The effect of bolus properties on muscle activation patterns and TMJ loading during unilateral chewing.

Mastication is a vital human function and uses an intricate coordination of muscle activation to break down food. Collection of detailed muscle activation patterns is complex and commonly only masseter and anterior temporalis muscle activation are recorded. Chewing is the orofacial task with the highest muscle forces, potentially leading to high temporomandibular joint (TMJ) loading. Increased TMJ loading is often associated with the onset and progression of temporomandibular disorders (TMD). Hence, studying TMJ mechanical stress during mastication is a central task. Current TMD self-management guidelines suggest eating small and soft pieces of food, but patient safety concerns inhibit in vivo investigations of TMJ biomechanics and currently no in silico model of muscle recruitment and TMJ biomechanics during chewing exists. For this purpose, we have developed a state-of-the-art in silico model, combining rigid body bones, finite element TMJ discs and line actuator muscles. To solve the problems regarding muscle activation measurement, we used a forward dynamics tracking approach, optimizing muscle activations driven by mandibular motion. We include a total of 256 different combinations of food bolus size, stiffness and position in our study and report kinematics, muscle activation patterns and TMJ disc von Mises stress. Computed mandibular kinematics agree well with previous measurements. The computed muscle activation pattern stayed stable over all simulations, with changes to the magnitude relative to stiffness and size of the bolus. Our biomedical simulation results agree with the clinical guidelines regarding bolus modifications as smaller and softer food boluses lead to less TMJ loading. The computed mechanical stress results help to strengthen the confidence in TMD self-management recommendations of eating soft and small pieces of food to reduce TMJ pain.

Integument colour change: Tracking delayed growth of Oppia nitens as a sub-lethal indicator of soil toxicity.

Growth is an important toxicity end-point in ecotoxicology but is rarely used in soil ecotoxicological studies. Here, we assessed the growth change of Oppia nitens when exposed to reference and heavy metal toxicants. To assess mite growth, we developed an image analysis methodology to measure colour spectrum changes of the mite integument at the final developmental stage, as a proxy for growth change. We linked the values of red, green, blue, key-black, and light colour of mites to different growth stages. Based on this concept, we assessed the growth change of mites exposed to cadmium, copper, zinc, lead, boric acid, or phenanthrene at sublethal concentrations in LUFA 2.2 soil for 14 days. Sublethal effects were detected after 7 days of exposure. The growth of O. nitens was more sensitive than survival and reproduction when exposed to copper (EC50growth = 1360 mg/kg compared to EC50reproduction = 2896 mg/kg). Mite growth sensitivity was within the same order of magnitude to mite reproduction when exposed to zinc (EC50growth = 1785; EC50reproduction = 1562 mg/kg). At least 25% of sublethal effects of boric acid and phenanthrene were detected in the mites but growth was not impacted when O. nitens were exposed to lead. Consistent with previous studies, cadmium was the most toxic metal to O. nitens. The mite growth pattern was comparable to mite survival and reproduction from previous studies. Mite growth is a sensitive toxicity endpoint, ecologically relevant, fast, easy to detect, and can be assessed in a non-invasive fashion, thereby complimenting existing O. nitens testing protocols.

Explainable deep learning in plant phenotyping.

The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.

Postural adaptation to microgravity underlies fine motor impairment in astronauts' speech.

Understanding the role of anti-gravity behaviour in fine motor control is crucial to achieving a unified theory of motor control. We compare speech from astronauts before and immediately after microgravity exposure to evaluate the role of anti-gravity posture during fine motor skills. Here we show a generalized lowering of vowel space after space travel, which suggests a generalized postural shift of the articulators. Biomechanical modelling of gravitational effects on the vocal tract supports this analysis-the jaw and tongue are pulled down in 1g, but movement trajectories of the tongue are otherwise unaffected. These results demonstrate the role of anti-gravity posture in fine motor behaviour and provide a basis for the unification of motor control models across domains.

Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping.

