and grinding position, to a smaller degree, have an effect on TMJ loading. Mediotrusive bruxing computed higher loads compared to laterotrusive simulations. The strongest correlation was found for TMJ stress and mean activation of the superficial masseter. Overall, our results provide in silico evidence that TMJ disc stress is related to tooth morphology. [ABSTRACT FROM AUTHOR]

The article focuses on biomechanical simulations related to lateral tongue bracing, exploring muscle activations that cause asymmetry in bracing and its effects, with an emphasis on the left side's impact. Topics discussed include tongue bracing, muscle activation patterns, and their influence on tongue-palate contact.

Objective: Musculoskeletal models play an important role in surgical planning and clinical assessment of gait and movement. Faster and more accurate simulation of muscle paths in such models can result in better predictions of forces and facilitate real-time clinical applications, such as rehabilitation with real-time feedback. We propose a novel and efficient method for computing wrapping paths across arbitrary surfaces, such as those defined by bone geometry. Methods: A muscle path is modeled as a massless, frictionless elastic strand that uses artificial forces, applied independently of the dynamic simulation, to wrap tightly around intervening obstacles. Contact with arbitrary surfaces is computed quickly using a distance grid, which is interpolated quadratically to provide smoother results. Results: Evaluation of the method demonstrates good accuracy, with mean relative errors of 0.002 or better when compared against simple cases with exact solutions. The method is also fast, with strand update times of around 0.5 msec for a variety of bone shaped obstacles. Conclusion: Our method has been implemented in the open source simulation system ArtiSynth (www.artisynth.org) and helps solve the problem of muscle wrapping around bones and other structures. Significance: Muscle wrapping on arbitrary surfaces opens up new possibilities for patient-specific musculoskeletal models where muscle paths can directly conform to shapes extracted from medical image data. [ABSTRACT FROM AUTHOR]

Quantal biomechanical effects in speech postures of the lips. J Neurophysiol 124: 833-843, 2020. First published July 29, 2020; doi:10.1152/jn.00676.2019.--The unique biomechanical and functional constraints on human speech make it a promising area for research investigating modular control of movement. The present article illustrates how a modular control approach to speech can provide insights relevant to understanding both motor control and observed variation across languages. We specifically explore the robust typological finding that languages produce different degrees of labial constriction using distinct muscle groupings and concomitantly distinct lip postures. Research has suggested that these lip postures exploit biomechanical regions of nonlinearity between neural activation and movement, also known as quantal regions, to allow movement goals to be realized despite variable activation signals. We present two sets of computer simulations showing that these labial postures can be generated under the assumption of modular control and that the corresponding modules are biomechanically robust: first to variation in the activation levels of participating muscles, and second to interference from surrounding muscles. These results provide support for the hypothesis that biomechanical robustness is an important factor in selecting the muscle groupings used for speech movements and provide insight into the neurological control of speech movements and how biomechanical and functional constraints govern the emergence of speech motor modules. We anticipate that future experimental work guided by biomechanical simulation results will provide new insights into the neural organization of speech movements. [ABSTRACT FROM AUTHOR]

The human masticatory system is a complex functional unit characterized by a multitude of skeletal components, muscles, soft tissues, and teeth. Muscle activation dynamics cannot be directly measured on live human subjects due to ethical, safety, and accessibility limitations. Therefore, estimation of muscle activations and their resultant forces is a longstanding and active area of research. Reinforcement learning (RL) is an adaptive learning strategy which is inspired by the behavioral psychology and enables an agent to learn the dynamics of an unknown system via policy-driven explorations. The RL framework is a well-formulated closed-loop system where high capacity neural networks are trained with the feedback mechanism of rewards to learn relatively complex actuation patterns. In this work, we are building on a deep RL algorithm, known as the Soft Actor-Critic, to learn the inverse dynamics of a simulated masticatory system, i.e., learn the activation patterns that drive the jaw to its desired location. The outcome of the proposed training procedure is a parametric neural model which acts as the brain of the biomechanical system. We demonstrate the model's ability to navigate the feasible three-dimensional (3D) envelope of motion with sub-millimeter accuracies. We also introduce a performance analysis platform consisting of a set of quantitative metrics to assess the functionalities of a given simulated masticatory system. This platform assesses the range of motion, metabolic efficiency, the agility of motion, the symmetry of activations, and the accuracy of reaching the desired target positions. We demonstrate how the model learns more metabolically efficient policies by integrating a force regularization term in the RL reward. We also demonstrate the inverse correlation between the metabolic efficiency of the models and their agility and range of motion. The presented masticatory model and the proposed RL training mechanism are valuable tools for the analysis of mastication and other biomechanical systems. We see this framework's potential in facilitating the functional analyses aspects of surgical treatment planning and predicting the rehabilitation performance in post-operative subjects. [ABSTRACT FROM AUTHOR]