The rise of self-supervised learning (SSL) methods in recent years presents an opportunity to leverage unlabeled and domain-specific datasets generated by image-based plant phenotyping platforms to accelerate plant breeding programs. Despite the surge of research on SSL, there has been a scarcity of research exploring the applications of SSL to image-based plant phenotyping tasks, particularly detection and counting tasks. We address this gap by benchmarking the performance of 2 SSL methods-momentum contrast (MoCo) v2 and dense contrastive learning (DenseCL)-against the conventional supervised learning method when transferring learned representations to 4 downstream (target) image-based plant phenotyping tasks: wheat head detection, plant instance detection, wheat spikelet counting, and leaf counting. We studied the effects of the domain of the pretraining (source) dataset on the downstream performance and the influence of redundancy in the pretraining dataset on the quality of learned representations. We also analyzed the similarity of the internal representations learned via the different pretraining methods. We find that supervised pretraining generally outperforms self-supervised pretraining and show that MoCo v2 and DenseCL learn different high-level representations compared to the supervised method. We also find that using a diverse source dataset in the same domain as or a similar domain to the target dataset maximizes performance in the downstream task. Finally, our results show that SSL methods may be more sensitive to redundancy in the pretraining dataset than the supervised pretraining method. We hope that this benchmark/evaluation study will guide practitioners in developing better SSL methods for image-based plant phenotyping.

Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns.

Deep learning has shown potential in domains with large-scale annotated datasets. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images. In this work, we propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation. As a use case, we focus on wheat head segmentation. We synthesize a computationally annotated dataset-using a few annotated images, a short unannotated video clip of a wheat field, and several video clips with no wheat-to train a customized U-Net model. Considering the distribution shift between the synthesized and real images, we apply three domain adaptation steps to gradually bridge the domain gap. Only using two annotated images, we achieved a Dice score of 0.89 on the internal test set. When further evaluated on a diverse external dataset collected from 18 different domains across five countries, this model achieved a Dice score of 0.73. To expose the model to images from different growth stages and environmental conditions, we incorporated two annotated images from each of the 18 domains to further fine-tune the model. This increased the Dice score to 0.91. The result highlights the utility of the proposed approach in the absence of large-annotated datasets. Although our use case is wheat head segmentation, the proposed approach can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.

Large Growth Deformations of Thin Tissue Using Solid-Shells.

Simulating large scale expansion of thin structures, such as in growing leaves, is challenging. Solid-shells have a number of potential advantages over conventional thin-shell methods, but have thus far only been investigated for small plastic deformation cases. In response, we present a new general-purpose FEM growth framework for handling a wide range of challenging growth scenarios using the solid-shell element. Solid-shells are a middle-ground between traditional volume and thin-shell elements where volumetric characteristics are retained while being treatable as a 2D manifold much like thin-shells. These elements are adaptable to accommodate the many techniques that are required for simulating large and intricate plastic deformations, including morphogen diffusion, plastic embedding, strain-aware adaptive remeshing, and collision handling. We demonstrate the capabilities of growing solid-shells in reproducing buckling, rippling, curling, and collision deformations, relevant towards animating growing leaves, flowers, and other thin structures. Solid-shells are compared side-by-side with thin-shells to examine their bending behavior and runtime performance. The experiments demonstrate that solid-shells are a viable alternative to thin-shells for simulating large and intricate growth deformations.

Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting.

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.

The effect of tooth cusp morphology and grinding direction on TMJ loading during bruxism.

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.

Quantitative evaluation of nonlinear methods for population structure visualization and inference.

Population structure (also called genetic structure and population stratification) is the presence of a systematic difference in allele frequencies between subpopulations in a population as a result of nonrandom mating between individuals. It can be informative of genetic ancestry, and in the context of medical genetics, it is an important confounding variable in genome-wide association studies. Recently, many nonlinear dimensionality reduction techniques have been proposed for the population structure visualization task. However, an objective comparison of these techniques has so far been missing from the literature. In this article, we discuss the previously proposed nonlinear techniques and some of their potential weaknesses. We then propose a novel quantitative evaluation methodology for comparing these nonlinear techniques, based on populations for which pedigree is known a priori either through artificial selection or simulation. Based on this evaluation metric, we find graph-based algorithms such as t-SNE and UMAP to be superior to principal component analysis, while neural network-based methods fall behind.

Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification.

The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop complex CNN models to achieve better performances, introducing over-parameterization and forcing the model to overfit on a training dataset. The most popular process for evaluating overfitting in a deep learning model is using accuracy and loss curves. Train and loss curves may help understand the performance of a model but do not provide guidance on how the model could be modified to attain better performance. In this article, we analyzed the relation between the features learned by a model and its capacity and showed that a model with higher representational capacity might learn many subtle features that may negatively affect its performance. Next, we showed that the shallow layers of a deep learning model learn more diverse features than the ones learned by the deeper layers. Finally, we propose SSIM cut curve, a new way to select the depth of a CNN model by using the pairwise similarity matrix between the visualization of the features learned at different depths by using Guided Backpropagation. We showed that our proposed method could potentially pave a new way to select a better CNN model.

Effect of facet inclination and location on TMJ loading during bruxism: An in-silico study.

Introduction: Functional impairment of the masticatory region can have significant consequences that range from a loss of quality of life to severe health issues. Increased temporomandibular joint loading is often connected with temporomandibular disorders, but the effect of morphological factors on joint loading is a heavily discussed topic. Due to the small size and complex structure of the masticatory region in vivo investigations of these connections are difficult to perform. Objectives: We propose a novel in silico approach for the investigation of the effect of wear facet inclination and position on TMJ stress. Methods: We use a forward-dynamics tracking approach to simulate lateral bruxing on the canine and first molar using 6 different inclinations, resulting in a total of 12 simulated cases. By using a computational model, we control a single variable without interfering with the system. Muscle activation pattern, maximum bruxing force as well as TMJ disc stress are reported for all simulations. Results: Muscle activation patterns and bruxing forces agree well with previously reported EMG findings and in vivo force measurements. The simulation results show that an increase in inclination leads to a decrease in TMJ loading. Wear facet position seems to play a smaller role with regard to bruxing force but might be more relevant for TMJ loading. Conclusion: Together these results suggest a possible effect of tooth morphology on TMJ loading during bruxism.

Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method.

To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot's alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets.

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

Deep neural networks for genomic prediction do not estimate marker effects.

Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models, such as deep neural networks, to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.

An in silico investigation of the effect of bolus properties on TMJ loading during mastication.

Mastication is the motor task with the highest muscle activations of the jaw region, potentially leading to high temporomandibular joint (TMJ) loading. Since increased loading of the TMJ is associated with temporomandibular disorders (TMD), TMJ mechanics during chewing has potential clinical relevance in TMD treatment. TMD self-management guidelines suggest eating soft and small pieces of food to reduce TMJ pain. Since TMJ loading cannot be measured in vivo, due to patient safety restrictions, computer modeling is an important tool for investigations of the potential connection between TMJ loading and TMD. The objective of this study is to investigate the effect of food bolus variables on mechanical TMJ loading to help inform better self-management guidelines for TMD. A combined rigid-body-finite-element model of the jaw region was used to investigate the effect of bolus size, stiffness, and position. Mandibular motion and TMJ disc von Mises stress were reported. Computed mandibular motion generally agrees well with previous literature. Disc stress was higher during the closing phase of the chewing cycle and for the non-working side disc. Smaller and softer food boluses overall lead to less TMJ loading. The results reinforce current guidelines regarding bolus modifications and provide new potential guidelines for bolus positioning that could be verified through a future clinical trial. The paper presents a first in silico investigation of dynamic chewing with detailed TMJ stress for different bolus properties. The results help to strengthen the confidence in TMD self-management recommendations, potentially reducing pain levels of patients.

Bayesian analysis of home advantage in North American professional sports before and during COVID-19.

Home advantage in professional sports is a widely accepted phenomenon despite the lack of any controlled experiments at the professional level. The return to play of professional sports during the COVID-19 pandemic presents a unique opportunity to analyze the hypothesized effect of home advantage in neutral settings. While recent work has examined the effect of COVID-19 restrictions on home advantage in European football, comparatively few studies have examined the effect of restrictions in the North American professional sports leagues. In this work, we infer the effect of and changes in home advantage prior to and during COVID-19 in the professional North American leagues for hockey, basketball, baseball, and American football. We propose a Bayesian multi-level regression model that infers the effect of home advantage while accounting for relative team strengths. We also demonstrate that the Negative Binomial distribution is the most appropriate likelihood to use in modelling North American sports leagues as they are prone to overdispersion in their points scored. Our model gives strong evidence that home advantage was negatively impacted in the NHL and NBA during their strongly restricted COVID-19 playoffs, while the MLB and NFL showed little to no change during their weakly restricted COVID-19 seasons.

Muscle Path Wrapping on Arbitrary Surfaces.

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.