This tribute article to Professor Alan G. Hannam is based on 7 presentations for him at the July 1, 2008 symposium honoring 3 "giants" in orofacial neuroscience: Professors B.J. Sessle, J.P. Lund, and A.G. Hannam. This tribute to Hannam's outstanding career draws examples from his 40-year academic career and spans topics from human evolution to complex modeling of the craniomandibular system. The first presentation by W. Hylander provides a plausible answer to the functional and evolutionary significance of canine reduction in hominins. The second presentation, by A. McMillan, describes research activities in the field of healthy aging, including findings that intensity-modulated radiotherapy improves the health condition and quality of life of people with nasopharyngeal carcinoma in comparison to conventional radiotherapy. The developments in dental imaging are summarized in the third paper by E. Lam, and an overview of the bite force magnitude and direction while clenching is described in the fourth paper by M. Watanabe. The last 3 contributions by G. Langenbach, I. Staveness, and C. Peck deal with the topic of bone remodeling as well as masticatory system modeling, which was Hannam's main research interest in recent years. These contributions show the considerable advancements that have been made in the last decade under Hannam's drive, in particular the development of an interactive model comprising, in addition to the masticatory system, also the upper airways. The final section of the article includes a final commentary from Professor Hannam. [ABSTRACT FROM AUTHOR]

The masticatory region is an important human motion system that is essential for basic human tasks like mastication, speech or swallowing. An association between temporomandibular disorders (TMDs) and high temporomandibular joint (TMJ) stress has been suggested, but in vivo joint force measurements are not feasible to directly test this assumption. Consequently, biomechanical computer simulation remains as one of a few means to investigate this complex system. To thoroughly examine orofacial biomechanics, we developed a novel, dynamic computer model of the masticatory system. The model combines a muscle driven rigid body model of the jaw region with a detailed finite element model (FEM) disk and elastic foundation (EF) articular cartilage. The model is validated using high-resolution MRI data for protrusion and opening that were collected from the same volunteer. Joint stresses for a clenching task as well as protrusive and opening movements are computed. Simulations resulted in mandibular positions as well as disk positions and shapes that agree well with the MRI data. The model computes reasonable disk stress patterns for dynamic tasks. Moreover, to the best of our knowledge this model presents the first ever contact model using a combination of EF layers and a FEM body, which results in a clear decrease in computation time. In conclusion, the presented model is a valuable tool for the investigation of the human TMJ and can potentially help in the future to increase the understanding of the masticatory system and the relationship between TMD and joint stress and to highlight potential therapeutic approaches for the restoration of orofacial function. [ABSTRACT FROM AUTHOR]

Temporomandibular joint disorders (TMD) are common dysfunctions of the masticatory region and are often linked to dislocation or changes of the temporomandibular joint (TMJ) disc. Magnetic resonance imaging (MRI) is the gold standard for TMJ imaging but standard clinical sequences do not deliver a sufficient resolution and contrast for the creation of detailed meshes of the TMJ disc. Additionally, bony structures cannot be captured appropriately using standard MRI sequences due to their low signal intensity. The objective of this study was to enable researchers to create high resolution representations of all structures of the TMJ and consequently investigate morphological as well as positional changes of the masticatory system. To create meshes of the bony structures, a single computed tomography (CT) scan was acquired. In addition, a high‐resolution MRI sequence was produced, which is used to collect the thickness and position change of the disc for various static postures using bite blocks. Changes in thickness of the TMJ disc as well as disc translation were measured. The newly developed workflow successfully allows researchers to create high resolution models of all structures of the TMJ for various static positions, enabling the investigation of TMJ disc translation and deformation. Discs were thinnest in the lateral part and moved mainly anteriorly and slightly medially. The procedure offers the most comprehensive picture of disc positioning and thickness changes reported to date. The presented data can be used for the development of a biomechanical computer model of TMJ anatomy and to investigate dynamic and static loads on the components of the system, which could be useful for the prediction of TMD onset. [ABSTRACT FROM AUTHOR]

Fish Tank Virtual Reality (FTVR) displays create a compelling 3D spatial effect by rendering to the perspective of the viewer with head-tracking. Combining FTVR with a spherical display enhances the 3D experience with unique properties of the spherical screen such as the enclosing shape, consistent curved surface, and borderless views from all angles around the display. The ability to generate a strong 3D effect on a spherical display with head-tracked rendering is promising for increasing user's performance in 3D tasks. An unanswered question is whether these natural affordances of spherical FTVR displays can improve spatial perception in comparison to traditional flat FTVR displays. To investigate this question, we conducted an experiment to see whether users can perceive the depth and size of virtual objects better on a spherical FTVR display compared to a flat FTVR display on two tasks. Using the spherical display, we found significantly that users had 1cm depth accuracy compared to 6.5cm accuracy using the flat display on a depth-ranking task. Likewise, their performance on a size-matching task was also significantly better with the size error of 2.3mm on the spherical display compared to 3.1mm on the flat display. Furthermore, the perception of size-constancy is stronger on the spherical display than the flat display. This study indicates that the natural affordances provided by the spherical form factor improve depth and size perception in 3D compared to a flat display. We believe that spherical FTVR displays have potential as a 3D virtual environment to provide better task performance for various 3D applications such as 3D designs, scientific visualizations, and virtual surgery. [ABSTRACT FROM AUTHOR]

Objective: Musculoskeletal simulation can be used to estimate muscle forces in clinical movement studies. However, such simulations typically only target movement measurements and are not applicable to force exertion tasks which are commonly used in rehabilitation therapy. Simulations can also produce nonphysiological joint forces or be too slow for real-time clinical applications, such as rehabilitation with real-time feedback. The objective of this study is to propose and evaluate a new formulation of forward-dynamics assisted tracking simulation that incorporates measured reaction forces as targets or constraints without any additional computational cost. [ABSTRACT FROM AUTHOR]

Posture perturbation experiments can be used to assess the biomechanical factors underlying postural stability. However, experiments are limited by participant safety, inability to measure important factors such as muscle forces and the large number of conditions to be tested. We employed forward dynamics computer simulations to mimic posture perturbation experiments to assess the effect of short-range muscle stiffness and muscle co-contraction on the postural stability of a musculoskeletal model. Two novel simulation techniques were developed: a static-optimisation formulation that uses target joint stiffness to elicit muscle co-contraction and a realisation of a short-range stiffness muscle model as a passive, prestressed spring that can be used in forward dynamics simulations. Our simulation results demonstrated that the realistic short-range stiffness has a large impact on postural stability as compared to traditional Hill-type muscle models that underestimate intrinsic stiffness. We also found that muscle co-contraction contributed to postural stability, but to a lesser extent than the realistic intrinsic stiffness alone. Although the present study does not consider active responses, our simulations have revealed important factors that contribute to the body's intrinsic stability and that may be helpful, in part, for compensating for diminished or delayed postural responses to maintain balance. [ABSTRACT FROM AUTHOR]

A musculoskeletal model of the spine was created using ArtiSynth, an open-source biomechanical modelling toolkit. The model included the entire spine and rib cage, with the lumbar vertebrae being mobile and 210 muscle fascicles. Muscle parameters needed for a full Hill-type musculotendon model including tendon ratios and pennation angles along with muscle force-length and force-velocity curves were incorporated into the model, as were the nonlinear stiffness of the functional spinal units and the effect of intra-abdominal pressure. We used forward dynamics-assisted data tracking for the estimation of muscle forces and validated the solution method by comparing the predicted spinal forces vs. the results of two in vivo experiments in the literature. Our model produced larger maximum extension moment in flexion than extension, which is observed in in vivo experiments. These results could not be achieved without the inclusion of the muscle force-length relationship. The model was also able to predict the ratios of axial forces at L4-L5 as measured in vivo intradiscal pressures for three cases of upright standing, holding a crate close to and far from the chest. Due to the high stiffness of the spine, our solution method was sensitive to input kinematics, which hindered extensive validation of the model for body positions other than standing. Modifying the solution method, possibly by only tracking the angular motion of the vertebrae rather than their translational motion, should make the model less sensitive and enable further validation. [ABSTRACT FROM AUTHOR]

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task. [ABSTRACT FROM AUTHOR]

Purpose: Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model. Methods: Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles $$(\hbox {act}_\mathrm{all})$$ , selecting the three muscles showing highest muscle activity bilaterally $$(\hbox {act}_3)$$ -this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance-and activating the muscles considered most relevant per instruction $$(\hbox {act}_\mathrm{rel})$$ , bilaterally. The model's lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients $$(\rho )$$ . Results: The correlation coefficient between simulations and measurements with $$\hbox {act}_\mathrm{rel}$$ resulted in a median $$\rho $$ of 0.77. $$\hbox {act}_3$$ had a median $$\rho $$ of 0.78, whereas with $$\hbox {act}_\mathrm{all}$$ the median $$\rho $$ decreased to 0.45. Conclusion: We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median $$\rho $$ of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient's own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may enable surgeons to test the functional results of wedge excisions for lip cancer in a virtual environment and to weigh surgery versus organ-sparing radiotherapy or photodynamic therapy. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-023-34854-w, published online 22 May 2023 The original version of this Article contained an error. In the Discussion section, "In the present study, astronauts spent between 10 and 4 days aboard the ISS, much lower than the 90 day threshold necessary to achieve deep adaptation." now reads: "In the present study, astronauts spent between 10 to 14 days aboard the ISS, much lower than the 90 day threshold necessary to achieve deep adaptation.". [Extracted from the article]

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications.We report performance results on three plant phenotyping benchmarks fromthe literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana. [ABSTRACT FROM AUTHOR]

Muscle paths play an important role in musculoskeletal simulations by determining a muscle’s length and how its force is distributed to joints. Most previous approaches estimate the way in which muscles ‘wrap’ around bones and other structures with smooth analytical wrapping surfaces. In this paper, we employ Newton’s method with discrete differential geometry to permit muscle wrapping over arbitrary polygonal mesh surfaces that represent underlying bones and structures. Precomputing distance fields allows us to speed up computations for the common situation where many paths cross the same wrapping surfaces. We found positive results for the accuracy, robustness, and efficiency of the method. However the method did not exhibit continuous changes in path length for dynamic simulations. Nonetheless this approach provides a valuable step toward fast muscle wrapping on arbitrary meshes. [ABSTRACT FROM AUTHOR]